|
Please note that this page does not hosts or makes available any of the listed filenames. You
cannot download any of those files from here.
|
| 1.1 2.13. Practical example. Descriptive statistics_lesson.xlsx.xlsx |
146.51KB |
| 1.1 3.17. Practical example. Confidence intervals_lesson.xlsx.xlsx |
1.74MB |
| 1.1 4.10.Hypothesis-testing-section-practical-example.xlsx.xlsx |
51.71KB |
| 1.1 5 Files Needed to Deploy the Model.html |
134B |
| 1.1 Absenteeism_data.csv.csv |
32.05KB |
| 1.1 Absenteeism_preprocessed.csv.csv |
29.13KB |
| 1.1 Arithmetic Operators - Resources.html |
134B |
| 1.1 Audiobooks_data.csv.csv |
710.77KB |
| 1.1 Comparison Operators - Resources.html |
134B |
| 1.1 Course notes_descriptive_statistics.pdf.pdf |
482.21KB |
| 1.1 Course notes_descriptive_statistics.pdf.pdf |
482.21KB |
| 1.1 Course notes_hypothesis_testing.pdf.pdf |
648.20KB |
| 1.1 Course notes_inferential statistics.pdf.pdf |
382.32KB |
| 1.1 Course Notes - Basic Probability.pdf.pdf |
371.05KB |
| 1.1 Course Notes - Bayesian Inference.pdf.pdf |
386.01KB |
| 1.1 Course Notes - Combinatorics.pdf.pdf |
226.12KB |
| 1.1 Course Notes - Probability Distributions.pdf.pdf |
456.24KB |
| 1.1 Course Notes - Section 2.pdf.pdf |
578.08KB |
| 1.1 Course Notes - Section 6.pdf.pdf |
936.42KB |
| 1.1 Defining a Function in Python - Resources.html |
134B |
| 1.1 For Loops - Resources.html |
134B |
| 1.1 Introduction to the If Statement - Resources.html |
134B |
| 1.1 Lists - Resources.html |
134B |
| 1.1 Python Introduction - Course Notes.pdf.pdf |
2.04MB |
| 1.1 Shortcuts-for-Jupyter.pdf.pdf |
619.17KB |
| 1.1 Shortcuts-for-Jupyter.pdf.pdf |
619.17KB |
| 1.1 sklearn - Linear Regression - Practical Example (Part 1).html |
134B |
| 1.2 Bais NN Example Part 1.html |
136B |
| 1.2 df_preprocessed.csv.csv |
29.11KB |
| 1.2 Statistics Glossary.xlsx.xlsx |
20.26KB |
| 1.2 Variables - Resources.html |
134B |
| 1.3 data_preprocessing_homework.pdf.pdf |
134.47KB |
| 1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 |
79.56MB |
| 1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.srt |
8.99KB |
| 1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.vtt |
7.90KB |
| 1. A Practical Example What You Will Learn in This Course.mp4 |
43.90MB |
| 1. A Practical Example What You Will Learn in This Course.srt |
6.37KB |
| 1. A Practical Example What You Will Learn in This Course.vtt |
5.62KB |
| 1. Are You Sure You're All Set.html |
519B |
| 1. Basic NN Example (Part 1).mp4 |
20.59MB |
| 1. Basic NN Example (Part 1).srt |
4.47KB |
| 1. Basic NN Example (Part 1).vtt |
3.91KB |
| 1. Business Case Getting acquainted with the dataset.mp4 |
87.65MB |
| 1. Business Case Getting acquainted with the dataset.srt |
10.79KB |
| 1. Business Case Getting acquainted with the dataset.vtt |
9.37KB |
| 1. Comparison Operators.mp4 |
10.18MB |
| 1. Comparison Operators.srt |
2.47KB |
| 1. Comparison Operators.vtt |
2.14KB |
| 1. Data Science and Business Buzzwords Why are there so many.mp4 |
37.68MB |
| 1. Data Science and Business Buzzwords Why are there so many.srt |
6.63KB |
| 1. Data Science and Business Buzzwords Why are there so many.vtt |
5.84KB |
| 1. Debunking Common Misconceptions.mp4 |
45.37MB |
| 1. Debunking Common Misconceptions.srt |
5.30KB |
| 1. Debunking Common Misconceptions.vtt |
4.69KB |
| 1. Defining a Function in Python.mp4 |
7.74MB |
| 1. Defining a Function in Python.srt |
2.53KB |
| 1. Defining a Function in Python.vtt |
2.20KB |
| 1. EXERCISE - Age vs Probability.html |
385B |
| 1. Exploring the Problem with a Machine Learning Mindset.mp4 |
27.54MB |
| 1. Exploring the Problem with a Machine Learning Mindset.srt |
4.59KB |
| 1. Exploring the Problem with a Machine Learning Mindset.vtt |
4.02KB |
| 1. Finding the Job - What to Expect and What to Look for.mp4 |
35.79MB |
| 1. Finding the Job - What to Expect and What to Look for.srt |
4.50KB |
| 1. Finding the Job - What to Expect and What to Look for.vtt |
3.94KB |
| 1. For Loops.mp4 |
11.80MB |
| 1. For Loops.srt |
2.80KB |
| 1. For Loops.vtt |
2.44KB |
| 1. Fundamentals of Combinatorics.mp4 |
7.55MB |
| 1. Fundamentals of Combinatorics.srt |
1.31KB |
| 1. Fundamentals of Combinatorics.vtt |
1.18KB |
| 1. Fundamentals of Probability Distributions.mp4 |
42.36MB |
| 1. Fundamentals of Probability Distributions.srt |
7.55KB |
| 1. Fundamentals of Probability Distributions.vtt |
6.72KB |
| 1. Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 |
52.30MB |
| 1. Game Plan for this Python, SQL, and Tableau Business Exercise.srt |
5.46KB |
| 1. Game Plan for this Python, SQL, and Tableau Business Exercise.vtt |
4.80KB |
| 1. How to Install TensorFlow.mp4 |
14.56MB |
| 1. How to Install TensorFlow.srt |
3.22KB |
| 1. How to Install TensorFlow.vtt |
2.84KB |
| 1. Introduction.mp4 |
15.50MB |
| 1. Introduction.srt |
1.63KB |
| 1. Introduction.vtt |
1.44KB |
| 1. Introduction to Cluster Analysis.mp4 |
53.42MB |
| 1. Introduction to Cluster Analysis.srt |
4.80KB |
| 1. Introduction to Cluster Analysis.vtt |
4.21KB |
| 1. Introduction to Logistic Regression.mp4 |
27.06MB |
| 1. Introduction to Logistic Regression.srt |
1.62KB |
| 1. Introduction to Logistic Regression.vtt |
1.44KB |
| 1. Introduction to Neural Networks.mp4 |
42.92MB |
| 1. Introduction to Neural Networks.srt |
5.90KB |
| 1. Introduction to Neural Networks.vtt |
5.18KB |
| 1. Introduction to Programming.mp4 |
58.54MB |
| 1. Introduction to Programming.srt |
6.91KB |
| 1. Introduction to Programming.vtt |
6.08KB |
| 1. Introduction to Regression Analysis.mp4 |
17.32MB |
| 1. Introduction to Regression Analysis.srt |
2.21KB |
| 1. Introduction to Regression Analysis.vtt |
1.95KB |
| 1. K-Means Clustering.mp4 |
27.29MB |
| 1. K-Means Clustering.srt |
6.67KB |
| 1. K-Means Clustering.vtt |
5.76KB |
| 1. Lists.mp4 |
22.00MB |
| 1. Lists.srt |
4.99KB |
| 1. Lists.vtt |
4.30KB |
| 1. MNIST What is the MNIST Dataset.mp4 |
17.82MB |
| 1. MNIST What is the MNIST Dataset.srt |
3.50KB |
| 1. MNIST What is the MNIST Dataset.vtt |
3.07KB |
| 1. Multiple Linear Regression.mp4 |
21.53MB |
| 1. Multiple Linear Regression.srt |
3.35KB |
| 1. Multiple Linear Regression.vtt |
2.93KB |
| 1. Necessary Programming Languages and Software Used in Data Science.mp4 |
63.11MB |
| 1. Necessary Programming Languages and Software Used in Data Science.srt |
7.30KB |
| 1. Necessary Programming Languages and Software Used in Data Science.vtt |
6.42KB |
| 1. Null vs Alternative Hypothesis.mp4 |
92.05MB |
| 1. Null vs Alternative Hypothesis.srt |
6.97KB |
| 1. Null vs Alternative Hypothesis.vtt |
6.18KB |
| 1. Object Oriented Programming.mp4 |
33.59MB |
| 1. Object Oriented Programming.srt |
6.10KB |
| 1. Object Oriented Programming.vtt |
5.34KB |
| 1. Population and Sample.mp4 |
58.11MB |
| 1. Population and Sample.srt |
5.47KB |
| 1. Population and Sample.vtt |
4.81KB |
| 1. Practical Example Descriptive Statistics.mp4 |
160.46MB |
| 1. Practical Example Descriptive Statistics.srt |
20.79KB |
| 1. Practical Example Descriptive Statistics.vtt |
18.00KB |
| 1. Practical Example Hypothesis Testing.mp4 |
69.48MB |
| 1. Practical Example Hypothesis Testing.srt |
8.49KB |
| 1. Practical Example Hypothesis Testing.vtt |
7.43KB |
| 1. Practical Example Inferential Statistics.mp4 |
102.67MB |
| 1. Practical Example Inferential Statistics.srt |
13.65KB |
| 1. Practical Example Inferential Statistics.vtt |
11.90KB |
| 1. Practical Example Linear Regression (Part 1).mp4 |
97.09MB |
| 1. Practical Example Linear Regression (Part 1).srt |
14.86KB |
| 1. Practical Example Linear Regression (Part 1).vtt |
12.98KB |
| 1. Preprocessing Introduction.mp4 |
27.78MB |
| 1. Preprocessing Introduction.srt |
3.87KB |
| 1. Preprocessing Introduction.vtt |
3.39KB |
| 1. Probability in Finance.mp4 |
99.07MB |
| 1. Probability in Finance.srt |
9.84KB |
| 1. Probability in Finance.vtt |
8.71KB |
| 1. Sets and Events.mp4 |
25.02MB |
| 1. Sets and Events.srt |
5.06KB |
| 1. Sets and Events.vtt |
4.48KB |
| 1. Stochastic Gradient Descent.mp4 |
28.69MB |
| 1. Stochastic Gradient Descent.srt |
4.82KB |
| 1. Stochastic Gradient Descent.vtt |
4.18KB |
| 1. Summary on What You've Learned.mp4 |
39.76MB |
| 1. Summary on What You've Learned.srt |
5.22KB |
| 1. Summary on What You've Learned.vtt |
4.61KB |
| 1. Techniques for Working with Traditional Data.mp4 |
79.85MB |
| 1. Techniques for Working with Traditional Data.srt |
10.63KB |
| 1. Techniques for Working with Traditional Data.vtt |
9.30KB |
| 1. The Basic Probability Formula.mp4 |
44.02MB |
| 1. The Basic Probability Formula.srt |
8.91KB |
| 1. The Basic Probability Formula.vtt |
7.83KB |
| 1. The IF Statement.mp4 |
13.63MB |
| 1. The IF Statement.srt |
3.60KB |
| 1. The IF Statement.vtt |
3.12KB |
| 1. The Linear Regression Model.mp4 |
57.37MB |
| 1. The Linear Regression Model.srt |
7.06KB |
| 1. The Linear Regression Model.vtt |
6.14KB |
| 1. The Reason behind these Disciplines.mp4 |
71.19MB |
| 1. The Reason behind these Disciplines.srt |
6.50KB |
| 1. The Reason behind these Disciplines.vtt |
5.69KB |
| 1. Types of Clustering.mp4 |
44.58MB |
| 1. Types of Clustering.srt |
4.66KB |
| 1. Types of Clustering.vtt |
4.12KB |
| 1. Types of Data.mp4 |
72.53MB |
| 1. Types of Data.srt |
5.96KB |
| 1. Types of Data.vtt |
5.25KB |
| 1. Using Arithmetic Operators in Python.mp4 |
18.92MB |
| 1. Using Arithmetic Operators in Python.srt |
4.12KB |
| 1. Using Arithmetic Operators in Python.vtt |
3.58KB |
| 1. Variables.mp4 |
25.30MB |
| 1. Variables.srt |
6.06KB |
| 1. Variables.vtt |
5.27KB |
| 1. What are Confidence Intervals.mp4 |
49.98MB |
| 1. What are Confidence Intervals.srt |
3.26KB |
| 1. What are Confidence Intervals.vtt |
2.86KB |
| 1. What are Data, Servers, Clients, Requests, and Responses.mp4 |
69.04MB |
| 1. What are Data, Servers, Clients, Requests, and Responses.srt |
5.94KB |
| 1. What are Data, Servers, Clients, Requests, and Responses.vtt |
5.20KB |
| 1. What is a Layer.mp4 |
12.50MB |
| 1. What is a Layer.srt |
2.39KB |
| 1. What is a Layer.vtt |
2.13KB |
| 1. What is a matrix.mp4 |
33.59MB |
| 1. What is a matrix.srt |
4.35KB |
| 1. What is a matrix.vtt |
3.80KB |
| 1. What is Initialization.mp4 |
21.76MB |
| 1. What is Initialization.srt |
3.51KB |
| 1. What is Initialization.vtt |
3.09KB |
| 1. What is Overfitting.mp4 |
31.09MB |
| 1. What is Overfitting.srt |
5.58KB |
| 1. What is Overfitting.vtt |
4.93KB |
| 1. What is sklearn and How is it Different from Other Packages.mp4 |
27.25MB |
| 1. What is sklearn and How is it Different from Other Packages.srt |
3.43KB |
| 1. What is sklearn and How is it Different from Other Packages.vtt |
3.01KB |
| 1. What to Expect from the Following Sections.html |
2.48KB |
| 1. What to Expect from this Part.mp4 |
31.11MB |
| 1. What to Expect from this Part.srt |
4.63KB |
| 1. What to Expect from this Part.vtt |
4.05KB |
| 10.1 2.4. Numerical variables. Frequency distribution table_exercise_solution.xlsx.xlsx |
13.15KB |
| 10.1 Addition and Subtraction of Matrices Python Notebook.html |
178B |
| 10.1 Binary predictors.html |
134B |
| 10.1 Feature selection.html |
134B |
| 10.1 Indexing Elements - Resources.html |
134B |
| 10.1 Online p-value calculator.pdf.pdf |
1.15MB |
| 10.1 TensorFlow MNIST All Exercises.html |
144B |
| 10.2 2.4. Numerical variables. Frequency distribution table_exercise.xlsx.xlsx |
11.75KB |
| 10. A1 Linearity.html |
158B |
| 10. A Breakdown of our Data Science Infographic.html |
158B |
| 10. Addition and Subtraction of Matrices.mp4 |
32.62MB |
| 10. Addition and Subtraction of Matrices.srt |
4.05KB |
| 10. Addition and Subtraction of Matrices.vtt |
3.48KB |
| 10. Analyzing the Reasons for Absence.mp4 |
40.57MB |
| 10. Analyzing the Reasons for Absence.srt |
5.85KB |
| 10. Analyzing the Reasons for Absence.vtt |
5.12KB |
| 10. Binary Predictors in a Logistic Regression.mp4 |
38.44MB |
| 10. Binary Predictors in a Logistic Regression.srt |
5.42KB |
| 10. Binary Predictors in a Logistic Regression.vtt |
4.75KB |
| 10. Business Case Testing the Model.mp4 |
11.21MB |
| 10. Business Case Testing the Model.srt |
2.71KB |
| 10. Business Case Testing the Model.vtt |
2.36KB |
| 10. Central Limit Theorem.html |
158B |
| 10. Discrete Distributions The Bernoulli Distribution.html |
158B |
| 10. Feature Selection (F-regression).mp4 |
29.51MB |
| 10. Feature Selection (F-regression).srt |
6.67KB |
| 10. Feature Selection (F-regression).vtt |
5.85KB |
| 10. Indexing Elements.mp4 |
5.94MB |
| 10. Indexing Elements.srt |
1.71KB |
| 10. Indexing Elements.vtt |
1.47KB |
| 10. Interpreting the Coefficients of the Logistic Regression.mp4 |
40.40MB |
| 10. Interpreting the Coefficients of the Logistic Regression.srt |
7.25KB |
| 10. Interpreting the Coefficients of the Logistic Regression.vtt |
6.34KB |
| 10. Jupyter's Interface.html |
158B |
| 10. Margin of Error.mp4 |
59.16MB |
| 10. Margin of Error.srt |
6.12KB |
| 10. Margin of Error.vtt |
5.39KB |
| 10. MNIST Exercises.html |
2.13KB |
| 10. Mutually Exclusive Sets.html |
158B |
| 10. Numerical Variables Exercise.html |
81B |
| 10. p-value.mp4 |
55.87MB |
| 10. p-value.srt |
5.04KB |
| 10. p-value.vtt |
4.46KB |
| 10. Relationship between Clustering and Regression.mp4 |
9.93MB |
| 10. Relationship between Clustering and Regression.srt |
2.18KB |
| 10. Relationship between Clustering and Regression.vtt |
1.92KB |
| 10. Software Integration - Explained.html |
158B |
| 10. Solving Variations without Repetition.html |
158B |
| 10. Techniques for Working with Traditional Methods.mp4 |
123.51MB |
| 10. Techniques for Working with Traditional Methods.srt |
11.08KB |
| 10. Techniques for Working with Traditional Methods.vtt |
9.66KB |
| 10. The Linear Model with Multiple Inputs.html |
158B |
| 10. Using Seaborn for Graphs.mp4 |
12.25MB |
| 10. Using Seaborn for Graphs.srt |
1.48KB |
| 10. Using Seaborn for Graphs.vtt |
1.30KB |
| 11.10 TensorFlow MNIST '8. Learning Rate (Part 1)' Solution.html |
165B |
| 11.11 TensorFlow MNIST '9. Learning Rate (Part 2)' Solution.html |
165B |
| 11.1 2.5. The Histogram_lesson.xlsx.xlsx |
18.63KB |
| 11.1 Binary predictors - exercise.html |
134B |
| 11.1 Calculation of P-values.html |
134B |
| 11.1 Combinations With Repetition.pdf.pdf |
207.41KB |
| 11.1 Logistic Regression prior to Backward Elimination.html |
226B |
| 11.1 Market segmentation.html |
134B |
| 11.1 Python Introduction - Course Notes.pdf.pdf |
2.03MB |
| 11.1 TensorFlow Business Case Homework.html |
134B |
| 11.1 TensorFlow MNIST '3. Width and Depth' Solution.html |
160B |
| 11.2 Bank_data.csv.csv |
19.55KB |
| 11.2 TensorFlow MNIST 'Around 98% Accuracy' Solution.html |
157B |
| 11.3 TensorFlow MNIST '2. Depth' Solution.html |
150B |
| 11.4 TensorFlow MNIST '7. Batch size (Part 2)' Solution.html |
162B |
| 11.5 TensorFlow MNIST 'Time' Solution.html |
162B |
| 11.6 TensorFlow MNIST '6. Batch size (Part 1)' Solution.html |
162B |
| 11.7 TensorFlow MNIST '1. Width' Solution.html |
150B |
| 11.8 TensorFlow MNIST '5. Activation Functions (Part 2)' Solution.html |
172B |
| 11.9 TensorFlow MNIST '4. Activation Functions (Part 1)' Solution.html |
172B |
| 11. A2 No Endogeneity.mp4 |
35.67MB |
| 11. A2 No Endogeneity.srt |
5.24KB |
| 11. A2 No Endogeneity.vtt |
4.58KB |
| 11. Addition and Subtraction of Matrices.html |
158B |
| 11. A Note on Calculation of P-values with sklearn.html |
372B |
| 11. Backward Elimination or How to Simplify Your Model.mp4 |
39.56MB |
| 11. Backward Elimination or How to Simplify Your Model.srt |
5.24KB |
| 11. Backward Elimination or How to Simplify Your Model.vtt |
4.58KB |
| 11. Binary Predictors in a Logistic Regression - Exercise.html |
87B |
| 11. Business Case A Comment on the Homework.mp4 |
36.39MB |
| 11. Business Case A Comment on the Homework.srt |
5.30KB |
| 11. Business Case A Comment on the Homework.vtt |
4.65KB |
| 11. Dependence and Independence of Sets.mp4 |
34.78MB |
| 11. Dependence and Independence of Sets.srt |
3.47KB |
| 11. Dependence and Independence of Sets.vtt |
3.05KB |
| 11. Discrete Distributions The Binomial Distribution.mp4 |
65.52MB |
| 11. Discrete Distributions The Binomial Distribution.srt |
8.34KB |
| 11. Discrete Distributions The Binomial Distribution.vtt |
7.40KB |
| 11. How to Interpret the Regression Table.mp4 |
44.65MB |
| 11. How to Interpret the Regression Table.srt |
6.31KB |
| 11. How to Interpret the Regression Table.vtt |
5.50KB |
| 11. Indexing Elements.html |
158B |
| 11. Margin of Error.html |
158B |
| 11. Market Segmentation with Cluster Analysis (Part 1).mp4 |
43.01MB |
| 11. Market Segmentation with Cluster Analysis (Part 1).srt |
7.53KB |
| 11. Market Segmentation with Cluster Analysis (Part 1).vtt |
6.53KB |
| 11. MNIST Solutions.html |
2.19KB |
| 11. Obtaining Dummies from a Single Feature.mp4 |
81.11MB |
| 11. Obtaining Dummies from a Single Feature.srt |
10.21KB |
| 11. Obtaining Dummies from a Single Feature.vtt |
8.96KB |
| 11. p-value.html |
158B |
| 11. Python 2 vs Python 3.mp4 |
11.28MB |
| 11. Python 2 vs Python 3.srt |
3.32KB |
| 11. Python 2 vs Python 3.vtt |
2.95KB |
| 11. Solving Combinations.mp4 |
57.35MB |
| 11. Solving Combinations.srt |
5.61KB |
| 11. Solving Combinations.vtt |
4.98KB |
| 11. Standard error.mp4 |
22.78MB |
| 11. Standard error.srt |
2.03KB |
| 11. Standard error.vtt |
1.76KB |
| 11. Techniques for Working with Traditional Methods.html |
158B |
| 11. The Histogram.mp4 |
13.78MB |
| 11. The Histogram.srt |
3.01KB |
| 11. The Histogram.vtt |
2.67KB |
| 11. The Linear model with Multiple Inputs and Multiple Outputs.mp4 |
38.32MB |
| 11. The Linear model with Multiple Inputs and Multiple Outputs.srt |
5.47KB |
| 11. The Linear model with Multiple Inputs and Multiple Outputs.vtt |
4.79KB |
| 12.1 3.13. Confidence intervals. Two means. Dependent samples_lesson.xlsx.xlsx |
10.47KB |
| 12.1 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx.xlsx |
14.54KB |
| 12.1 Accuracy.html |
134B |
| 12.1 Errors when Adding Matrices Python Notebook.html |
220B |
| 12.1 Market segmentation.html |
134B |
| 12.1 Structure Your Code with Indentation - Resources.html |
134B |
| 12.1 Summary table with p-values.html |
134B |
| 12.1 TensorFlow Business Case Homework.html |
134B |
| 12. A2 No Endogeneity.html |
158B |
| 12. Business Case Final Exercise.html |
439B |
| 12. Calculating the Accuracy of the Model.mp4 |
32.85MB |
| 12. Calculating the Accuracy of the Model.srt |
4.13KB |
| 12. Calculating the Accuracy of the Model.vtt |
3.63KB |
| 12. Confidence intervals. Two means. Dependent samples.mp4 |
70.48MB |
| 12. Confidence intervals. Two means. Dependent samples.srt |
8.04KB |
| 12. Confidence intervals. Two means. Dependent samples.vtt |
7.10KB |
| 12. Creating a Summary Table with p-values.mp4 |
12.30MB |
| 12. Creating a Summary Table with p-values.srt |
3.01KB |
| 12. Creating a Summary Table with p-values.vtt |
2.62KB |
| 12. Dependence and Independence of Sets.html |
158B |
| 12. Discrete Distributions The Binomial Distribution.html |
158B |
| 12. Errors when Adding Matrices.mp4 |
11.18MB |
| 12. Errors when Adding Matrices.srt |
2.58KB |
| 12. Errors when Adding Matrices.vtt |
2.27KB |
| 12. EXERCISE - Obtaining Dummies from a Single Feature.html |
129B |
| 12. How to Interpret the Regression Table.html |
158B |
| 12. Market Segmentation with Cluster Analysis (Part 2).mp4 |
56.11MB |
| 12. Market Segmentation with Cluster Analysis (Part 2).srt |
9.19KB |
| 12. Market Segmentation with Cluster Analysis (Part 2).vtt |
7.96KB |
| 12. Real Life Examples of Traditional Methods.mp4 |
42.79MB |
| 12. Real Life Examples of Traditional Methods.srt |
3.59KB |
| 12. Real Life Examples of Traditional Methods.vtt |
3.14KB |
| 12. Solving Combinations.html |
158B |
| 12. Standard Error.html |
158B |
| 12. Structuring with Indentation.mp4 |
6.81MB |
| 12. Structuring with Indentation.srt |
2.27KB |
| 12. Structuring with Indentation.vtt |
1.96KB |
| 12. Test for the Mean. Population Variance Unknown.mp4 |
40.25MB |
| 12. Test for the Mean. Population Variance Unknown.srt |
5.73KB |
| 12. Test for the Mean. Population Variance Unknown.vtt |
5.11KB |
| 12. Testing the Model We Created.mp4 |
49.06MB |
| 12. Testing the Model We Created.srt |
6.50KB |
| 12. Testing the Model We Created.vtt |
5.67KB |
| 12. The Histogram.html |
158B |
| 12. The Linear model with Multiple Inputs and Multiple Outputs.html |
158B |
| 13.1 3.13. Confidence intervals. Two means. Dependent samples_exercise.xlsx.xlsx |
13.74KB |
| 13.1 4.6.Test-for-the-mean.Population-variance-unknown-exercise.xlsx.xlsx |
11.34KB |
| 13.1 Accuracy of the model - exercise.html |
134B |
| 13.1 Multiple linear regression - Exercise.html |
134B |
| 13.1 Statistics - PDF with Excel Solutions that don't visualize properly.pdf.pdf |
289.12KB |
| 13.1 Symmetry Explained.pdf.pdf |
85.04KB |
| 13.1 Transpose of a Matrix Python Notebook.html |
167B |
| 13.2 2.5.The-Histogram-exercise.xlsx.xlsx |
15.50KB |
| 13.2 3.13. Confidence intervals. Two means. Dependent samples_exercise_solution.xlsx.xlsx |
14.24KB |
| 13.2 4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx.xlsx |
12.61KB |
| 13.2 Bank_data.csv.csv |
19.55KB |
| 13.3 2.5.The-Histogram-exercise-solution.xlsx.xlsx |
17.10KB |
| 13. A3 Normality and Homoscedasticity.mp4 |
42.70MB |
| 13. A3 Normality and Homoscedasticity.srt |
6.67KB |
| 13. A3 Normality and Homoscedasticity.vtt |
5.81KB |
| 13. Calculating the Accuracy of the Model.html |
87B |
| 13. Confidence intervals. Two means. Dependent samples Exercise.html |
81B |
| 13. Decomposition of Variability.mp4 |
49.67MB |
| 13. Decomposition of Variability.srt |
4.17KB |
| 13. Decomposition of Variability.vtt |
3.67KB |
| 13. Discrete Distributions The Poisson Distribution.mp4 |
58.42MB |
| 13. Discrete Distributions The Poisson Distribution.srt |
6.50KB |
| 13. Discrete Distributions The Poisson Distribution.vtt |
5.77KB |
| 13. Estimators and Estimates.mp4 |
47.83MB |
| 13. Estimators and Estimates.srt |
3.72KB |
| 13. Estimators and Estimates.vtt |
3.27KB |
| 13. Graphical Representation of Simple Neural Networks.mp4 |
22.64MB |
| 13. Graphical Representation of Simple Neural Networks.srt |
2.69KB |
| 13. Graphical Representation of Simple Neural Networks.vtt |
2.34KB |
| 13. Histogram Exercise.html |
81B |
| 13. How is Clustering Useful.mp4 |
74.45MB |
| 13. How is Clustering Useful.srt |
6.40KB |
| 13. How is Clustering Useful.vtt |
5.65KB |
| 13. Machine Learning (ML) Techniques.mp4 |
99.33MB |
| 13. Machine Learning (ML) Techniques.srt |
8.74KB |
| 13. Machine Learning (ML) Techniques.vtt |
7.67KB |
| 13. Multiple Linear Regression - Exercise.html |
76B |
| 13. Saving the Model and Preparing it for Deployment.mp4 |
37.46MB |
| 13. Saving the Model and Preparing it for Deployment.srt |
5.58KB |
| 13. Saving the Model and Preparing it for Deployment.vtt |
4.88KB |
| 13. SOLUTION - Obtaining Dummies from a Single Feature.html |
116B |
| 13. Structuring with Indentation.html |
158B |
| 13. Symmetry of Combinations.mp4 |
38.69MB |
| 13. Symmetry of Combinations.srt |
4.31KB |
| 13. Symmetry of Combinations.vtt |
3.79KB |
| 13. Test for the Mean. Population Variance Unknown Exercise.html |
81B |
| 13. The Conditional Probability Formula.mp4 |
45.86MB |
| 13. The Conditional Probability Formula.srt |
4.94KB |
| 13. The Conditional Probability Formula.vtt |
4.42KB |
| 13. Transpose of a Matrix.mp4 |
38.08MB |
| 13. Transpose of a Matrix.srt |
5.37KB |
| 13. Transpose of a Matrix.vtt |
4.69KB |
| 14.1 2.6. Cross table and scatter plot.xlsx.xlsx |
26.12KB |
| 14.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_lesson.xlsx.xlsx |
9.83KB |
| 14.1 4.7. Test for the mean. Dependent samples_lesson.xlsx.xlsx |
9.79KB |
| 14.1 Dot Product Python Notebook.html |
154B |
| 14.1 Feature scaling.html |
134B |
| 14.1 iris_dataset.csv.csv |
2.40KB |
| 14.2 Exercise - part 1.html |
134B |
| 14. A4 No Autocorrelation.mp4 |
31.52MB |
| 14. A4 No Autocorrelation.srt |
4.91KB |
| 14. A4 No Autocorrelation.vtt |
4.27KB |
| 14. ARTICLE - A Note on 'pickling'.html |
2.14KB |
| 14. Confidence intervals. Two means. Independent samples (Part 1).mp4 |
28.75MB |
| 14. Confidence intervals. Two means. Independent samples (Part 1).srt |
6.07KB |
| 14. Confidence intervals. Two means. Independent samples (Part 1).vtt |
5.33KB |
| 14. Cross Tables and Scatter Plots.mp4 |
39.80MB |
| 14. Cross Tables and Scatter Plots.srt |
6.69KB |
| 14. Cross Tables and Scatter Plots.vtt |
5.87KB |
| 14. Decomposition of Variability.html |
158B |
| 14. Discrete Distributions The Poisson Distribution.html |
158B |
| 14. Dot Product.mp4 |
24.00MB |
| 14. Dot Product.srt |
4.27KB |
| 14. Dot Product.vtt |
3.68KB |
| 14. Dropping a Dummy Variable from the Data Set.html |
2.34KB |
| 14. Estimators and Estimates.html |
158B |
| 14. EXERCISE Species Segmentation with Cluster Analysis (Part 1).html |
87B |
| 14. Feature Scaling (Standardization).mp4 |
39.09MB |
| 14. Feature Scaling (Standardization).srt |
7.68KB |
| 14. Feature Scaling (Standardization).vtt |
6.77KB |
| 14. Graphical Representation of Simple Neural Networks.html |
158B |
| 14. Machine Learning (ML) Techniques.html |
158B |
| 14. Symmetry of Combinations.html |
158B |
| 14. Test for the Mean. Dependent Samples.mp4 |
50.37MB |
| 14. Test for the Mean. Dependent Samples.srt |
6.27KB |
| 14. Test for the Mean. Dependent Samples.vtt |
5.59KB |
| 14. The Conditional Probability Formula.html |
158B |
| 14. Underfitting and Overfitting.mp4 |
22.30MB |
| 14. Underfitting and Overfitting.srt |
4.98KB |
| 14. Underfitting and Overfitting.vtt |
4.37KB |
| 15.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise_solution.xlsx.xlsx |
10.12KB |
| 15.1 4.7. Test for the mean. Dependent samples_exercise_solution.xlsx.xlsx |
14.40KB |
| 15.1 Dot Product of Matrices Python Notebook.html |
171B |
| 15.1 Feature scaling standardization.html |
134B |
| 15.1 iris_dataset.csv.csv |
2.40KB |
| 15.1 Logistic Regression.html |
196B |
| 15.1 Solving Integrals.pdf.pdf |
343.85KB |
| 15.1 Testing the model.html |
134B |
| 15.2 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise.xlsx.xlsx |
9.83KB |
| 15.2 4.7. Test for the mean. Dependent samples_exercise.xlsx.xlsx |
12.80KB |
| 15.2 iris_with_answers.csv.csv |
3.63KB |
| 15.2 Logistic Regression with Comments.html |
210B |
| 15.3 Exercise - part 2.html |
134B |
| 15. A4 No autocorrelation.html |
158B |
| 15. Characteristics of Continuous Distributions.mp4 |
79.77MB |
| 15. Characteristics of Continuous Distributions.srt |
8.63KB |
| 15. Characteristics of Continuous Distributions.vtt |
7.62KB |
| 15. Confidence intervals. Two means. Independent samples (Part 1) Exercise.html |
81B |
| 15. Cross Tables and Scatter Plots.html |
158B |
| 15. Dot Product of Matrices.mp4 |
49.43MB |
| 15. Dot Product of Matrices.srt |
9.52KB |
| 15. Dot Product of Matrices.vtt |
8.22KB |
| 15. EXERCISE - Saving the Model (and Scaler).html |
284B |
| 15. EXERCISE Species Segmentation with Cluster Analysis (Part 2).html |
87B |
| 15. Feature Selection through Standardization of Weights.mp4 |
34.89MB |
| 15. Feature Selection through Standardization of Weights.srt |
7.27KB |
| 15. Feature Selection through Standardization of Weights.vtt |
6.41KB |
| 15. More on Dummy Variables A Statistical Perspective.mp4 |
13.74MB |
| 15. More on Dummy Variables A Statistical Perspective.srt |
1.70KB |
| 15. More on Dummy Variables A Statistical Perspective.vtt |
1.50KB |
| 15. Solving Combinations with Separate Sample Spaces.mp4 |
33.15MB |
| 15. Solving Combinations with Separate Sample Spaces.srt |
3.74KB |
| 15. Solving Combinations with Separate Sample Spaces.vtt |
3.31KB |
| 15. Test for the Mean. Dependent Samples Exercise.html |
81B |
| 15. Testing the Model.mp4 |
32.28MB |
| 15. Testing the Model.srt |
6.55KB |
| 15. Testing the Model.vtt |
5.70KB |
| 15. The Law of Total Probability.mp4 |
35.21MB |
| 15. The Law of Total Probability.srt |
3.48KB |
| 15. The Law of Total Probability.vtt |
3.10KB |
| 15. Types of Machine Learning.mp4 |
125.14MB |
| 15. Types of Machine Learning.srt |
10.52KB |
| 15. Types of Machine Learning.vtt |
9.23KB |
| 15. What is the Objective Function.mp4 |
17.91MB |
| 15. What is the Objective Function.srt |
2.12KB |
| 15. What is the Objective Function.vtt |
1.87KB |
| 15. What is the OLS.mp4 |
28.31MB |
| 15. What is the OLS.srt |
3.82KB |
| 15. What is the OLS.vtt |
3.33KB |
| 16.1 2.6. Cross table and scatter plot_exercise.xlsx.xlsx |
16.28KB |
| 16.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_lesson.xlsx.xlsx |
9.52KB |
| 16.1 4.8. Test for the mean. Independent samples (Part 1)_lesson.xlsx.xlsx |
9.63KB |
| 16.1 Predicting with the Standardized Cofficients.html |
134B |
| 16.1 Testing the model - exercise.html |
134B |
| 16.2 2.6. Cross table and scatter plot_exercise_solution.xlsx.xlsx |
40.44KB |
| 16.2 Bank_data.csv.csv |
19.55KB |
| 16.3 Bank_data_testing.csv.csv |
8.30KB |
| 16. A5 No Multicollinearity.mp4 |
28.70MB |
| 16. A5 No Multicollinearity.srt |
4.62KB |
| 16. A5 No Multicollinearity.vtt |
4.04KB |
| 16. Characteristics of Continuous Distributions.html |
158B |
| 16. Classifying the Various Reasons for Absence.mp4 |
74.61MB |
| 16. Classifying the Various Reasons for Absence.srt |
10.03KB |
| 16. Classifying the Various Reasons for Absence.vtt |
8.79KB |
| 16. Confidence intervals. Two means. Independent samples (Part 2).mp4 |
26.82MB |
| 16. Confidence intervals. Two means. Independent samples (Part 2).srt |
4.51KB |
| 16. Confidence intervals. Two means. Independent samples (Part 2).vtt |
3.98KB |
| 16. Cross Tables and Scatter Plots Exercise.html |
81B |
| 16. Predicting with the Standardized Coefficients.mp4 |
25.97MB |
| 16. Predicting with the Standardized Coefficients.srt |
5.59KB |
| 16. Predicting with the Standardized Coefficients.vtt |
4.90KB |
| 16. Preparing the Deployment of the Model through a Module.mp4 |
44.48MB |
| 16. Preparing the Deployment of the Model through a Module.srt |
5.62KB |
| 16. Preparing the Deployment of the Model through a Module.vtt |
4.90KB |
| 16. Solving Combinations with Separate Sample Spaces.html |
158B |
| 16. Test for the mean. Independent samples (Part 1).mp4 |
29.96MB |
| 16. Test for the mean. Independent samples (Part 1).srt |
5.51KB |
| 16. Test for the mean. Independent samples (Part 1).vtt |
4.80KB |
| 16. Testing the Model - Exercise.html |
87B |
| 16. The Additive Rule.mp4 |
25.74MB |
| 16. The Additive Rule.srt |
2.63KB |
| 16. The Additive Rule.vtt |
2.36KB |
| 16. Types of Machine Learning.html |
158B |
| 16. What is the Objective Function.html |
158B |
| 16. What is the OLS.html |
158B |
| 16. Why is Linear Algebra Useful.mp4 |
144.33MB |
| 16. Why is Linear Algebra Useful.srt |
11.79KB |
| 16. Why is Linear Algebra Useful.vtt |
10.31KB |
| 17.1 2.7. Mean, median and mode_lesson.xlsx.xlsx |
10.49KB |
| 17.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise_solution.xlsx.xlsx |
9.79KB |
| 17.1 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise.xlsx.xlsx |
10.77KB |
| 17.1 Feature scaling - exercise.html |
134B |
| 17.1 Normal Distribution - Exp and Var.pdf.pdf |
144.08KB |
| 17.2 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise.xlsx.xlsx |
9.17KB |
| 17.2 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise-solution.xlsx.xlsx |
11.25KB |
| 17. A5 No Multicollinearity.html |
158B |
| 17. Combinatorics in Real-Life The Lottery.mp4 |
39.38MB |
| 17. Combinatorics in Real-Life The Lottery.srt |
4.08KB |
| 17. Combinatorics in Real-Life The Lottery.vtt |
3.58KB |
| 17. Common Objective Functions L2-norm Loss.mp4 |
23.27MB |
| 17. Common Objective Functions L2-norm Loss.srt |
2.77KB |
| 17. Common Objective Functions L2-norm Loss.vtt |
2.44KB |
| 17. Confidence intervals. Two means. Independent samples (Part 2) Exercise.html |
81B |
| 17. Continuous Distributions The Normal Distribution.mp4 |
48.24MB |
| 17. Continuous Distributions The Normal Distribution.srt |
4.78KB |
| 17. Continuous Distributions The Normal Distribution.vtt |
4.23KB |
| 17. Feature Scaling (Standardization) - Exercise.html |
76B |
| 17. Mean, median and mode.mp4 |
37.12MB |
| 17. Mean, median and mode.srt |
5.73KB |
| 17. Mean, median and mode.vtt |
5.00KB |
| 17. Real Life Examples of Machine Learning (ML).mp4 |
36.82MB |
| 17. Real Life Examples of Machine Learning (ML).srt |
2.91KB |
| 17. Real Life Examples of Machine Learning (ML).vtt |
2.57KB |
| 17. R-Squared.mp4 |
41.04MB |
| 17. R-Squared.srt |
6.58KB |
| 17. R-Squared.vtt |
5.79KB |
| 17. Test for the mean. Independent samples (Part 1). Exercise.html |
81B |
| 17. The Additive Rule.html |
158B |
| 17. Using .concat() in Python.mp4 |
38.74MB |
| 17. Using .concat() in Python.srt |
5.08KB |
| 17. Using .concat() in Python.vtt |
4.42KB |
| 18.1 2.7. Mean, median and mode_exercise.xlsx.xlsx |
10.87KB |
| 18.1 4.9. Test for the mean. Independent samples (Part 2)_lesson.xlsx.xlsx |
9.31KB |
| 18.1 Dealing with categorical data.html |
134B |
| 18.2 2.7. Mean, median and mode_exercise_solution.xlsx.xlsx |
11.35KB |
| 18. Combinatorics in Real-Life The Lottery.html |
158B |
| 18. Common Objective Functions L2-norm Loss.html |
158B |
| 18. Confidence intervals. Two means. Independent samples (Part 3).mp4 |
19.93MB |
| 18. Confidence intervals. Two means. Independent samples (Part 3).srt |
1.96KB |
| 18. Confidence intervals. Two means. Independent samples (Part 3).vtt |
1.72KB |
| 18. Continuous Distributions The Normal Distribution.html |
158B |
| 18. Dealing with Categorical Data - Dummy Variables.mp4 |
55.66MB |
| 18. Dealing with Categorical Data - Dummy Variables.srt |
8.15KB |
| 18. Dealing with Categorical Data - Dummy Variables.vtt |
7.11KB |
| 18. EXERCISE - Using .concat() in Python.html |
189B |
| 18. Mean, Median and Mode Exercise.html |
81B |
| 18. Real Life Examples of Machine Learning (ML).html |
158B |
| 18. R-Squared.html |
158B |
| 18. Test for the mean. Independent samples (Part 2).mp4 |
36.39MB |
| 18. Test for the mean. Independent samples (Part 2).srt |
5.14KB |
| 18. Test for the mean. Independent samples (Part 2).vtt |
4.55KB |
| 18. The Multiplication Law.mp4 |
42.91MB |
| 18. The Multiplication Law.srt |
4.61KB |
| 18. The Multiplication Law.vtt |
4.08KB |
| 18. Underfitting and Overfitting.mp4 |
16.95MB |
| 18. Underfitting and Overfitting.srt |
3.46KB |
| 18. Underfitting and Overfitting.vtt |
3.05KB |
| 19.1 2.8. Skewness_lesson.xlsx.xlsx |
34.63KB |
| 19.1 Dealing with categorical data.html |
134B |
| 19.1 Train - Test split explained.html |
134B |
| 19. A Recap of Combinatorics.mp4 |
40.92MB |
| 19. A Recap of Combinatorics.srt |
3.77KB |
| 19. A Recap of Combinatorics.vtt |
3.29KB |
| 19. Common Objective Functions Cross-Entropy Loss.mp4 |
37.24MB |
| 19. Common Objective Functions Cross-Entropy Loss.srt |
5.26KB |
| 19. Common Objective Functions Cross-Entropy Loss.vtt |
4.57KB |
| 19. Continuous Distributions The Standard Normal Distribution.mp4 |
47.90MB |
| 19. Continuous Distributions The Standard Normal Distribution.srt |
5.28KB |
| 19. Continuous Distributions The Standard Normal Distribution.vtt |
4.70KB |
| 19. Dealing with Categorical Data - Dummy Variables.html |
76B |
| 19. Skewness.mp4 |
19.40MB |
| 19. Skewness.srt |
3.65KB |
| 19. Skewness.vtt |
3.20KB |
| 19. SOLUTION - Using .concat() in Python.html |
142B |
| 19. Test for the mean. Independent samples (Part 2).html |
158B |
| 19. The Multiplication Law.html |
158B |
| 19. Train - Test Split Explained.mp4 |
49.18MB |
| 19. Train - Test Split Explained.srt |
9.59KB |
| 19. Train - Test Split Explained.vtt |
8.35KB |
| 2.1 2.13.Practical-example.Descriptive-statistics-exercise-solution.xlsx.xlsx |
146.22KB |
| 2.1 3.17. Practical example. Confidence intervals_exercise.xlsx.xlsx |
1.73MB |
| 2.1 3.2. What is a distribution_lesson.xlsx.xlsx |
19.46KB |
| 2.1 4.10. Hypothesis testing section_practical example_exercise.xlsx.xlsx |
43.38KB |
| 2.1 A simple example in Python.html |
134B |
| 2.1 Basic NN Example (Part 2).html |
136B |
| 2.1 Course Notes - Section 6.pdf.pdf |
936.42KB |
| 2.1 Creating a Function with a Parameter - Resources.html |
134B |
| 2.1 Example of clustering.html |
134B |
| 2.1 sklearn - Linear Regression - Practical Example (Part 2).html |
134B |
| 2.2 2.13.Practical-example.Descriptive-statistics-exercise.xlsx.xlsx |
120.28KB |
| 2.2 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx.xlsx |
1.74MB |
| 2.2 4.10.Hypothesis-testing-section-practical-example-exercise-solution.xlsx.xlsx |
44.04KB |
| 2.2 Course notes_inferential statistics.pdf.pdf |
382.32KB |
| 2. Analyzing Age vs Probability in Tableau.mp4 |
80.01MB |
| 2. Analyzing Age vs Probability in Tableau.srt |
10.02KB |
| 2. Analyzing Age vs Probability in Tableau.vtt |
8.75KB |
| 2. A Note on Installing Packages in Anaconda.html |
2.32KB |
| 2. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.html |
158B |
| 2. A Simple Example in Python.mp4 |
34.70MB |
| 2. A Simple Example in Python.srt |
5.80KB |
| 2. A Simple Example in Python.vtt |
5.05KB |
| 2. A Simple Example of Clustering.mp4 |
51.82MB |
| 2. A Simple Example of Clustering.srt |
9.59KB |
| 2. A Simple Example of Clustering.vtt |
8.28KB |
| 2. Basic NN Example (Part 2).mp4 |
34.94MB |
| 2. Basic NN Example (Part 2).srt |
6.79KB |
| 2. Basic NN Example (Part 2).vtt |
5.88KB |
| 2. Business Case Outlining the Solution.mp4 |
12.21MB |
| 2. Business Case Outlining the Solution.srt |
2.52KB |
| 2. Business Case Outlining the Solution.vtt |
2.19KB |
| 2. Comparison Operators.html |
158B |
| 2. Creating the Targets for the Logistic Regression.mp4 |
45.79MB |
| 2. Creating the Targets for the Logistic Regression.srt |
8.39KB |
| 2. Creating the Targets for the Logistic Regression.vtt |
7.36KB |
| 2. Data Science and Business Buzzwords Why are there so many.html |
158B |
| 2. Debunking Common Misconceptions.html |
158B |
| 2. Dendrogram.mp4 |
29.06MB |
| 2. Dendrogram.srt |
7.36KB |
| 2. Dendrogram.vtt |
6.41KB |
| 2. Deploying the 'absenteeism_module' - Part I.mp4 |
25.48MB |
| 2. Deploying the 'absenteeism_module' - Part I.srt |
4.77KB |
| 2. Deploying the 'absenteeism_module' - Part I.vtt |
4.20KB |
| 2. Finding the Job - What to Expect and What to Look for.html |
158B |
| 2. For Loops.html |
158B |
| 2. Fundamentals of Combinatorics.html |
158B |
| 2. Fundamentals of Probability Distributions.html |
158B |
| 2. Further Reading on Null and Alternative Hypothesis.html |
2.29KB |
| 2. How are Going to Approach this Section.mp4 |
19.41MB |
| 2. How are Going to Approach this Section.srt |
2.93KB |
| 2. How are Going to Approach this Section.vtt |
2.57KB |
| 2. How to Create a Function with a Parameter.mp4 |
23.87MB |
| 2. How to Create a Function with a Parameter.srt |
4.35KB |
| 2. How to Create a Function with a Parameter.vtt |
3.78KB |
| 2. Importing the Absenteeism Data in Python.mp4 |
23.15MB |
| 2. Importing the Absenteeism Data in Python.srt |
3.99KB |
| 2. Importing the Absenteeism Data in Python.vtt |
3.48KB |
| 2. Introduction to Neural Networks.html |
158B |
| 2. Introduction to Programming.html |
158B |
| 2. Introduction to Regression Analysis.html |
158B |
| 2. Lists.html |
158B |
| 2. MNIST How to Tackle the MNIST.mp4 |
22.59MB |
| 2. MNIST How to Tackle the MNIST.srt |
3.62KB |
| 2. MNIST How to Tackle the MNIST.vtt |
3.17KB |
| 2. Multiple Linear Regression.html |
158B |
| 2. Necessary Programming Languages and Software Used in Data Science.html |
158B |
| 2. Object Oriented Programming.html |
158B |
| 2. Population and Sample.html |
158B |
| 2. Practical Example Descriptive Statistics Exercise.html |
81B |
| 2. Practical Example Hypothesis Testing Exercise.html |
81B |
| 2. Practical Example Inferential Statistics Exercise.html |
81B |
| 2. Practical Example Linear Regression (Part 2).mp4 |
46.00MB |
| 2. Practical Example Linear Regression (Part 2).srt |
8.03KB |
| 2. Practical Example Linear Regression (Part 2).vtt |
7.04KB |
| 2. Probability in Statistics.mp4 |
77.28MB |
| 2. Probability in Statistics.srt |
8.44KB |
| 2. Probability in Statistics.vtt |
7.49KB |
| 2. Problems with Gradient Descent.mp4 |
11.01MB |
| 2. Problems with Gradient Descent.srt |
2.83KB |
| 2. Problems with Gradient Descent.vtt |
2.50KB |
| 2. Sets and Events.html |
158B |
| 2. Some Examples of Clusters.mp4 |
71.53MB |
| 2. Some Examples of Clusters.srt |
6.24KB |
| 2. Some Examples of Clusters.vtt |
5.43KB |
| 2. Techniques for Working with Traditional Data.html |
158B |
| 2. The Basic Probability Formula.html |
158B |
| 2. The Business Task.mp4 |
39.15MB |
| 2. The Business Task.srt |
3.75KB |
| 2. The Business Task.vtt |
3.30KB |
| 2. The IF Statement.html |
158B |
| 2. The Linear Regression Model.html |
158B |
| 2. The Reason behind these Disciplines.html |
158B |
| 2. Types of Basic Preprocessing.mp4 |
11.84MB |
| 2. Types of Basic Preprocessing.srt |
1.64KB |
| 2. Types of Basic Preprocessing.vtt |
1.46KB |
| 2. Types of Data.html |
158B |
| 2. Types of Simple Initializations.mp4 |
14.31MB |
| 2. Types of Simple Initializations.srt |
3.68KB |
| 2. Types of Simple Initializations.vtt |
3.23KB |
| 2. Underfitting and Overfitting for Classification.mp4 |
25.07MB |
| 2. Underfitting and Overfitting for Classification.srt |
2.63KB |
| 2. Underfitting and Overfitting for Classification.vtt |
2.31KB |
| 2. Using Arithmetic Operators in Python.html |
158B |
| 2. Variables.html |
158B |
| 2. What's Further out there in terms of Machine Learning.mp4 |
20.13MB |
| 2. What's Further out there in terms of Machine Learning.srt |
2.55KB |
| 2. What's Further out there in terms of Machine Learning.vtt |
2.27KB |
| 2. What are Confidence Intervals.html |
158B |
| 2. What are Data, Servers, Clients, Requests, and Responses.html |
158B |
| 2. What Does the Course Cover.mp4 |
34.50MB |
| 2. What Does the Course Cover.srt |
5.08KB |
| 2. What Does the Course Cover.vtt |
4.49KB |
| 2. What is a Deep Net.mp4 |
29.53MB |
| 2. What is a Deep Net.srt |
3.25KB |
| 2. What is a Deep Net.vtt |
2.84KB |
| 2. What is a Distribution.mp4 |
61.59MB |
| 2. What is a Distribution.srt |
5.86KB |
| 2. What is a Distribution.vtt |
5.07KB |
| 2. What is a Matrix.html |
158B |
| 2. What is Machine Learning.html |
158B |
| 20.1 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2.xlsx.xlsx |
10.54KB |
| 20.1 Making predictions.html |
134B |
| 20.2 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2-solution.xlsx.xlsx |
11.39KB |
| 20. A Practical Example of Combinatorics.mp4 |
134.14MB |
| 20. A Practical Example of Combinatorics.srt |
13.97KB |
| 20. A Practical Example of Combinatorics.vtt |
12.37KB |
| 20. Bayes' Law.mp4 |
59.55MB |
| 20. Bayes' Law.srt |
7.17KB |
| 20. Bayes' Law.vtt |
6.38KB |
| 20. Common Objective Functions Cross-Entropy Loss.html |
158B |
| 20. Continuous Distributions The Standard Normal Distribution.html |
158B |
| 20. Making Predictions with the Linear Regression.mp4 |
24.70MB |
| 20. Making Predictions with the Linear Regression.srt |
4.45KB |
| 20. Making Predictions with the Linear Regression.vtt |
3.87KB |
| 20. Reordering Columns in a Pandas DataFrame in Python.mp4 |
14.02MB |
| 20. Reordering Columns in a Pandas DataFrame in Python.srt |
1.82KB |
| 20. Reordering Columns in a Pandas DataFrame in Python.vtt |
1.61KB |
| 20. Skewness.html |
158B |
| 20. Test for the mean. Independent samples (Part 2) Exercise.html |
81B |
| 21.1 2.8. Skewness_exercise_solution.xlsx.xlsx |
19.78KB |
| 21.1 GD-function-example.xlsx.xlsx |
42.33KB |
| 21.2 2.8. Skewness_exercise.xlsx.xlsx |
9.49KB |
| 21. Bayes' Law.html |
158B |
| 21. Continuous Distributions The Students' T Distribution.mp4 |
27.19MB |
| 21. Continuous Distributions The Students' T Distribution.srt |
2.79KB |
| 21. Continuous Distributions The Students' T Distribution.vtt |
2.52KB |
| 21. EXERCISE - Reordering Columns in a Pandas DataFrame in Python.html |
167B |
| 21. Optimization Algorithm 1-Parameter Gradient Descent.mp4 |
55.62MB |
| 21. Optimization Algorithm 1-Parameter Gradient Descent.srt |
8.47KB |
| 21. Optimization Algorithm 1-Parameter Gradient Descent.vtt |
7.43KB |
| 21. Skewness Exercise.html |
81B |
| 22.1 2.9. Variance_lesson.xlsx.xlsx |
10.08KB |
| 22. Continuous Distributions The Students' T Distribution.html |
158B |
| 22. Optimization Algorithm 1-Parameter Gradient Descent.html |
158B |
| 22. SOLUTION - Reordering Columns in a Pandas DataFrame in Python.html |
462B |
| 22. Variance.mp4 |
50.95MB |
| 22. Variance.srt |
7.54KB |
| 22. Variance.vtt |
6.64KB |
| 23.1 2.9. Variance_exercise_solution.xlsx.xlsx |
11.05KB |
| 23.1 Creating Checkpoints.html |
181B |
| 23.2 2.9. Variance_exercise.xlsx.xlsx |
10.83KB |
| 23. Continuous Distributions The Chi-Squared Distribution.mp4 |
26.34MB |
| 23. Continuous Distributions The Chi-Squared Distribution.srt |
2.76KB |
| 23. Continuous Distributions The Chi-Squared Distribution.vtt |
2.46KB |
| 23. Creating Checkpoints while Coding in Jupyter.mp4 |
25.68MB |
| 23. Creating Checkpoints while Coding in Jupyter.srt |
3.64KB |
| 23. Creating Checkpoints while Coding in Jupyter.vtt |
3.21KB |
| 23. Optimization Algorithm n-Parameter Gradient Descent.mp4 |
39.43MB |
| 23. Optimization Algorithm n-Parameter Gradient Descent.srt |
7.53KB |
| 23. Optimization Algorithm n-Parameter Gradient Descent.vtt |
6.62KB |
| 23. Variance Exercise.html |
522B |
| 24.1 2.10. Standard deviation and coefficient of variation_lesson.xlsx.xlsx |
10.97KB |
| 24. Continuous Distributions The Chi-Squared Distribution.html |
158B |
| 24. EXERCISE - Creating Checkpoints while Coding in Jupyter.html |
137B |
| 24. Optimization Algorithm n-Parameter Gradient Descent.html |
158B |
| 24. Standard Deviation and Coefficient of Variation.mp4 |
45.12MB |
| 24. Standard Deviation and Coefficient of Variation.srt |
6.60KB |
| 24. Standard Deviation and Coefficient of Variation.vtt |
5.75KB |
| 25. Continuous Distributions The Exponential Distribution.mp4 |
40.23MB |
| 25. Continuous Distributions The Exponential Distribution.srt |
4.14KB |
| 25. Continuous Distributions The Exponential Distribution.vtt |
3.65KB |
| 25. SOLUTION - Creating Checkpoints while Coding in Jupyter.html |
117B |
| 25. Standard Deviation.html |
158B |
| 26.1 2.10. Standard deviation and coefficient of variation_exercise_solution.xlsx.xlsx |
12.37KB |
| 26.2 2.10. Standard deviation and coefficient of variation_exercise.xlsx.xlsx |
11.30KB |
| 26. Analyzing the Dates from the Initial Data Set.mp4 |
57.28MB |
| 26. Analyzing the Dates from the Initial Data Set.srt |
8.43KB |
| 26. Analyzing the Dates from the Initial Data Set.vtt |
7.44KB |
| 26. Continuous Distributions The Exponential Distribution.html |
158B |
| 26. Standard Deviation and Coefficient of Variation Exercise.html |
81B |
| 27.1 2.11. Covariance_lesson.xlsx.xlsx |
24.92KB |
| 27. Continuous Distributions The Logistic Distribution.mp4 |
47.05MB |
| 27. Continuous Distributions The Logistic Distribution.srt |
5.03KB |
| 27. Continuous Distributions The Logistic Distribution.vtt |
4.45KB |
| 27. Covariance.mp4 |
27.49MB |
| 27. Covariance.srt |
4.92KB |
| 27. Covariance.vtt |
4.30KB |
| 27. Extracting the Month Value from the Date Column.mp4 |
47.80MB |
| 27. Extracting the Month Value from the Date Column.srt |
7.97KB |
| 27. Extracting the Month Value from the Date Column.vtt |
6.95KB |
| 28. Continuous Distributions The Logistic Distribution.html |
158B |
| 28. Covariance.html |
158B |
| 28. Extracting the Day of the Week from the Date Column.mp4 |
27.96MB |
| 28. Extracting the Day of the Week from the Date Column.srt |
4.48KB |
| 28. Extracting the Day of the Week from the Date Column.vtt |
3.91KB |
| 29.1 2.11. Covariance_exercise_solution.xlsx.xlsx |
29.51KB |
| 29.1 Removing the “Date” Column.html |
188B |
| 29.2 2.11. Covariance_exercise.xlsx.xlsx |
20.23KB |
| 29.2 Preprocessing.html |
181B |
| 29. Covariance Exercise.html |
81B |
| 29. EXERCISE - Removing the Date Column.html |
1.21KB |
| 3.1 3.9.The-z-table.xlsx.xlsx |
25.58KB |
| 3.1 Add an Else Statement - Resources.html |
134B |
| 3.1 Adjusted R-squared.html |
134B |
| 3.1 Another Way to Define a Function - Resources.html |
134B |
| 3.1 A simple example of clustering.html |
134B |
| 3.1 Basic NN Example (Part 3).html |
136B |
| 3.1 Course Notes - Section 2.pdf.pdf |
578.08KB |
| 3.1 FAQ_The_Data_Science_Course.pdf.pdf |
306.10KB |
| 3.1 Heatmaps.html |
134B |
| 3.1 Help Yourself with Methods - Resources.html |
134B |
| 3.1 Logical and Identity Operators - Resources.html |
134B |
| 3.1 Numbers and Boolean Values - Resources.html |
134B |
| 3.1 TensorFlow MNIST Part 1 with Comments.html |
159B |
| 3.1 The Double Equality Sign - Resources.html |
134B |
| 3.1 While Loops and Incrementing - Resources.html |
134B |
| 3.2 3.9. Population variance known, z-score_lesson.xlsx.xlsx |
11.21KB |
| 3.2 Countries_exercise.csv.csv |
8.27KB |
| 3.2 Download All Resources.html |
134B |
| 3. Adjusted R-Squared.mp4 |
54.84MB |
| 3. Adjusted R-Squared.srt |
7.53KB |
| 3. Adjusted R-Squared.vtt |
6.57KB |
| 3. A Note on Multicollinearity.html |
840B |
| 3. An overview of CNNs.mp4 |
58.79MB |
| 3. An overview of CNNs.srt |
6.44KB |
| 3. An overview of CNNs.vtt |
5.66KB |
| 3. A Simple Example of Clustering - Exercise.html |
87B |
| 3. Basic NN Example (Part 3).mp4 |
24.41MB |
| 3. Basic NN Example (Part 3).srt |
4.46KB |
| 3. Basic NN Example (Part 3).vtt |
3.88KB |
| 3. Checking the Content of the Data Set.mp4 |
61.90MB |
| 3. Checking the Content of the Data Set.srt |
7.05KB |
| 3. Checking the Content of the Data Set.vtt |
6.16KB |
| 3. Computing Expected Values.mp4 |
75.68MB |
| 3. Computing Expected Values.srt |
6.69KB |
| 3. Computing Expected Values.vtt |
5.90KB |
| 3. Confidence Intervals; Population Variance Known; z-score.mp4 |
78.20MB |
| 3. Confidence Intervals; Population Variance Known; z-score.srt |
9.80KB |
| 3. Confidence Intervals; Population Variance Known; z-score.vtt |
8.65KB |
| 3. Correlation vs Regression.mp4 |
14.73MB |
| 3. Correlation vs Regression.srt |
2.10KB |
| 3. Correlation vs Regression.vtt |
1.82KB |
| 3. Defining a Function in Python - Part II.mp4 |
14.79MB |
| 3. Defining a Function in Python - Part II.srt |
3.13KB |
| 3. Defining a Function in Python - Part II.vtt |
2.70KB |
| 3. Deploying the 'absenteeism_module' - Part II.mp4 |
54.26MB |
| 3. Deploying the 'absenteeism_module' - Part II.srt |
7.54KB |
| 3. Deploying the 'absenteeism_module' - Part II.vtt |
6.63KB |
| 3. Difference between Classification and Clustering.mp4 |
36.15MB |
| 3. Difference between Classification and Clustering.srt |
3.28KB |
| 3. Difference between Classification and Clustering.vtt |
2.89KB |
| 3. Digging into a Deep Net.mp4 |
59.37MB |
| 3. Digging into a Deep Net.srt |
6.70KB |
| 3. Digging into a Deep Net.vtt |
5.84KB |
| 3. Download All Resources and Important FAQ.html |
20.77KB |
| 3. EXERCISE - Reasons vs Probability.html |
401B |
| 3. Heatmaps.mp4 |
29.62MB |
| 3. Heatmaps.srt |
6.35KB |
| 3. Heatmaps.vtt |
5.47KB |
| 3. Introducing the Data Set.mp4 |
40.86MB |
| 3. Introducing the Data Set.srt |
4.15KB |
| 3. Introducing the Data Set.vtt |
3.66KB |
| 3. Levels of Measurement.mp4 |
54.39MB |
| 3. Levels of Measurement.srt |
4.55KB |
| 3. Levels of Measurement.vtt |
4.03KB |
| 3. Logical and Identity Operators.mp4 |
30.05MB |
| 3. Logical and Identity Operators.srt |
5.78KB |
| 3. Logical and Identity Operators.vtt |
4.99KB |
| 3. Logistic vs Logit Function.mp4 |
86.50MB |
| 3. Logistic vs Logit Function.srt |
4.88KB |
| 3. Logistic vs Logit Function.vtt |
4.27KB |
| 3. MNIST Relevant Packages.mp4 |
18.90MB |
| 3. MNIST Relevant Packages.srt |
2.12KB |
| 3. MNIST Relevant Packages.vtt |
1.89KB |
| 3. Modules and Packages.mp4 |
8.50MB |
| 3. Modules and Packages.srt |
1.26KB |
| 3. Modules and Packages.vtt |
1.13KB |
| 3. Momentum.mp4 |
16.43MB |
| 3. Momentum.srt |
3.45KB |
| 3. Momentum.vtt |
3.04KB |
| 3. Null vs Alternative Hypothesis.html |
158B |
| 3. Numbers and Boolean Values in Python.mp4 |
17.06MB |
| 3. Numbers and Boolean Values in Python.srt |
3.69KB |
| 3. Numbers and Boolean Values in Python.vtt |
3.17KB |
| 3. Permutations and How to Use Them.mp4 |
41.48MB |
| 3. Permutations and How to Use Them.srt |
4.07KB |
| 3. Permutations and How to Use Them.vtt |
3.61KB |
| 3. Probability in Data Science.mp4 |
63.49MB |
| 3. Probability in Data Science.srt |
6.65KB |
| 3. Probability in Data Science.vtt |
5.86KB |
| 3. Real Life Examples of Traditional Data.mp4 |
29.93MB |
| 3. Real Life Examples of Traditional Data.srt |
2.25KB |
| 3. Real Life Examples of Traditional Data.vtt |
1.97KB |
| 3. Scalars and Vectors.mp4 |
33.85MB |
| 3. Scalars and Vectors.srt |
3.78KB |
| 3. Scalars and Vectors.vtt |
3.30KB |
| 3. Selecting the Inputs for the Logistic Regression.mp4 |
16.76MB |
| 3. Selecting the Inputs for the Logistic Regression.srt |
3.66KB |
| 3. Selecting the Inputs for the Logistic Regression.vtt |
3.14KB |
| 3. Simple Linear Regression with sklearn.mp4 |
34.77MB |
| 3. Simple Linear Regression with sklearn.srt |
7.33KB |
| 3. Simple Linear Regression with sklearn.vtt |
6.45KB |
| 3. Standardization.mp4 |
50.98MB |
| 3. Standardization.srt |
5.98KB |
| 3. Standardization.vtt |
5.29KB |
| 3. State-of-the-Art Method - (Xavier) Glorot Initialization.mp4 |
17.15MB |
| 3. State-of-the-Art Method - (Xavier) Glorot Initialization.srt |
3.71KB |
| 3. State-of-the-Art Method - (Xavier) Glorot Initialization.vtt |
3.24KB |
| 3. TensorFlow Outline and Logic.mp4 |
47.70MB |
| 3. TensorFlow Outline and Logic.srt |
5.21KB |
| 3. TensorFlow Outline and Logic.vtt |
4.59KB |
| 3. The Double Equality Sign.mp4 |
5.99MB |
| 3. The Double Equality Sign.srt |
1.83KB |
| 3. The Double Equality Sign.vtt |
1.59KB |
| 3. The ELSE Statement.mp4 |
13.58MB |
| 3. The ELSE Statement.srt |
2.78KB |
| 3. The ELSE Statement.vtt |
2.45KB |
| 3. The Importance of Working with a Balanced Dataset.mp4 |
39.41MB |
| 3. The Importance of Working with a Balanced Dataset.srt |
4.48KB |
| 3. The Importance of Working with a Balanced Dataset.vtt |
3.91KB |
| 3. Training the Model.mp4 |
28.72MB |
| 3. Training the Model.srt |
4.28KB |
| 3. Training the Model.vtt |
3.79KB |
| 3. Types of Probability Distributions.mp4 |
92.30MB |
| 3. Types of Probability Distributions.srt |
9.31KB |
| 3. Types of Probability Distributions.vtt |
8.31KB |
| 3. Using Methods.mp4 |
21.96MB |
| 3. Using Methods.srt |
3.96KB |
| 3. Using Methods.vtt |
3.47KB |
| 3. Ways Sets Can Interact.mp4 |
45.39MB |
| 3. Ways Sets Can Interact.srt |
4.44KB |
| 3. Ways Sets Can Interact.vtt |
3.92KB |
| 3. What are Data Connectivity, APIs, and Endpoints.mp4 |
104.08MB |
| 3. What are Data Connectivity, APIs, and Endpoints.srt |
8.55KB |
| 3. What are Data Connectivity, APIs, and Endpoints.vtt |
7.56KB |
| 3. What is a Distribution.html |
158B |
| 3. What is the difference between Analysis and Analytics.mp4 |
53.56MB |
| 3. What is the difference between Analysis and Analytics.srt |
5.08KB |
| 3. What is the difference between Analysis and Analytics.vtt |
4.42KB |
| 3. What is Validation.mp4 |
32.71MB |
| 3. What is Validation.srt |
4.90KB |
| 3. What is Validation.vtt |
4.27KB |
| 3. While Loops and Incrementing.mp4 |
15.44MB |
| 3. While Loops and Incrementing.srt |
2.77KB |
| 3. While Loops and Incrementing.vtt |
2.42KB |
| 3. Why Python.mp4 |
75.07MB |
| 3. Why Python.srt |
6.97KB |
| 3. Why Python.vtt |
6.11KB |
| 30. Analyzing Several Straightforward Columns for this Exercise.mp4 |
29.51MB |
| 30. Analyzing Several Straightforward Columns for this Exercise.srt |
4.34KB |
| 30. Analyzing Several Straightforward Columns for this Exercise.vtt |
3.82KB |
| 30. Correlation Coefficient.mp4 |
29.39MB |
| 30. Correlation Coefficient.srt |
4.72KB |
| 30. Correlation Coefficient.vtt |
4.15KB |
| 31. Correlation.html |
158B |
| 31. Working on Education, Children, and Pets.mp4 |
39.59MB |
| 31. Working on Education, Children, and Pets.srt |
5.68KB |
| 31. Working on Education, Children, and Pets.vtt |
4.98KB |
| 32.1 2.12. Correlation_exercise.xlsx.xlsx |
29.30KB |
| 32.1 Exercises and solutions.html |
170B |
| 32.2 2.12. Correlation_exercise_solution.xlsx.xlsx |
29.48KB |
| 32.2 Preprocessing.html |
156B |
| 32. Correlation Coefficient Exercise.html |
81B |
| 32. Final Remarks of this Section.mp4 |
21.64MB |
| 32. Final Remarks of this Section.srt |
2.48KB |
| 32. Final Remarks of this Section.vtt |
2.20KB |
| 4.1 3.9.The-z-table.xlsx.xlsx |
25.58KB |
| 4.1 Audiobooks Preprocessing.html |
134B |
| 4.1 Basic NN Example (Part 4).html |
145B |
| 4.1 Building a logistic regression.html |
134B |
| 4.1 Clustering categorical data.html |
134B |
| 4.1 Course notes_hypothesis_testing.pdf.pdf |
648.20KB |
| 4.1 Create Lists with the range() Function - Resources.html |
134B |
| 4.1 Deploying the ‘absenteeism_module.html |
185B |
| 4.1 Else if, for Brief - Elif - Resources.html |
134B |
| 4.1 Shortcuts-for-Jupyter.pdf.pdf |
619.17KB |
| 4.1 sklearn - Linear Regression - Practical Example (Part 3).html |
134B |
| 4.1 TensorFlow MNIST Part 2 with Comments.html |
159B |
| 4.1 Using a Function in Another Function - Resources.html |
134B |
| 4.2 3.9. Population variance known, z-score_exercise.xlsx.xlsx |
10.83KB |
| 4.3 3.9. Population variance known, z-score_exercise_solution.xlsx.xlsx |
11.16KB |
| 4. Actual Introduction to TensorFlow.mp4 |
17.41MB |
| 4. Actual Introduction to TensorFlow.srt |
2.18KB |
| 4. Actual Introduction to TensorFlow.vtt |
1.92KB |
| 4. Adjusted R-Squared.html |
158B |
| 4. Analyzing Reasons vs Probability in Tableau.mp4 |
86.92MB |
| 4. Analyzing Reasons vs Probability in Tableau.srt |
9.55KB |
| 4. Analyzing Reasons vs Probability in Tableau.vtt |
8.37KB |
| 4. Basic NN Example (Part 4).mp4 |
61.14MB |
| 4. Basic NN Example (Part 4).srt |
10.87KB |
| 4. Basic NN Example (Part 4).vtt |
9.46KB |
| 4. Building a Logistic Regression.mp4 |
17.10MB |
| 4. Building a Logistic Regression.srt |
3.28KB |
| 4. Building a Logistic Regression.vtt |
2.89KB |
| 4. Business Case Preprocessing.mp4 |
103.42MB |
| 4. Business Case Preprocessing.srt |
13.46KB |
| 4. Business Case Preprocessing.vtt |
11.71KB |
| 4. Clustering Categorical Data.mp4 |
21.23MB |
| 4. Clustering Categorical Data.srt |
3.24KB |
| 4. Clustering Categorical Data.vtt |
2.81KB |
| 4. Computing Expected Values.html |
158B |
| 4. Confidence Intervals; Population Variance Known; z-score; Exercise.html |
81B |
| 4. Correlation vs Regression.html |
158B |
| 4. DeepMind and Deep Learning.html |
1.05KB |
| 4. Exporting the Obtained Data Set as a .csv.html |
998B |
| 4. How to Use a Function within a Function.mp4 |
8.13MB |
| 4. How to Use a Function within a Function.srt |
2.04KB |
| 4. How to Use a Function within a Function.vtt |
1.78KB |
| 4. Introducing the Data Set.html |
158B |
| 4. Introduction to Terms with Multiple Meanings.mp4 |
27.85MB |
| 4. Introduction to Terms with Multiple Meanings.srt |
4.06KB |
| 4. Introduction to Terms with Multiple Meanings.vtt |
3.60KB |
| 4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp4 |
29.08MB |
| 4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.srt |
5.94KB |
| 4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.vtt |
5.22KB |
| 4. Levels of Measurement.html |
158B |
| 4. Lists with the range() Function.mp4 |
11.42MB |
| 4. Lists with the range() Function.srt |
2.79KB |
| 4. Lists with the range() Function.vtt |
2.45KB |
| 4. Logical and Identity Operators.html |
158B |
| 4. Math Prerequisites.mp4 |
14.56MB |
| 4. Math Prerequisites.srt |
4.06KB |
| 4. Math Prerequisites.vtt |
3.53KB |
| 4. MNIST Model Outline.mp4 |
56.38MB |
| 4. MNIST Model Outline.srt |
9.07KB |
| 4. MNIST Model Outline.vtt |
7.91KB |
| 4. Modules and Packages.html |
158B |
| 4. Non-Linearities and their Purpose.mp4 |
27.69MB |
| 4. Non-Linearities and their Purpose.srt |
3.89KB |
| 4. Non-Linearities and their Purpose.vtt |
3.38KB |
| 4. Numbers and Boolean Values in Python.html |
158B |
| 4. Permutations and How to Use Them.html |
158B |
| 4. Practical Example Linear Regression (Part 3).mp4 |
23.69MB |
| 4. Practical Example Linear Regression (Part 3).srt |
4.12KB |
| 4. Practical Example Linear Regression (Part 3).vtt |
3.64KB |
| 4. Preprocessing Categorical Data.mp4 |
18.61MB |
| 4. Preprocessing Categorical Data.srt |
2.77KB |
| 4. Preprocessing Categorical Data.vtt |
2.42KB |
| 4. Rejection Region and Significance Level.mp4 |
112.61MB |
| 4. Rejection Region and Significance Level.srt |
8.77KB |
| 4. Rejection Region and Significance Level.vtt |
7.75KB |
| 4. Scalars and Vectors.html |
158B |
| 4. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp4 |
32.00MB |
| 4. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.srt |
6.70KB |
| 4. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.vtt |
5.86KB |
| 4. Standardizing the Data.mp4 |
20.59MB |
| 4. Standardizing the Data.srt |
4.20KB |
| 4. Standardizing the Data.vtt |
3.68KB |
| 4. Techniques for Working with Big Data.mp4 |
75.50MB |
| 4. Techniques for Working with Big Data.srt |
5.67KB |
| 4. Techniques for Working with Big Data.vtt |
4.96KB |
| 4. The Double Equality Sign.html |
158B |
| 4. The ELIF Statement.mp4 |
33.16MB |
| 4. The ELIF Statement.srt |
6.65KB |
| 4. The ELIF Statement.vtt |
5.75KB |
| 4. The Normal Distribution.mp4 |
49.85MB |
| 4. The Normal Distribution.srt |
4.90KB |
| 4. The Normal Distribution.vtt |
4.32KB |
| 4. Training, Validation, and Test Datasets.mp4 |
25.19MB |
| 4. Training, Validation, and Test Datasets.srt |
3.60KB |
| 4. Training, Validation, and Test Datasets.vtt |
3.11KB |
| 4. Training the Model.html |
158B |
| 4. Types of Probability Distributions.html |
158B |
| 4. Using Methods.html |
158B |
| 4. Ways Sets Can Interact.html |
158B |
| 4. What are Data Connectivity, APIs, and Endpoints.html |
158B |
| 4. What is the difference between Analysis and Analytics.html |
158B |
| 4. Why Python.html |
158B |
| 5.10 Basic NN Example Exercise 5 Solution.html |
149B |
| 5.1 2.3.Categorical-variables.Visualization-techniques-lesson.xlsx.xlsx |
30.77KB |
| 5.1 365_DataScience_Diagram.pdf.pdf |
323.08KB |
| 5.1 A Note on Boolean Values - Resources.html |
134B |
| 5.1 Basic NN Example Exercise 6 Solution.html |
149B |
| 5.1 Basic NN Example with TensorFlow (Part 1).html |
154B |
| 5.1 Building a logistic regression.html |
134B |
| 5.1 Categorical.csv.csv |
10.34KB |
| 5.1 Combining Conditional Statements and Functions - Resources.html |
134B |
| 5.1 List Slicing - Resources.html |
134B |
| 5.1 Multiple linear regression - exercise.html |
134B |
| 5.1 Preprocessing Exercise.html |
134B |
| 5.1 Reassign Values - Resources.html |
134B |
| 5.1 Strings - Resources.html |
134B |
| 5.1 TensorFlow MNIST Part 3 with Comments.html |
159B |
| 5.2 Basic NN Example Exercise 3b Solution.html |
154B |
| 5.2 Clustering categorical data.html |
134B |
| 5.2 Example_bank_data.csv.csv |
6.21KB |
| 5.3 Basic NN Example Exercise 2 Solution.html |
149B |
| 5.4 Basic NN Example Exercise 3a Solution.html |
154B |
| 5.5 Basic NN Example (All Exercises).html |
143B |
| 5.6 Basic NN Example Exercise 1 Solution.html |
149B |
| 5.7 Basic NN Example Exercise 3d Solution.html |
154B |
| 5.8 Basic NN Example Exercise 4 Solution.html |
149B |
| 5.9 Basic NN Example Exercise 3c Solution.html |
154B |
| 5. Activation Functions.mp4 |
25.10MB |
| 5. Activation Functions.srt |
5.26KB |
| 5. Activation Functions.vtt |
4.58KB |
| 5. A Note on Boolean Values.mp4 |
11.26MB |
| 5. A Note on Boolean Values.srt |
2.92KB |
| 5. A Note on Boolean Values.vtt |
2.55KB |
| 5. A Note on Normalization.html |
733B |
| 5. An Overview of RNNs.mp4 |
25.26MB |
| 5. An Overview of RNNs.srt |
3.71KB |
| 5. An Overview of RNNs.vtt |
3.30KB |
| 5. Basic NN Example Exercises.html |
1.37KB |
| 5. Binary and One-Hot Encoding.mp4 |
28.95MB |
| 5. Binary and One-Hot Encoding.srt |
4.81KB |
| 5. Binary and One-Hot Encoding.vtt |
4.18KB |
| 5. Building a Logistic Regression - Exercise.html |
87B |
| 5. Business Analytics, Data Analytics, and Data Science An Introduction.mp4 |
64.52MB |
| 5. Business Analytics, Data Analytics, and Data Science An Introduction.srt |
10.63KB |
| 5. Business Analytics, Data Analytics, and Data Science An Introduction.vtt |
9.26KB |
| 5. Business Case Preprocessing Exercise.html |
383B |
| 5. Categorical Variables - Visualization Techniques.mp4 |
38.47MB |
| 5. Categorical Variables - Visualization Techniques.srt |
6.43KB |
| 5. Categorical Variables - Visualization Techniques.vtt |
5.66KB |
| 5. Characteristics of Discrete Distributions.mp4 |
22.70MB |
| 5. Characteristics of Discrete Distributions.srt |
2.46KB |
| 5. Characteristics of Discrete Distributions.vtt |
2.17KB |
| 5. Clustering Categorical Data - Exercise.html |
87B |
| 5. Conditional Statements and Functions.mp4 |
15.69MB |
| 5. Conditional Statements and Functions.srt |
3.52KB |
| 5. Conditional Statements and Functions.vtt |
3.05KB |
| 5. Confidence Interval Clarifications.mp4 |
57.03MB |
| 5. Confidence Interval Clarifications.srt |
5.41KB |
| 5. Confidence Interval Clarifications.vtt |
4.82KB |
| 5. Dummies and Variance Inflation Factor - Exercise.html |
76B |
| 5. EXERCISE - Transportation Expense vs Probability.html |
561B |
| 5. Frequency.mp4 |
61.74MB |
| 5. Frequency.srt |
6.42KB |
| 5. Frequency.vtt |
5.66KB |
| 5. Geometrical Representation of the Linear Regression Model.mp4 |
5.13MB |
| 5. Geometrical Representation of the Linear Regression Model.srt |
1.64KB |
| 5. Geometrical Representation of the Linear Regression Model.vtt |
1.45KB |
| 5. How to Reassign Values.mp4 |
4.00MB |
| 5. How to Reassign Values.srt |
1.30KB |
| 5. How to Reassign Values.vtt |
1.13KB |
| 5. Intersection of Sets.mp4 |
26.96MB |
| 5. Intersection of Sets.srt |
2.47KB |
| 5. Intersection of Sets.vtt |
2.19KB |
| 5. Learning Rate Schedules Visualized.mp4 |
9.11MB |
| 5. Learning Rate Schedules Visualized.srt |
2.17KB |
| 5. Learning Rate Schedules Visualized.vtt |
1.90KB |
| 5. Linear Algebra and Geometry.mp4 |
49.79MB |
| 5. Linear Algebra and Geometry.srt |
4.10KB |
| 5. Linear Algebra and Geometry.vtt |
3.54KB |
| 5. List Slicing.mp4 |
30.77MB |
| 5. List Slicing.srt |
5.55KB |
| 5. List Slicing.vtt |
4.83KB |
| 5. Lists with the range() Function.html |
158B |
| 5. MNIST Loss and Optimization Algorithm.mp4 |
25.86MB |
| 5. MNIST Loss and Optimization Algorithm.srt |
3.54KB |
| 5. MNIST Loss and Optimization Algorithm.vtt |
3.09KB |
| 5. Multiple Linear Regression Exercise.html |
76B |
| 5. N-Fold Cross Validation.mp4 |
20.70MB |
| 5. N-Fold Cross Validation.srt |
4.18KB |
| 5. N-Fold Cross Validation.vtt |
3.67KB |
| 5. Python Strings.mp4 |
30.76MB |
| 5. Python Strings.srt |
7.45KB |
| 5. Python Strings.vtt |
6.46KB |
| 5. Rejection Region and Significance Level.html |
158B |
| 5. Simple Operations with Factorials.mp4 |
36.12MB |
| 5. Simple Operations with Factorials.srt |
3.27KB |
| 5. Simple Operations with Factorials.vtt |
2.90KB |
| 5. Splitting the Data for Training and Testing.mp4 |
52.76MB |
| 5. Splitting the Data for Training and Testing.srt |
8.10KB |
| 5. Splitting the Data for Training and Testing.vtt |
7.07KB |
| 5. Taking a Closer Look at APIs.mp4 |
115.59MB |
| 5. Taking a Closer Look at APIs.srt |
10.39KB |
| 5. Taking a Closer Look at APIs.vtt |
9.15KB |
| 5. Techniques for Working with Big Data.html |
158B |
| 5. The Normal Distribution.html |
158B |
| 5. Types of File Formats, supporting Tensors.mp4 |
20.35MB |
| 5. Types of File Formats, supporting Tensors.srt |
3.45KB |
| 5. Types of File Formats, supporting Tensors.vtt |
3.00KB |
| 5. Types of Machine Learning.mp4 |
45.10MB |
| 5. Types of Machine Learning.srt |
5.24KB |
| 5. Types of Machine Learning.vtt |
4.62KB |
| 5. What's Regression Analysis - a Quick Refresher.html |
2.84KB |
| 5. What is the Standard Library.mp4 |
18.03MB |
| 5. What is the Standard Library.srt |
3.57KB |
| 5. What is the Standard Library.vtt |
3.15KB |
| 5. Why Jupyter.mp4 |
44.32MB |
| 5. Why Jupyter.srt |
4.64KB |
| 5. Why Jupyter.vtt |
4.10KB |
| 6.1 3.4. Standard normal distribution_lesson.xlsx.xlsx |
10.38KB |
| 6.1 Basic NN Example with TensorFlow (Part 2).html |
154B |
| 6.1 Creating a Data Provider (Class).html |
134B |
| 6.1 Creating Functions Containing a Few Arguments - Resources.html |
134B |
| 6.1 How to choose the number of clusters.html |
134B |
| 6.1 Simple linear regression with sklearn.html |
134B |
| 6.1 sklearn - Linear Regression - Practical Example (Part 4).html |
134B |
| 6.1 TensorFlow MNIST Part 4 with Comments.html |
159B |
| 6.1 Tuples - Resources.html |
134B |
| 6.1 Use Conditional Statements and Loops Together - Resources.html |
134B |
| 6. Activation Functions Softmax Activation.mp4 |
25.93MB |
| 6. Activation Functions Softmax Activation.srt |
4.46KB |
| 6. Activation Functions Softmax Activation.vtt |
3.89KB |
| 6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp4 |
26.36MB |
| 6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).srt |
5.21KB |
| 6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).vtt |
4.57KB |
| 6. Analyzing Transportation Expense vs Probability in Tableau.mp4 |
68.54MB |
| 6. Analyzing Transportation Expense vs Probability in Tableau.srt |
7.21KB |
| 6. Analyzing Transportation Expense vs Probability in Tableau.vtt |
6.31KB |
| 6. An Invaluable Coding Tip.mp4 |
23.05MB |
| 6. An Invaluable Coding Tip.srt |
3.21KB |
| 6. An Invaluable Coding Tip.vtt |
2.78KB |
| 6. A Note on Boolean Values.html |
158B |
| 6. An Overview of non-NN Approaches.mp4 |
44.78MB |
| 6. An Overview of non-NN Approaches.srt |
5.12KB |
| 6. An Overview of non-NN Approaches.vtt |
4.56KB |
| 6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4 |
38.50MB |
| 6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.srt |
7.36KB |
| 6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.vtt |
6.47KB |
| 6. Business Analytics, Data Analytics, and Data Science An Introduction.html |
158B |
| 6. Calculating the Accuracy of the Model.mp4 |
43.91MB |
| 6. Calculating the Accuracy of the Model.srt |
5.19KB |
| 6. Calculating the Accuracy of the Model.vtt |
4.51KB |
| 6. Categorical Variables - Visualization Techniques.html |
158B |
| 6. Characteristics of Discrete Distributions.html |
158B |
| 6. Conditional Statements and Loops.mp4 |
16.09MB |
| 6. Conditional Statements and Loops.srt |
3.59KB |
| 6. Conditional Statements and Loops.vtt |
3.15KB |
| 6. Creating a Data Provider.mp4 |
76.34MB |
| 6. Creating a Data Provider.srt |
7.75KB |
| 6. Creating a Data Provider.vtt |
6.80KB |
| 6. Early Stopping or When to Stop Training.mp4 |
24.17MB |
| 6. Early Stopping or When to Stop Training.srt |
6.86KB |
| 6. Early Stopping or When to Stop Training.vtt |
6.01KB |
| 6. Fitting the Model and Assessing its Accuracy.mp4 |
41.62MB |
| 6. Fitting the Model and Assessing its Accuracy.srt |
7.38KB |
| 6. Fitting the Model and Assessing its Accuracy.vtt |
6.41KB |
| 6. Frequency.html |
158B |
| 6. Functions Containing a Few Arguments.mp4 |
7.58MB |
| 6. Functions Containing a Few Arguments.srt |
1.31KB |
| 6. Functions Containing a Few Arguments.vtt |
1.13KB |
| 6. Geometrical Representation of the Linear Regression Model.html |
158B |
| 6. How to Choose the Number of Clusters.mp4 |
44.13MB |
| 6. How to Choose the Number of Clusters.srt |
7.37KB |
| 6. How to Choose the Number of Clusters.vtt |
6.43KB |
| 6. How to Reassign Values.html |
158B |
| 6. Intersection of Sets.html |
158B |
| 6. Linear Algebra and Geometry.html |
158B |
| 6. Practical Example Linear Regression (Part 4).mp4 |
56.04MB |
| 6. Practical Example Linear Regression (Part 4).srt |
11.49KB |
| 6. Practical Example Linear Regression (Part 4).vtt |
10.02KB |
| 6. Python Strings.html |
158B |
| 6. Real Life Examples of Big Data.mp4 |
22.04MB |
| 6. Real Life Examples of Big Data.srt |
1.88KB |
| 6. Real Life Examples of Big Data.vtt |
1.64KB |
| 6. Simple Linear Regression with sklearn - Exercise.html |
76B |
| 6. Simple Operations with Factorials.html |
158B |
| 6. Student's T Distribution.mp4 |
35.43MB |
| 6. Student's T Distribution.srt |
4.14KB |
| 6. Student's T Distribution.vtt |
3.68KB |
| 6. Taking a Closer Look at APIs.html |
158B |
| 6. Test for Significance of the Model (F-Test).mp4 |
16.43MB |
| 6. Test for Significance of the Model (F-Test).srt |
2.56KB |
| 6. Test for Significance of the Model (F-Test).vtt |
2.23KB |
| 6. The Standard Normal Distribution.mp4 |
22.50MB |
| 6. The Standard Normal Distribution.srt |
3.94KB |
| 6. The Standard Normal Distribution.vtt |
3.45KB |
| 6. Tuples.mp4 |
16.67MB |
| 6. Tuples.srt |
3.39KB |
| 6. Tuples.vtt |
2.96KB |
| 6. Type I Error and Type II Error.mp4 |
43.94MB |
| 6. Type I Error and Type II Error.srt |
5.67KB |
| 6. Type I Error and Type II Error.vtt |
4.90KB |
| 6. Types of Machine Learning.html |
158B |
| 6. Using a Statistical Approach towards the Solution to the Exercise.mp4 |
20.18MB |
| 6. Using a Statistical Approach towards the Solution to the Exercise.srt |
2.80KB |
| 6. Using a Statistical Approach towards the Solution to the Exercise.vtt |
2.46KB |
| 6. What is the Standard Library.html |
158B |
| 6. Why Jupyter.html |
158B |
| 7.1 2.3. Categorical variables. Visualization techniques_exercise.xlsx.xlsx |
15.24KB |
| 7.1 365_DataScience.png.png |
6.93MB |
| 7.1 Add Comments - Resources.html |
134B |
| 7.1 All In - Conditional Statements, Functions, and Loops - Resources.html |
134B |
| 7.1 Arrays in Python Notebook.html |
181B |
| 7.1 Basic NN Example with TensorFlow (Part 3).html |
154B |
| 7.1 Dictionaries - Resources.html |
134B |
| 7.1 How to choose the number of clusters.html |
134B |
| 7.1 Notable Built-In Functions in Python - Resources.html |
134B |
| 7.1 TensorFlow Business Case Model Outline.html |
134B |
| 7.1 TensorFlow MNIST Part 5 with Comments.html |
159B |
| 7.2 365_DataScience_Diagram.pdf.pdf |
323.08KB |
| 7.2 Countries_exercise.csv.csv |
8.27KB |
| 7.2 Statistics - PDF with Excel Solutions that don't visualize properly.pdf.pdf |
289.12KB |
| 7.3 2.3. Categorical variables. Visualization techniques_exercise_solution.xlsx.xlsx |
41.11KB |
| 7. Adam (Adaptive Moment Estimation).mp4 |
22.35MB |
| 7. Adam (Adaptive Moment Estimation).srt |
3.33KB |
| 7. Adam (Adaptive Moment Estimation).vtt |
2.92KB |
| 7. Add Comments.mp4 |
5.00MB |
| 7. Add Comments.srt |
1.71KB |
| 7. Add Comments.vtt |
1.49KB |
| 7. Arrays in Python - A Convenient Way To Represent Matrices.mp4 |
26.68MB |
| 7. Arrays in Python - A Convenient Way To Represent Matrices.srt |
6.13KB |
| 7. Arrays in Python - A Convenient Way To Represent Matrices.vtt |
5.31KB |
| 7. Backpropagation.mp4 |
34.95MB |
| 7. Backpropagation.srt |
4.47KB |
| 7. Backpropagation.vtt |
3.91KB |
| 7. Basic NN Example with TF Loss Function and Gradient Descent.mp4 |
32.51MB |
| 7. Basic NN Example with TF Loss Function and Gradient Descent.srt |
4.83KB |
| 7. Basic NN Example with TF Loss Function and Gradient Descent.vtt |
4.21KB |
| 7. Built-in Functions in Python.mp4 |
22.02MB |
| 7. Built-in Functions in Python.srt |
4.21KB |
| 7. Built-in Functions in Python.vtt |
3.68KB |
| 7. Business Case Model Outline.mp4 |
53.13MB |
| 7. Business Case Model Outline.srt |
6.94KB |
| 7. Business Case Model Outline.vtt |
6.07KB |
| 7. Business Intelligence (BI) Techniques.mp4 |
89.94MB |
| 7. Business Intelligence (BI) Techniques.srt |
8.63KB |
| 7. Business Intelligence (BI) Techniques.vtt |
7.57KB |
| 7. Categorical Variables Exercise.html |
81B |
| 7. Communication between Software Products through Text Files.mp4 |
60.34MB |
| 7. Communication between Software Products through Text Files.srt |
5.47KB |
| 7. Communication between Software Products through Text Files.vtt |
4.80KB |
| 7. Conditional Statements, Functions, and Loops.mp4 |
9.48MB |
| 7. Conditional Statements, Functions, and Loops.srt |
2.41KB |
| 7. Conditional Statements, Functions, and Loops.vtt |
2.09KB |
| 7. Continuing with BI, ML, and AI.mp4 |
108.99MB |
| 7. Continuing with BI, ML, and AI.srt |
11.88KB |
| 7. Continuing with BI, ML, and AI.vtt |
10.43KB |
| 7. Creating a Summary Table with the Coefficients and Intercept.mp4 |
38.87MB |
| 7. Creating a Summary Table with the Coefficients and Intercept.srt |
6.63KB |
| 7. Creating a Summary Table with the Coefficients and Intercept.vtt |
5.77KB |
| 7. Dictionaries.mp4 |
25.05MB |
| 7. Dictionaries.srt |
4.21KB |
| 7. Dictionaries.vtt |
3.63KB |
| 7. Discrete Distributions The Uniform Distribution.mp4 |
24.40MB |
| 7. Discrete Distributions The Uniform Distribution.srt |
2.74KB |
| 7. Discrete Distributions The Uniform Distribution.vtt |
2.43KB |
| 7. Download All Resources.html |
458B |
| 7. Dropping a Column from a DataFrame in Python.mp4 |
61.76MB |
| 7. Dropping a Column from a DataFrame in Python.srt |
7.81KB |
| 7. Dropping a Column from a DataFrame in Python.vtt |
6.81KB |
| 7. Dummy Variables - Exercise.html |
713B |
| 7. Events and Their Complements.mp4 |
59.16MB |
| 7. Events and Their Complements.srt |
6.72KB |
| 7. Events and Their Complements.vtt |
5.96KB |
| 7. How to Choose the Number of Clusters - Exercise.html |
87B |
| 7. Importing Modules in Python.mp4 |
19.94MB |
| 7. Importing Modules in Python.srt |
4.82KB |
| 7. Importing Modules in Python.vtt |
4.17KB |
| 7. Installing Python and Jupyter.mp4 |
51.02MB |
| 7. Installing Python and Jupyter.srt |
8.84KB |
| 7. Installing Python and Jupyter.vtt |
7.78KB |
| 7. MNIST Batching and Early Stopping.mp4 |
12.85MB |
| 7. MNIST Batching and Early Stopping.srt |
2.93KB |
| 7. MNIST Batching and Early Stopping.vtt |
2.56KB |
| 7. Multiple Linear Regression with sklearn.mp4 |
20.07MB |
| 7. Multiple Linear Regression with sklearn.srt |
4.17KB |
| 7. Multiple Linear Regression with sklearn.vtt |
3.67KB |
| 7. OLS Assumptions.mp4 |
21.85MB |
| 7. OLS Assumptions.srt |
3.04KB |
| 7. OLS Assumptions.vtt |
2.67KB |
| 7. Python Packages Installation.mp4 |
40.58MB |
| 7. Python Packages Installation.srt |
5.62KB |
| 7. Python Packages Installation.vtt |
4.89KB |
| 7. Solving Variations with Repetition.mp4 |
34.00MB |
| 7. Solving Variations with Repetition.srt |
3.47KB |
| 7. Solving Variations with Repetition.vtt |
3.09KB |
| 7. Student's T Distribution.html |
158B |
| 7. The Linear Model (Linear Algebraic Version).mp4 |
28.44MB |
| 7. The Linear Model (Linear Algebraic Version).srt |
3.88KB |
| 7. The Linear Model (Linear Algebraic Version).vtt |
3.43KB |
| 7. The Standard Normal Distribution.html |
158B |
| 7. Type I Error and Type II Error.html |
158B |
| 7. Understanding Logistic Regression Tables.mp4 |
30.55MB |
| 7. Understanding Logistic Regression Tables.srt |
5.56KB |
| 7. Understanding Logistic Regression Tables.vtt |
4.84KB |
| 7. Union of Sets.mp4 |
57.19MB |
| 7. Union of Sets.srt |
5.53KB |
| 7. Union of Sets.vtt |
4.96KB |
| 8.1 2.4. Numerical variables. Frequency distribution table_lesson.xlsx.xlsx |
11.44KB |
| 8.1 3.11. The t-table.xlsx.xlsx |
15.85KB |
| 8.1 3.4.Standard-normal-distribution-exercise.xlsx.xlsx |
11.99KB |
| 8.1 4.4. Test for the mean. Population variance known_lesson.xlsx.xlsx |
10.96KB |
| 8.1 Basic NN Example with TensorFlow (Complete).html |
156B |
| 8.1 First regression in Python.html |
134B |
| 8.1 Iterating over Dictionaries - Resources.html |
134B |
| 8.1 sklearn - Linear Regression - Practical Example (Part 5).html |
134B |
| 8.1 TensorFlow Business Case Optimization.html |
134B |
| 8.1 TensorFlow MNIST Part 6 with Comments.html |
159B |
| 8.1 Tensors Notebook.html |
148B |
| 8.1 Understanding logistic regression.html |
134B |
| 8.2 3.11. Population variance unknown, t-score_lesson.xlsx.xlsx |
10.78KB |
| 8.2 3.4.Standard-normal-distribution-exercise-solution.xlsx.xlsx |
24.04KB |
| 8.2 Bank_data.csv.csv |
19.55KB |
| 8. Add Comments.html |
158B |
| 8. Backpropagation picture.mp4 |
19.50MB |
| 8. Backpropagation picture.srt |
3.98KB |
| 8. Backpropagation picture.vtt |
3.44KB |
| 8. Basic NN Example with TF Model Output.mp4 |
37.40MB |
| 8. Basic NN Example with TF Model Output.srt |
7.93KB |
| 8. Basic NN Example with TF Model Output.vtt |
6.87KB |
| 8. Business Case Optimization.mp4 |
41.52MB |
| 8. Business Case Optimization.srt |
6.60KB |
| 8. Business Case Optimization.vtt |
5.76KB |
| 8. Business Intelligence (BI) Techniques.html |
158B |
| 8. Calculating the Adjusted R-Squared in sklearn.mp4 |
30.88MB |
| 8. Calculating the Adjusted R-Squared in sklearn.srt |
6.27KB |
| 8. Calculating the Adjusted R-Squared in sklearn.vtt |
5.54KB |
| 8. Communication between Software Products through Text Files.html |
158B |
| 8. Confidence Intervals; Population Variance Unknown; t-score.mp4 |
32.20MB |
| 8. Confidence Intervals; Population Variance Unknown; t-score.srt |
5.71KB |
| 8. Confidence Intervals; Population Variance Unknown; t-score.vtt |
5.00KB |
| 8. Continuing with BI, ML, and AI.html |
158B |
| 8. Dictionaries.html |
158B |
| 8. Discrete Distributions The Uniform Distribution.html |
158B |
| 8. Events and Their Complements.html |
158B |
| 8. EXERCISE - Dropping a Column from a DataFrame in Python.html |
866B |
| 8. First Regression in Python.mp4 |
44.56MB |
| 8. First Regression in Python.srt |
7.91KB |
| 8. First Regression in Python.vtt |
6.91KB |
| 8. How to Iterate over Dictionaries.mp4 |
16.99MB |
| 8. How to Iterate over Dictionaries.srt |
3.88KB |
| 8. How to Iterate over Dictionaries.vtt |
3.34KB |
| 8. Importing Modules in Python.html |
158B |
| 8. Interpreting the Coefficients for Our Problem.mp4 |
52.37MB |
| 8. Interpreting the Coefficients for Our Problem.srt |
7.89KB |
| 8. Interpreting the Coefficients for Our Problem.vtt |
6.93KB |
| 8. MNIST Learning.mp4 |
46.68MB |
| 8. MNIST Learning.srt |
10.20KB |
| 8. MNIST Learning.vtt |
8.89KB |
| 8. Numerical Variables - Frequency Distribution Table.mp4 |
25.85MB |
| 8. Numerical Variables - Frequency Distribution Table.srt |
4.36KB |
| 8. Numerical Variables - Frequency Distribution Table.vtt |
3.83KB |
| 8. OLS Assumptions.html |
158B |
| 8. Practical Example Linear Regression (Part 5).mp4 |
57.88MB |
| 8. Practical Example Linear Regression (Part 5).srt |
10.59KB |
| 8. Practical Example Linear Regression (Part 5).vtt |
9.28KB |
| 8. Pros and Cons of K-Means Clustering.mp4 |
37.70MB |
| 8. Pros and Cons of K-Means Clustering.srt |
4.62KB |
| 8. Pros and Cons of K-Means Clustering.vtt |
4.01KB |
| 8. Python Functions.html |
158B |
| 8. Solving Variations with Repetition.html |
158B |
| 8. Test for the Mean. Population Variance Known.mp4 |
54.23MB |
| 8. Test for the Mean. Population Variance Known.srt |
8.15KB |
| 8. Test for the Mean. Population Variance Known.vtt |
7.12KB |
| 8. The Linear Model.html |
158B |
| 8. The Standard Normal Distribution Exercise.html |
81B |
| 8. Understanding Jupyter's Interface - the Notebook Dashboard.mp4 |
13.79MB |
| 8. Understanding Jupyter's Interface - the Notebook Dashboard.srt |
3.74KB |
| 8. Understanding Jupyter's Interface - the Notebook Dashboard.vtt |
3.25KB |
| 8. Understanding Logistic Regression Tables - Exercise.html |
87B |
| 8. Union of Sets.html |
158B |
| 8. What is a Tensor.mp4 |
22.53MB |
| 8. What is a Tensor.srt |
3.61KB |
| 8. What is a Tensor.vtt |
3.17KB |
| 9.1 3.11. Population variance unknown, t-score_exercise.xlsx.xlsx |
10.62KB |
| 9.1 365_DataScience.png.png |
6.93MB |
| 9.1 4.4. Test for the mean. Population variance known_exercise.xlsx.xlsx |
11.03KB |
| 9.1 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf.pdf |
182.36KB |
| 9.1 Basic NN Example with TensorFlow Exercise 2.4 Solution.html |
162B |
| 9.1 Calculating the Adjusted R-Squared.html |
134B |
| 9.1 First regression in Python - Exercise.html |
134B |
| 9.1 Line Continuation - Resources.html |
134B |
| 9.1 Logistic Regression prior to Custom Scaler.html |
219B |
| 9.1 TensorFlow Business Case Interpretation.html |
134B |
| 9.1 TensorFlow MNIST Complete Code with Comments.html |
152B |
| 9.2 3.11. Population variance unknown, t-score_exercise_solution.xlsx.xlsx |
11.10KB |
| 9.2 4.4. Test for the mean. Population variance known_exercise_solution.xlsx.xlsx |
11.22KB |
| 9.2 Basic NN Example with TensorFlow Exercise 2.2 Solution.html |
162B |
| 9.3 Basic NN Example with TensorFlow Exercise 2.1 Solution.html |
162B |
| 9.4 Basic NN Example with TensorFlow Exercise 3 Solution.html |
160B |
| 9.5 Basic NN Example with TensorFlow Exercise 4 Solution.html |
160B |
| 9.6 Basic NN Example with TensorFlow Exercise 2.3 Solution.html |
162B |
| 9.7 Basic NN Example with TensorFlow Exercise 1 Solution.html |
160B |
| 9.8 Basic NN Example with TensorFlow (All Exercises).html |
154B |
| 9. A1 Linearity.mp4 |
12.61MB |
| 9. A1 Linearity.srt |
2.36KB |
| 9. A1 Linearity.vtt |
2.07KB |
| 9. A Breakdown of our Data Science Infographic.mp4 |
67.74MB |
| 9. A Breakdown of our Data Science Infographic.srt |
5.11KB |
| 9. A Breakdown of our Data Science Infographic.vtt |
4.45KB |
| 9. Backpropagation - A Peek into the Mathematics of Optimization.html |
539B |
| 9. Basic NN Example with TF Exercises.html |
1.59KB |
| 9. Business Case Interpretation.mp4 |
25.74MB |
| 9. Business Case Interpretation.srt |
2.94KB |
| 9. Business Case Interpretation.vtt |
2.60KB |
| 9. Calculating the Adjusted R-Squared in sklearn - Exercise.html |
76B |
| 9. Central Limit Theorem.mp4 |
62.88MB |
| 9. Central Limit Theorem.srt |
5.64KB |
| 9. Central Limit Theorem.vtt |
4.95KB |
| 9. Confidence Intervals; Population Variance Unknown; t-score; Exercise.html |
81B |
| 9. Discrete Distributions The Bernoulli Distribution.mp4 |
34.14MB |
| 9. Discrete Distributions The Bernoulli Distribution.srt |
3.86KB |
| 9. Discrete Distributions The Bernoulli Distribution.vtt |
3.48KB |
| 9. First Regression in Python Exercise.html |
1.33KB |
| 9. Linear Regression - Exercise.html |
503B |
| 9. MNIST Results and Testing.mp4 |
62.77MB |
| 9. MNIST Results and Testing.srt |
8.17KB |
| 9. MNIST Results and Testing.vtt |
7.15KB |
| 9. Mutually Exclusive Sets.mp4 |
25.39MB |
| 9. Mutually Exclusive Sets.srt |
2.52KB |
| 9. Mutually Exclusive Sets.vtt |
2.24KB |
| 9. Numerical Variables - Frequency Distribution Table.html |
158B |
| 9. Prerequisites for Coding in the Jupyter Notebooks.mp4 |
30.59MB |
| 9. Prerequisites for Coding in the Jupyter Notebooks.srt |
7.79KB |
| 9. Prerequisites for Coding in the Jupyter Notebooks.vtt |
6.80KB |
| 9. Real Life Examples of Business Intelligence (BI).mp4 |
29.54MB |
| 9. Real Life Examples of Business Intelligence (BI).srt |
2.13KB |
| 9. Real Life Examples of Business Intelligence (BI).vtt |
1.89KB |
| 9. Software Integration - Explained.mp4 |
72.65MB |
| 9. Software Integration - Explained.srt |
6.82KB |
| 9. Software Integration - Explained.vtt |
6.00KB |
| 9. SOLUTION - Dropping a Column from a DataFrame in Python.html |
113B |
| 9. Solving Variations without Repetition.mp4 |
43.14MB |
| 9. Solving Variations without Repetition.srt |
4.53KB |
| 9. Solving Variations without Repetition.vtt |
4.00KB |
| 9. Standardizing only the Numerical Variables (Creating a Custom Scaler).mp4 |
41.20MB |
| 9. Standardizing only the Numerical Variables (Creating a Custom Scaler).srt |
5.03KB |
| 9. Standardizing only the Numerical Variables (Creating a Custom Scaler).vtt |
4.41KB |
| 9. Test for the Mean. Population Variance Known Exercise.html |
81B |
| 9. The Linear Model with Multiple Inputs.mp4 |
25.12MB |
| 9. The Linear Model with Multiple Inputs.srt |
3.10KB |
| 9. The Linear Model with Multiple Inputs.vtt |
2.74KB |
| 9. To Standardize or not to Standardize.mp4 |
30.11MB |
| 9. To Standardize or not to Standardize.srt |
5.89KB |
| 9. To Standardize or not to Standardize.vtt |
5.14KB |
| 9. Understanding Line Continuation.mp4 |
2.35MB |
| 9. Understanding Line Continuation.srt |
1.14KB |
| 9. Understanding Line Continuation.vtt |
1.00KB |
| 9. What do the Odds Actually Mean.mp4 |
32.28MB |
| 9. What do the Odds Actually Mean.srt |
4.79KB |
| 9. What do the Odds Actually Mean.vtt |
4.18KB |
| 9. What is a Tensor.html |
158B |
| Discuss.FTUForum.com.url |
294B |
| FreeCoursesOnline.Me.url |
286B |
| FTUApps.com.url |
239B |
| FTUForum.com.url |
328B |
| How you can help Team-FTU.txt |
237B |