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[CourseClub.ME].url 122B
[FCS Forum].url 133B
[FreeCourseSite.com].url 127B
1.1 1.04. Real-life example.csv 219.83KB
1.1 2.13. Practical example. Descriptive statistics_lesson.xlsx 146.51KB
1.1 3.17. Practical example. Confidence intervals_lesson.xlsx 1.74MB
1.1 4.10.Hypothesis-testing-section-practical-example.xlsx 51.90KB
1.1 Absenteeism_predictions.csv 2.10KB
1.1 Absenteeism_preprocessed.csv 29.13KB
1.1 Additional-Python-Tools-Solutions.ipynb 25.49KB
1.1 Arithmetic Operators - Exercise_Py3.ipynb 2.62KB
1.1 Audiobooks_data.csv 710.77KB
1.1 Audiobooks_data.csv 710.77KB
1.1 Comparison Operators - Solution_Py3.ipynb 2.41KB
1.1 Course_Notes_Cluster_Analysis.pdf 208.65KB
1.1 Course_Notes_Logistic_Regression.pdf 335.17KB
1.1 Course notes_descriptive_statistics.pdf 482.21KB
1.1 Course notes_hypothesis_testing.pdf 656.44KB
1.1 Course notes_inferential statistics.pdf 382.32KB
1.1 Course notes_regression_analysis.pdf 312.18KB
1.1 Course notes_regression_analysis.pdf 312.18KB
1.1 Course Notes - Basic Probability.pdf 371.05KB
1.1 Course Notes - Bayesian Inference.pdf 386.01KB
1.1 Course Notes - Combinatorics.pdf 226.12KB
1.1 Course Notes - Probability Distributions.pdf 463.95KB
1.1 Course Notes - Section 2.pdf 578.08KB
1.1 Course Notes - Section 6.pdf 936.42KB
1.1 data_preprocessing_homework.pdf 134.47KB
1.1 Defining a Function in Python - Lecture_Py3.ipynb 868B
1.1 For Loops - Solution_Py3.ipynb 1.80KB
1.1 Introduction to the If Statement - Lecture_Py3.ipynb 1.14KB
1.1 Lists - Solution_Py3.ipynb 3.18KB
1.1 Minimal_example_Part_1.ipynb 1.19KB
1.1 model.original 1.01KB
1.1 Probability in Finance Homework.pdf 110.68KB
1.1 Shortcuts-for-Jupyter.pdf 619.17KB
1.1 Statistics Glossary.xlsx 20.26KB
1.1 Variables - Lecture_Py3.ipynb 3.61KB
1.2 Absenteeism_data.csv 32.05KB
1.2 Additional-Python-Tools-Exercises.ipynb 11.37KB
1.2 Arithmetic Operators - Lecture_Py3.ipynb 3.53KB
1.2 Comparison Operators - Lecture_Py3.ipynb 2.53KB
1.2 Course notes_descriptive_statistics.pdf 482.21KB
1.2 For Loops - Exercise_Py3.ipynb 1.28KB
1.2 Glossary.xlsx 19.97KB
1.2 Introduction to the If Statement - Solution_Py3.ipynb 2.19KB
1.2 Lists - Exercise_Py3.ipynb 2.14KB
1.2 Probability in Finance Solutions.pdf 184.46KB
1.2 scaler.original 1.86KB
1.2 Shortcuts-for-Jupyter.pdf 619.17KB
1.2 sklearn - Linear Regression - Practical Example (Part 1)_with_comments.ipynb 171.38KB
1.2 Variables - Exercise_Py3.ipynb 2.23KB
1.3 absenteeism_module.py 6.62KB
1.3 Additional-Python-Tools-Lectures.ipynb 13.47KB
1.3 Arithmetic Operators - Solution_Py3.ipynb 4.24KB
1.3 Comparison Operators - Exercise_Py3.ipynb 1.61KB
1.3 df_preprocessed.csv 29.11KB
1.3 For Loops - Lecture_Py3.ipynb 1.26KB
1.3 Introduction to the If Statement - Exercise_Py3.ipynb 1.53KB
1.3 Lists - Lecture_Py3.ipynb 2.70KB
1.3 Python Introduction - Course Notes.pdf 2.04MB
1.3 sklearn - Linear Regression - Practical Example (Part 1).ipynb 166.91KB
1.4 Absenteeism Exercise - Integration.ipynb 62.35KB
1.4 Variables - Solution_Py3.ipynb 3.79KB
1.5 Absenteeism_new_data.csv 1.87KB
1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 126.87MB
1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.srt 8.99KB
1. A Practical Example What You Will Learn in This Course.mp4 49.03MB
1. A Practical Example What You Will Learn in This Course.srt 6.37KB
1. Are You Sure You're All Set.html 519B
1. Basic NN Example (Part 1).mp4 20.60MB
1. Basic NN Example (Part 1).srt 4.46KB
1. Bonus Lecture Next Steps.html 2.53KB
1. Business Case Exploring the Dataset and Identifying Predictors.mp4 66.27MB
1. Business Case Exploring the Dataset and Identifying Predictors.srt 10.66KB
1. Business Case Getting Acquainted with the Dataset.mp4 87.65MB
1. Business Case Getting Acquainted with the Dataset.srt 10.78KB
1. Comparison Operators.mp4 10.17MB
1. Comparison Operators.srt 2.47KB
1. Data Science and Business Buzzwords Why are there so Many.mp4 81.41MB
1. Data Science and Business Buzzwords Why are there so Many.srt 6.62KB
1. Debunking Common Misconceptions.mp4 72.85MB
1. Debunking Common Misconceptions.srt 5.29KB
1. Defining a Function in Python.mp4 6.30MB
1. Defining a Function in Python.srt 2.42KB
1. EXERCISE - Age vs Probability.html 385B
1. Exploring the Problem with a Machine Learning Mindset.mp4 27.55MB
1. Exploring the Problem with a Machine Learning Mindset.srt 4.58KB
1. Finding the Job - What to Expect and What to Look for.mp4 54.38MB
1. Finding the Job - What to Expect and What to Look for.srt 4.49KB
1. For Loops.mp4 23.60MB
1. For Loops.srt 6.58KB
1. Fundamentals of Combinatorics.mp4 16.21MB
1. Fundamentals of Combinatorics.srt 1.30KB
1. Fundamentals of Probability Distributions.mp4 73.40MB
1. Fundamentals of Probability Distributions.srt 7.54KB
1. Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 52.31MB
1. Game Plan for this Python, SQL, and Tableau Business Exercise.srt 5.46KB
1. How to Install TensorFlow 2.0.mp4 38.76MB
1. How to Install TensorFlow 2.0.srt 6.38KB
1. Introduction.mp4 15.50MB
1. Introduction.srt 1.63KB
1. Introduction to Cluster Analysis.mp4 53.43MB
1. Introduction to Cluster Analysis.srt 4.80KB
1. Introduction to Logistic Regression.mp4 27.07MB
1. Introduction to Logistic Regression.srt 1.61KB
1. Introduction to Neural Networks.mp4 42.93MB
1. Introduction to Neural Networks.srt 42.94MB
1. Introduction to Programming.mp4 58.54MB
1. Introduction to Programming.srt 6.90KB
1. Introduction to Regression Analysis.mp4 17.32MB
1. Introduction to Regression Analysis.srt 2.21KB
1. K-Means Clustering.mp4 27.29MB
1. K-Means Clustering.srt 6.67KB
1. Lists.mp4 37.79MB
1. Lists.srt 9.83KB
1. MNIST The Dataset.mp4 13.39MB
1. MNIST The Dataset.srt 3.58KB
1. MNIST What is the MNIST Dataset.mp4 17.82MB
1. MNIST What is the MNIST Dataset.srt 3.49KB
1. Multiple Linear Regression.mp4 21.53MB
1. Multiple Linear Regression.srt 3.35KB
1. Necessary Programming Languages and Software Used in Data Science.mp4 103.52MB
1. Necessary Programming Languages and Software Used in Data Science.srt 7.29KB
1. Null vs Alternative Hypothesis.mp4 92.04MB
1. Null vs Alternative Hypothesis.srt 6.97KB
1. Object Oriented Programming.mp4 33.59MB
1. Object Oriented Programming.srt 6.10KB
1. Population and Sample.mp4 58.11MB
1. Population and Sample.srt 5.47KB
1. Practical Example Descriptive Statistics.mp4 160.46MB
1. Practical Example Descriptive Statistics.srt 20.78KB
1. Practical Example Hypothesis Testing.mp4 69.48MB
1. Practical Example Hypothesis Testing.srt 8.49KB
1. Practical Example Inferential Statistics.mp4 102.66MB
1. Practical Example Inferential Statistics.srt 13.64KB
1. Practical Example Linear Regression (Part 1).mp4 97.08MB
1. Practical Example Linear Regression (Part 1).srt 14.86KB
1. Preprocessing Introduction.mp4 27.78MB
1. Preprocessing Introduction.srt 3.87KB
1. Probability in Finance.mp4 99.06MB
1. Probability in Finance.srt 9.83KB
1. READ ME!!!!.html 564B
1. Sets and Events.mp4 53.46MB
1. Sets and Events.srt 5.06KB
1. Stochastic Gradient Descent.mp4 28.68MB
1. Stochastic Gradient Descent.srt 4.81KB
1. Summary on What You've Learned.mp4 39.75MB
1. Summary on What You've Learned.srt 5.21KB
1. Techniques for Working with Traditional Data.mp4 138.30MB
1. Techniques for Working with Traditional Data.srt 10.62KB
1. The Basic Probability Formula.mp4 85.91MB
1. The Basic Probability Formula.srt 8.90KB
1. The IF Statement.mp4 10.81MB
1. The IF Statement.srt 3.53KB
1. The Linear Regression Model.mp4 57.37MB
1. The Linear Regression Model.srt 7.06KB
1. The Reason Behind These Disciplines.mp4 81.19MB
1. The Reason Behind These Disciplines.srt 6.50KB
1. Types of Clustering.mp4 44.58MB
1. Types of Clustering.srt 4.66KB
1. Types of Data.mp4 72.52MB
1. Types of Data.srt 5.95KB
1. Using Arithmetic Operators in Python.mp4 18.93MB
1. Using Arithmetic Operators in Python.srt 4.11KB
1. Using the .format() Method.mp4 47.64MB
1. Using the .format() Method.srt 12.34KB
1. Variables.mp4 14.09MB
1. Variables.srt 4.53KB
1. What are Confidence Intervals.mp4 49.99MB
1. What are Confidence Intervals.srt 3.26KB
1. What are Data, Servers, Clients, Requests, and Responses.mp4 69.03MB
1. What are Data, Servers, Clients, Requests, and Responses.srt 5.93KB
1. What is a Layer.mp4 12.51MB
1. What is a Layer.srt 2.39KB
1. What is a Matrix.mp4 33.59MB
1. What is a Matrix.srt 4.34KB
1. What is Initialization.mp4 21.76MB
1. What is Initialization.srt 3.50KB
1. What is Overfitting.mp4 31.08MB
1. What is Overfitting.srt 5.58KB
1. What is sklearn and How is it Different from Other Packages.mp4 27.26MB
1. What is sklearn and How is it Different from Other Packages.srt 3.42KB
1. What to Expect from the Following Sections.html 2.48KB
1. What to Expect from this Part.mp4 31.10MB
1. What to Expect from this Part.srt 4.63KB
10.1 1.02. Multiple linear regression.csv 1.07KB
10.1 2.4.Numerical-variables.Frequency-distribution-table-exercise-solution.xlsx 13.15KB
10.1 Adding and subtracting matrices.ipynb 3.22KB
10.1 Binary predictors.ipynb 2.41KB
10.1 Indexing Elements - Exercise_Py3.ipynb 1.35KB
10.1 Online p-value calculator.pdf 1.15MB
10.1 TensorFlow_Minimal_Example_Exercise_1_Solution.ipynb 23.63KB
10.1 TensorFlow_MNIST_Exercises_All.ipynb 15.47KB
10.1 TensorFlow_MNIST_Part6_with_comments.ipynb 12.54KB
10.2 2.02. Binary predictors.csv 2.56KB
10.2 Indexing Elements - Lecture_Py3.ipynb 1.32KB
10.2 sklearn - Feature Selection with F-regression.ipynb 10.44KB
10.2 TensorFlow_Minimal_Example_Exercise_2_3_Solution.ipynb 49.96KB
10.3 Indexing Elements - Solution_Py3.ipynb 2.17KB
10.3 sklearn - Feature Selection with F-regression_with_comments.ipynb 12.99KB
10.3 TensorFlow_Minimal_Example_All_Exercises.ipynb 13.97KB
10.4 TensorFlow_Minimal_Example_Exercise_4_Solution.ipynb 26.98KB
10.5 TensorFlow_Minimal_Example_Exercise_3_Solution.ipynb 26.71KB
10.6 TensorFlow_Minimal_Example_Exercise_2_2_Solution.ipynb 25.51KB
10.7 TensorFlow_Minimal_Example_Exercise_2_1_Solution.ipynb 25.54KB
10.8 TensorFlow_Minimal_Example_Exercise_2_4_Solution.ipynb 21.75KB
10. A1 Linearity.html 165B
10. A Breakdown of our Data Science Infographic.html 165B
10. Addition and Subtraction of Matrices.mp4 32.62MB
10. Addition and Subtraction of Matrices.srt 4.04KB
10. Analyzing the Reasons for Absence.mp4 40.58MB
10. Analyzing the Reasons for Absence.srt 5.85KB
10. Basic NN Example with TF Exercises.html 1.59KB
10. Binary Predictors in a Logistic Regression.mp4 38.43MB
10. Binary Predictors in a Logistic Regression.srt 5.41KB
10. Business Case Testing the Model.mp4 11.20MB
10. Business Case Testing the Model.srt 2.71KB
10. Central Limit Theorem.html 165B
10. Discrete Distributions The Bernoulli Distribution.html 165B
10. Feature Selection (F-regression).mp4 29.51MB
10. Feature Selection (F-regression).srt 6.67KB
10. Indexing Elements.mp4 5.93MB
10. Indexing Elements.srt 1.70KB
10. Interpreting the Coefficients of the Logistic Regression.mp4 40.40MB
10. Interpreting the Coefficients of the Logistic Regression.srt 7.25KB
10. Jupyter's Interface.html 165B
10. Margin of Error.mp4 47.24MB
10. Margin of Error.srt 6.15KB
10. MNIST Exercises.html 2.13KB
10. MNIST Learning.mp4 40.96MB
10. MNIST Learning.srt 7.94KB
10. Mutually Exclusive Sets.html 165B
10. Numerical Variables Exercise.html 81B
10. p-value.mp4 55.87MB
10. p-value.srt 5.03KB
10. Relationship between Clustering and Regression.mp4 9.93MB
10. Relationship between Clustering and Regression.srt 2.18KB
10. Setting an Early Stopping Mechanism - Exercise.html 192B
10. Software Integration - Explained.html 165B
10. Solving Variations without Repetition.html 165B
10. Techniques for Working with Traditional Methods.mp4 111.65MB
10. Techniques for Working with Traditional Methods.srt 10.98KB
10. The Linear Model with Multiple Inputs.html 165B
10. Using Seaborn for Graphs.mp4 12.24MB
10. Using Seaborn for Graphs.srt 1.48KB
11.10 8. TensorFlow_MNIST_Learning_rate_Part_1_Solution.ipynb 20.58KB
11.10 9. TensorFlow_MNIST_Learning_rate_Part_2_Solution.ipynb 15.21KB
11.11 3. TensorFlow_MNIST_Width_and_Depth_Solution.ipynb 15.30KB
11.11 7. TensorFlow_MNIST_Batch_size_Part_2_Solution.ipynb 14.16KB
11.1 2.5. The Histogram_lesson.xlsx 18.63KB
11.1 6. TensorFlow_MNIST_Batch_size_Part_1_Solution.ipynb 15.12KB
11.1 8. TensorFlow_MNIST_Learning_rate_Part_1_Solution.ipynb 14.07KB
11.1 Bank_data.csv 19.55KB
11.1 Combinations With Repetition.pdf 207.41KB
11.1 Logistic Regression prior to Backward Elimination.html 226B
11.1 Market segmentation example_with_comments.ipynb 5.90KB
11.1 sklearn - How to properly include p-values.ipynb 12.71KB
11.1 TensorFlow_Audiobooks_Machine_learning_Homework.ipynb 14.40KB
11.1 TensorFlow_Audiobooks_Machine_Learning_with_comments.ipynb 11.95KB
11.2 1.02. Multiple linear regression.csv 1.07KB
11.2 1. TensorFlow_MNIST_Width_Solution.ipynb 14.84KB
11.2 2. TensorFlow_MNIST_Depth_Solution.ipynb 14.87KB
11.2 3.12. Example.csv 283B
11.2 Binary Predictors in a Logistic Regression - Exercise.ipynb 2.54KB
11.2 TensorFlow_Audiobooks_Preprocessing_with_comments.ipynb 11.19KB
11.3 0. TensorFlow_MNIST_take_note_of_time_Solution.ipynb 14.00KB
11.3 5. TensorFlow_MNIST_Activation_functions_Part_2_Solution.ipynb 14.74KB
11.3 Audiobooks_data.csv 710.77KB
11.3 Binary Predictors in a Logistic Regression - Solution.ipynb 4.51KB
11.3 Market segmentation example.ipynb 3.80KB
11.4 5. TensorFlow_MNIST_Activation_functions_Part_2_Solution.ipynb 13.93KB
11.4 9. TensorFlow_MNIST_Learning_rate_Part_2_Solution.ipynb 15.80KB
11.5 6. TensorFlow_MNIST_Batch_size_Part_1_Solution.ipynb 14.26KB
11.5 TensorFlow_MNIST_around_98_percent_accuracy.ipynb 15.02KB
11.6 4. TensorFlow_MNIST_Activation_functions_Part_1_Solution.ipynb 14.35KB
11.6 7. TensorFlow_MNIST_Batch_size_Part_2_Solution.ipynb 15.18KB
11.7 1. TensorFlow_MNIST_Width_Solution.ipynb 14.01KB
11.7 TensorFlow_MNIST_All_Exercises.ipynb 16.65KB
11.8 2. TensorFlow_MNIST_Depth_Solution.ipynb 15.31KB
11.8 TensorFlow_MNIST_around_98_percent_accuracy.ipynb 17.66KB
11.9 3. TensorFlow_MNIST_Width_and_Depth_Solution.ipynb 16.81KB
11.9 4. TensorFlow_MNIST_Activation_functions_Part_1_Solution.ipynb 15.11KB
11. A2 No Endogeneity.mp4 35.68MB
11. A2 No Endogeneity.srt 5.24KB
11. Addition and Subtraction of Matrices.html 165B
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. Binary Predictors in a Logistic Regression - Exercise.html 87B
11. Business Case A Comment on the Homework.mp4 36.38MB
11. Business Case A Comment on the Homework.srt 5.30KB
11. Business Case Testing the Model.mp4 10.79MB
11. Business Case Testing the Model.srt 2.04KB
11. Dependence and Independence of Sets.mp4 34.79MB
11. Dependence and Independence of Sets.srt 3.46KB
11. Discrete Distributions The Binomial Distribution.mp4 68.83MB
11. Discrete Distributions The Binomial Distribution.srt 8.30KB
11. How to Interpret the Regression Table.mp4 44.64MB
11. How to Interpret the Regression Table.srt 6.30KB
11. Indexing Elements.html 165B
11. Margin of Error.html 165B
11. Market Segmentation with Cluster Analysis (Part 1).mp4 43.01MB
11. Market Segmentation with Cluster Analysis (Part 1).srt 7.53KB
11. MNIST - Exercises.html 1.98KB
11. MNIST Solutions.html 2.19KB
11. Obtaining Dummies from a Single Feature.mp4 81.12MB
11. Obtaining Dummies from a Single Feature.srt 10.20KB
11. p-value.html 165B
11. Solving Combinations.mp4 57.34MB
11. Solving Combinations.srt 5.61KB
11. Standard error.mp4 22.77MB
11. Standard error.srt 2.02KB
11. Techniques for Working with Traditional Methods.html 165B
11. The Histogram.mp4 13.78MB
11. The Histogram.srt 3.01KB
11. The Linear model with Multiple Inputs and Multiple Outputs.mp4 38.31MB
11. The Linear model with Multiple Inputs and Multiple Outputs.srt 5.46KB
12.1 1.02. Multiple linear regression.csv 1.07KB
12.1 3.13. Confidence intervals. Two means. Dependent samples_lesson.xlsx 10.47KB
12.1 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx 14.54KB
12.1 Accuracy_with_comments.ipynb 11.67KB
12.1 Errors when adding scalars, vectors, and matrices in Python.ipynb 3.17KB
12.1 Market segmentation example_Part2.ipynb 4.68KB
12.1 Structure Your Code with Indentation - Solution_Py3.ipynb 1.50KB
12.1 TensorFlow_Audiobooks_Machine_learning_Homework.ipynb 14.40KB
12.1 TensorFlow_Audiobooks_Machine_Learning_with_comments.ipynb 11.95KB
12.1 TensorFlow_MNIST_complete.ipynb 6.78KB
12.2 Accuracy.ipynb 3.63KB
12.2 Audiobooks_data.csv 710.77KB
12.2 Market segmentation example_Part2_with_comments.ipynb 6.81KB
12.2 sklearn - Multiple Linear Regression Summary Table.ipynb 13.71KB
12.2 Structure Your Code with Indentation - Lecture_Py3.ipynb 958B
12.2 TensorFlow_MNIST_complete_with_comments.ipynb 14.51KB
12.3 sklearn - Multiple Linear Regression Summary Table_with_comments.ipynb 16.63KB
12.3 Structure Your Code with Indentation - Exercise_Py3.ipynb 956B
12.3 TensorFlow_Audiobooks_Preprocessing_with_comments.ipynb 11.19KB
12. A2 No Endogeneity.html 165B
12. Business Case Final Exercise.html 433B
12. Business Case Final Exercise.html 439B
12. Calculating the Accuracy of the Model.mp4 32.86MB
12. Calculating the Accuracy of the Model.srt 4.13KB
12. Confidence intervals. Two means. Dependent samples.mp4 70.47MB
12. Confidence intervals. Two means. Dependent samples.srt 8.03KB
12. Creating a Summary Table with P-values.mp4 12.31MB
12. Creating a Summary Table with P-values.srt 3.01KB
12. Dependence and Independence of Sets.html 165B
12. Discrete Distributions The Binomial Distribution.html 165B
12. Errors when Adding Matrices.mp4 11.18MB
12. Errors when Adding Matrices.srt 2.57KB
12. EXERCISE - Obtaining Dummies from a Single Feature.html 129B
12. How to Interpret the Regression Table.html 165B
12. Market Segmentation with Cluster Analysis (Part 2).mp4 56.11MB
12. Market Segmentation with Cluster Analysis (Part 2).srt 56.14MB
12. MNIST Testing the Model.mp4 29.52MB
12. MNIST Testing the Model.srt 6.02KB
12. Real Life Examples of Traditional Methods.mp4 42.78MB
12. Real Life Examples of Traditional Methods.srt 3.58KB
12. Solving Combinations.html 165B
12. Standard Error.html 165B
12. Structuring with Indentation.mp4 5.47MB
12. Structuring with Indentation.srt 2.18KB
12. Test for the Mean. Population Variance Unknown.mp4 40.24MB
12. Test for the Mean. Population Variance Unknown.srt 5.73KB
12. Testing the Model We Created.mp4 49.07MB
12. Testing the Model We Created.srt 6.50KB
12. The Histogram.html 165B
12. The Linear model with Multiple Inputs and Multiple Outputs.html 165B
13.1 3.13. Confidence intervals. Two means. Dependent samples_exercise.xlsx 13.74KB
13.1 4.6.Test-for-the-mean.Population-variance-unknown-exercise.xlsx 11.34KB
13.1 Calculating the Accuracy of the Model - Exercise.ipynb 5.39KB
13.1 Poisson - Expected Value and Variance.pdf 145.99KB
13.1 real_estate_price_size_year.csv 2.35KB
13.1 Statistics - PDF with Excel Solutions that don't visualize properly.pdf 289.12KB
13.1 Symmetry Explained.pdf 85.04KB
13.1 Tranpose of a matrix.ipynb 2.89KB
13.2 2.5.The-Histogram-exercise-solution.xlsx 17.10KB
13.2 3.13. Confidence intervals. Two means. Dependent samples_exercise_solution.xlsx 14.24KB
13.2 4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx 12.63KB
13.2 Calculating the Accuracy of the Model - Solution.ipynb 81.21KB
13.2 sklearn - Multiple Linear Regression Exercise.ipynb 5.67KB
13.3 2.5.The-Histogram-exercise.xlsx 15.50KB
13.3 Bank_data.csv 19.55KB
13.3 sklearn - Multiple Linear Regression Exercise Solution.ipynb 15.44KB
13. A3 Normality and Homoscedasticity.mp4 42.70MB
13. A3 Normality and Homoscedasticity.srt 6.67KB
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. Discrete Distributions The Poisson Distribution.mp4 55.75MB
13. Discrete Distributions The Poisson Distribution.srt 6.57KB
13. Estimators and Estimates.mp4 47.83MB
13. Estimators and Estimates.srt 3.71KB
13. Graphical Representation of Simple Neural Networks.mp4 22.65MB
13. Graphical Representation of Simple Neural Networks.srt 2.69KB
13. Histogram Exercise.html 81B
13. How is Clustering Useful.mp4 74.45MB
13. How is Clustering Useful.srt 6.39KB
13. Machine Learning (ML) Techniques.mp4 99.32MB
13. Machine Learning (ML) Techniques.srt 8.73KB
13. Multiple Linear Regression - Exercise.html 76B
13. Saving the Model and Preparing it for Deployment.mp4 37.45MB
13. Saving the Model and Preparing it for Deployment.srt 5.57KB
13. SOLUTION - Obtaining Dummies from a Single Feature.html 116B
13. Structuring with Indentation.html 165B
13. Symmetry of Combinations.mp4 40.30MB
13. Symmetry of Combinations.srt 4.30KB
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 4.93KB
13. Transpose of a Matrix.mp4 38.07MB
13. Transpose of a Matrix.srt 5.37KB
14.1 2.6. Cross table and scatter plot.xlsx 26.12KB
14.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_lesson.xlsx 9.83KB
14.1 4.7. Test for the mean. Dependent samples_lesson.xlsx 9.79KB
14.1 Dot product.ipynb 2.13KB
14.1 SKLEAR~1.IPY 12.87KB
14.1 Species Segmentation with Cluster Analysis Part 1- Solution.ipynb 7.35KB
14.2 1.02. Multiple linear regression.csv 1.07KB
14.2 Species Segmentation with Cluster Analysis Part 1- Exercise.ipynb 4.46KB
14.3 iris_dataset.csv 2.40KB
14.3 sklearn - Feature Selection through Feature Scaling (Standardization) - Part 1.ipynb 11.73KB
14. A4 No Autocorrelation.mp4 31.51MB
14. A4 No Autocorrelation.srt 4.90KB
14. ARTICLE - A Note on 'pickling'.html 2.14KB
14. Confidence intervals. Two means. Independent Samples (Part 1).mp4 28.76MB
14. Confidence intervals. Two means. Independent Samples (Part 1).srt 6.07KB
14. Cross Tables and Scatter Plots.mp4 39.80MB
14. Cross Tables and Scatter Plots.srt 6.68KB
14. Decomposition of Variability.html 165B
14. Discrete Distributions The Poisson Distribution.html 165B
14. Dot Product.mp4 24.00MB
14. Dot Product.srt 4.26KB
14. Dropping a Dummy Variable from the Data Set.html 2.34KB
14. Estimators and Estimates.html 165B
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. Graphical Representation of Simple Neural Networks.html 165B
14. Machine Learning (ML) Techniques.html 165B
14. Symmetry of Combinations.html 165B
14. Test for the Mean. Dependent Samples.mp4 50.37MB
14. Test for the Mean. Dependent Samples.srt 6.26KB
14. The Conditional Probability Formula.html 165B
14. Underfitting and Overfitting.mp4 22.30MB
14. Underfitting and Overfitting.srt 4.97KB
15.1 1.02. Multiple linear regression.csv 1.07KB
15.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise.xlsx 9.83KB
15.1 4.7. Test for the mean. Dependent samples_exercise.xlsx 12.80KB
15.1 Dot product (Part 2).ipynb 3.60KB
15.1 Logistic Regression with Comments.html 210B
15.1 Solving Integrals.pdf 343.85KB
15.1 Species Segmentation with Cluster Analysis Part 2 - Exercise.ipynb 10.74KB
15.1 Testing the model.ipynb 5.77KB
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15.2 4.7. Test for the mean. Dependent samples_exercise_solution.xlsx 14.40KB
15.2 Logistic Regression.html 196B
15.2 SKLEAR~1.IPY 16.79KB
15.2 Species Segmentation with Cluster Analysis Part 2 - Solution.ipynb 15.30KB
15.2 Testing the model_with_comments.ipynb 7.56KB
15.3 2.03. Test dataset.csv 322B
15.3 iris_dataset.csv 2.40KB
15.3 sklearn - Feature Selection through Feature Scaling (Standardization) - Part 2.ipynb 14.89KB
15.4 iris_with_answers.csv 3.63KB
15. A4 No autocorrelation.html 165B
15. Characteristics of Continuous Distributions.mp4 84.12MB
15. Characteristics of Continuous Distributions.srt 8.66KB
15. Confidence intervals. Two means. Independent Samples (Part 1). Exercise.html 81B
15. Cross Tables and Scatter Plots.html 165B
15. Dot Product of Matrices.mp4 49.43MB
15. Dot Product of Matrices.srt 9.52KB
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 34.90MB
15. Feature Selection through Standardization of Weights.srt 7.26KB
15. More on Dummy Variables A Statistical Perspective.mp4 13.75MB
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15. Solving Combinations with Separate Sample Spaces.mp4 33.16MB
15. Solving Combinations with Separate Sample Spaces.srt 3.73KB
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. The Law of Total Probability.mp4 34.94MB
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15. Types of Machine Learning.mp4 125.14MB
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15. What is the Objective Function.mp4 17.92MB
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15. What is the OLS.mp4 28.31MB
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16.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_lesson.xlsx 9.52KB
16.1 4.8. Test for the mean. Independent samples (Part 1)_lesson.xlsx 9.63KB
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16.2 1.02. Multiple linear regression.csv 1.07KB
16.2 2.6. Cross table and scatter plot_exercise_solution.xlsx 40.44KB
16.2 Testing the Model - Solution.ipynb 111.10KB
16.3 sklearn - Making Predictions with the Standardized Coefficients_with_comments.ipynb 22.03KB
16.3 Testing the Model - Exercise..ipynb 6.79KB
16.4 Bank_data_testing.csv 8.30KB
16. A5 No Multicollinearity.mp4 28.71MB
16. A5 No Multicollinearity.srt 4.62KB
16. Characteristics of Continuous Distributions.html 165B
16. Classifying the Various Reasons for Absence.mp4 74.60MB
16. Classifying the Various Reasons for Absence.srt 10.02KB
16. Confidence intervals. Two means. Independent Samples (Part 2).mp4 26.83MB
16. Confidence intervals. Two means. Independent Samples (Part 2).srt 4.51KB
16. Cross Tables and Scatter Plots Exercise.html 81B
16. Predicting with the Standardized Coefficients.mp4 25.96MB
16. Predicting with the Standardized Coefficients.srt 5.59KB
16. Preparing the Deployment of the Model through a Module.mp4 44.49MB
16. Preparing the Deployment of the Model through a Module.srt 5.62KB
16. Solving Combinations with Separate Sample Spaces.html 165B
16. Test for the mean. Independent Samples (Part 1).mp4 33.95MB
16. Test for the mean. Independent Samples (Part 1).srt 5.49KB
16. Testing the Model - Exercise.html 87B
16. The Additive Rule.mp4 26.97MB
16. The Additive Rule.srt 2.74KB
16. Types of Machine Learning.html 165B
16. What is the Objective Function.html 165B
16. What is the OLS.html 165B
16. Why is Linear Algebra Useful.mp4 144.33MB
16. Why is Linear Algebra Useful.srt 11.79KB
17.1 2.7. Mean, median and mode_lesson.xlsx 10.49KB
17.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise_solution.xlsx 9.79KB
17.1 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise-solution.xlsx 11.25KB
17.1 Normal Distribution - Exp and Var.pdf 144.08KB
17.1 sklearn - Feature Scaling Exercise Solution.ipynb 16.28KB
17.2 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise.xlsx 9.17KB
17.2 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise.xlsx 10.77KB
17.2 real_estate_price_size_year.csv 2.35KB
17.3 sklearn - Feature Scaling Exercise.ipynb 6.07KB
17. A5 No Multicollinearity.html 165B
17. Combinatorics in Real-Life The Lottery.mp4 41.30MB
17. Combinatorics in Real-Life The Lottery.srt 4.15KB
17. Common Objective Functions L2-norm Loss.mp4 23.28MB
17. Common Objective Functions L2-norm Loss.srt 2.77KB
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 4.77KB
17. Feature Scaling (Standardization) - Exercise.html 76B
17. Mean, median and mode.mp4 37.12MB
17. Mean, median and mode.srt 5.73KB
17. Real Life Examples of Machine Learning (ML).mp4 36.81MB
17. Real Life Examples of Machine Learning (ML).srt 2.90KB
17. R-Squared.mp4 41.03MB
17. R-Squared.srt 6.57KB
17. Test for the mean. Independent Samples (Part 1). Exercise.html 81B
17. The Additive Rule.html 165B
17. Using .concat() in Python.mp4 38.74MB
17. Using .concat() in Python.srt 5.07KB
18.1 2.7. Mean, median and mode_exercise_solution.xlsx 11.35KB
18.1 4.9. Test for the mean. Independent samples (Part 2)_lesson.xlsx 9.31KB
18.1 Dummy Variables.ipynb 4.62KB
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18.2 Dummy variables_with_comments.ipynb 7.09KB
18.3 1.03. Dummies.csv 1.19KB
18. Combinatorics in Real-Life The Lottery.html 165B
18. Common Objective Functions L2-norm Loss.html 165B
18. Confidence intervals. Two means. Independent Samples (Part 3).mp4 19.94MB
18. Confidence intervals. Two means. Independent Samples (Part 3).srt 1.96KB
18. Continuous Distributions The Normal Distribution.html 165B
18. Dealing with Categorical Data - Dummy Variables.mp4 55.66MB
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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 165B
18. R-Squared.html 165B
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. The Multiplication Law.mp4 49.03MB
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18. Underfitting and Overfitting.mp4 16.96MB
18. Underfitting and Overfitting.srt 3.45KB
19.1 2.8. Skewness_lesson.xlsx 34.63KB
19.1 real_estate_price_size_year_view.csv 3.39KB
19.1 sklearn - Train Test Split.ipynb 7.23KB
19.2 Multiple Linear Regression with Dummies Exercise Solution.ipynb 18.00KB
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19.3 Multiple Linear Regression with Dummies Exercise.ipynb 3.01KB
19. A Recap of Combinatorics.mp4 38.50MB
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19. Common Objective Functions Cross-Entropy Loss.mp4 37.25MB
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19. Continuous Distributions The Standard Normal Distribution.mp4 47.90MB
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19. Dealing with Categorical Data - Dummy Variables.html 76B
19. Skewness.mp4 19.40MB
19. Skewness.srt 3.64KB
19. SOLUTION - Using .concat() in Python.html 142B
19. Test for the mean. Independent Samples (Part 2).html 165B
19. The Multiplication Law.html 165B
19. Train - Test Split Explained.mp4 49.17MB
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2.1 2.13.Practical-example.Descriptive-statistics-exercise.xlsx 120.27KB
2.1 3.01. Country clusters.csv 200B
2.1 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx 1.82MB
2.1 3.2. What is a distribution_lesson.xlsx 19.46KB
2.1 4.10.+Hypothesis+testing+section_practical+example_exercise.xlsx 43.69KB
2.1 Absenteeism_predictions.csv 2.10KB
2.1 Course_Notes_Cluster_Analysis.pdf 208.65KB
2.1 Course_Notes_Logistic_Regression.pdf 335.17KB
2.1 Course Notes - Section 6.pdf 936.42KB
2.1 Creating a Function with a Parameter - Solution_Py3.ipynb 1.79KB
2.1 Minimal_example_Part_2.ipynb 3.65KB
2.1 sklearn - Linear Regression - Practical Example (Part 2)_with_comments.ipynb 335.63KB
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2.2 4.10.Hypothesis-testing-section-practical-example-exercise-solution.xlsx 44.27KB
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2. Analyzing Age vs Probability in Tableau.mp4 56.55MB
2. Analyzing Age vs Probability in Tableau.srt 10.01KB
2. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.html 165B
2. A Simple Example in Python.mp4 34.70MB
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2. A Simple Example of Clustering.mp4 51.83MB
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2. Basic NN Example (Part 2).mp4 34.95MB
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2. Business Case Outlining the Solution.mp4 7.30MB
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2. Comparison Operators.html 165B
2. Creating the Targets for the Logistic Regression.mp4 45.80MB
2. Creating the Targets for the Logistic Regression.srt 8.39KB
2. Data Science and Business Buzzwords Why are there so Many.html 165B
2. Debunking Common Misconceptions.html 165B
2. Dendrogram.mp4 29.06MB
2. Dendrogram.srt 7.36KB
2. Deploying the 'absenteeism_module' - Part I.mp4 25.49MB
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2. Finding the Job - What to Expect and What to Look for.html 165B
2. For Loops.html 165B
2. Fundamentals of Combinatorics.html 165B
2. Fundamentals of Probability Distributions.html 165B
2. Further Reading on Null and Alternative Hypothesis.html 2.29KB
2. How are we Going to Approach this Section.mp4 19.41MB
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2. How to Create a Function with a Parameter.mp4 18.08MB
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2. How to Install TensorFlow 1.mp4 11.36MB
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2. Importing the Absenteeism Data in Python.mp4 23.16MB
2. Importing the Absenteeism Data in Python.srt 3.99KB
2. Introduction to Neural Networks.html 165B
2. Introduction to Programming.html 165B
2. Introduction to Regression Analysis.html 165B
2. Iterating Over Range Objects.mp4 22.49MB
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2. Lists.html 165B
2. MNIST How to Tackle the MNIST.mp4 18.66MB
2. MNIST How to Tackle the MNIST.mp4 22.59MB
2. MNIST How to Tackle the MNIST.srt 3.52KB
2. MNIST How to Tackle the MNIST.srt 3.62KB
2. Multiple Linear Regression.html 165B
2. Necessary Programming Languages and Software Used in Data Science.html 165B
2. Object Oriented Programming.html 165B
2. Population and Sample.html 165B
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. Probability in Statistics.mp4 77.28MB
2. Probability in Statistics.srt 8.44KB
2. Problems with Gradient Descent.mp4 11.02MB
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2. Sets and Events.html 165B
2. Some Examples of Clusters.mp4 71.53MB
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2. Techniques for Working with Traditional Data.html 165B
2. TensorFlow Outline and Comparison with Other Libraries.mp4 33.52MB
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2. The Basic Probability Formula.html 165B
2. The Business Task.mp4 39.16MB
2. The Business Task.srt 3.74KB
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2. The Linear Regression Model.html 165B
2. The Reason Behind These Disciplines.html 165B
2. Types of Basic Preprocessing.mp4 11.85MB
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2. Types of Data.html 165B
2. Types of Simple Initializations.mp4 14.31MB
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2. Underfitting and Overfitting for Classification.mp4 25.08MB
2. Underfitting and Overfitting for Classification.srt 2.63KB
2. Using Arithmetic Operators in Python.html 165B
2. Variables.html 165B
2. What's Further out there in terms of Machine Learning.mp4 20.12MB
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2. What are Confidence Intervals.html 165B
2. What are Data, Servers, Clients, Requests, and Responses.html 165B
2. What Does the Course Cover.mp4 62.25MB
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2. What is a Deep Net.mp4 29.54MB
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2. What is a Distribution.mp4 61.59MB
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2. What is a Matrix.html 165B
20.1 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2.xlsx 10.54KB
20.1 Additional Exercises Combinatorics Solutions.pdf 245.67KB
20.1 Making predictions.ipynb 5.77KB
20.2 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2-solution.xlsx 11.39KB
20.2 Additional Exercises Combinatorics.pdf 106.58KB
20.2 Making predictions_with_comments.ipynb 9.41KB
20. A Practical Example of Combinatorics.mp4 134.31MB
20. A Practical Example of Combinatorics.srt 13.96KB
20. Bayes' Law.mp4 49.93MB
20. Bayes' Law.srt 7.20KB
20. Common Objective Functions Cross-Entropy Loss.html 165B
20. Continuous Distributions The Standard Normal Distribution.html 165B
20. Making Predictions with the Linear Regression.mp4 24.70MB
20. Making Predictions with the Linear Regression.srt 4.44KB
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. Skewness.html 165B
20. Test for the mean. Independent Samples (Part 2). Exercise.html 81B
21.1 2.8. Skewness_exercise.xlsx 9.49KB
21.1 GD-function-example.xlsx 42.33KB
21.2 2.8. Skewness_exercise_solution.xlsx 19.78KB
21. Bayes' Law.html 165B
21. Continuous Distributions The Students' T Distribution.mp4 27.18MB
21. Continuous Distributions The Students' T Distribution.srt 2.79KB
21. EXERCISE - Reordering Columns in a Pandas DataFrame in Python.html 167B
21. Optimization Algorithm 1-Parameter Gradient Descent.mp4 55.63MB
21. Optimization Algorithm 1-Parameter Gradient Descent.srt 8.47KB
21. Skewness Exercise.html 81B
22.1 2.9. Variance_lesson.xlsx 10.08KB
22.1 CDS_2017-2018 Hamilton.pdf 845.31KB
22.2 Bayesian Homework - Solutions.pdf 30.35KB
22.3 Bayesian Homework .pdf 27.26KB
22. A Practical Example of Bayesian Inference.mp4 145.12MB
22. A Practical Example of Bayesian Inference.srt 19.32KB
22. Continuous Distributions The Students' T Distribution.html 165B
22. Optimization Algorithm 1-Parameter Gradient Descent.html 165B
22. SOLUTION - Reordering Columns in a Pandas DataFrame in Python.html 471B
22. Variance.mp4 50.95MB
22. Variance.srt 7.53KB
23.1 2.9. Variance_exercise.xlsx 10.83KB
23.1 Absenteeism Exercise - Preprocessing - df_reason_mod.ipynb 4.82KB
23.2 2.9. Variance_exercise_solution.xlsx 11.05KB
23. Continuous Distributions The Chi-Squared Distribution.mp4 26.34MB
23. Continuous Distributions The Chi-Squared Distribution.srt 2.76KB
23. Creating Checkpoints while Coding in Jupyter.mp4 25.68MB
23. Creating Checkpoints while Coding in Jupyter.srt 3.64KB
23. Optimization Algorithm n-Parameter Gradient Descent.mp4 39.43MB
23. Optimization Algorithm n-Parameter Gradient Descent.srt 7.53KB
23. Variance Exercise.html 522B
24.1 2.10. Standard deviation and coefficient of variation_lesson.xlsx 10.97KB
24. Continuous Distributions The Chi-Squared Distribution.html 165B
24. EXERCISE - Creating Checkpoints while Coding in Jupyter.html 137B
24. Optimization Algorithm n-Parameter Gradient Descent.html 165B
24. Standard Deviation and Coefficient of Variation.mp4 45.13MB
24. Standard Deviation and Coefficient of Variation.srt 6.60KB
25. Continuous Distributions The Exponential Distribution.mp4 40.23MB
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25. SOLUTION - Creating Checkpoints while Coding in Jupyter.html 117B
25. Standard Deviation.html 165B
26.1 2.10.Standard-deviation-and-coefficient-of-variation-exercise.xlsx 11.61KB
26.2 2.10.Standard-deviation-and-coefficient-of-variation-exercise-solution.xlsx 12.60KB
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. Continuous Distributions The Exponential Distribution.html 165B
26. Standard Deviation and Coefficient of Variation Exercise.html 81B
27.1 2.11. Covariance_lesson.xlsx 24.92KB
27. Continuous Distributions The Logistic Distribution.mp4 47.05MB
27. Continuous Distributions The Logistic Distribution.srt 5.02KB
27. Covariance.mp4 27.49MB
27. Covariance.srt 4.92KB
27. Extracting the Month Value from the Date Column.mp4 47.79MB
27. Extracting the Month Value from the Date Column.srt 7.97KB
28. Continuous Distributions The Logistic Distribution.html 165B
28. Covariance.html 165B
28. Extracting the Day of the Week from the Date Column.mp4 27.97MB
28. Extracting the Day of the Week from the Date Column.srt 4.47KB
29.1 2.11. Covariance_exercise.xlsx 20.23KB
29.1 Absenteeism Exercise - Preprocessing LECTURES.ipynb 7.61MB
29.1 FIFA19.csv 8.64MB
29.2 2.11. Covariance_exercise_solution.xlsx 29.51KB
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29.2 Daily Views.xlsx 9.53KB
29.3 Absenteeism Exercise - Removing the Date Column - SOLUTION.ipynb 8.33KB
29.3 Daily Views (post).xlsx 20.21KB
29.4 FIFA19 (post).csv 8.64MB
29.5 Customers_Membership.xlsx 9.69KB
29.6 Customers_Membership (post).xlsx 15.62KB
29. A Practical Example of Probability Distributions.mp4 157.82MB
29. A Practical Example of Probability Distributions.srt 19.91KB
29. Covariance Exercise.html 81B
29. EXERCISE - Removing the Date Column.html 1.21KB
3.1 1.02. Multiple linear regression.csv 1.09KB
3.1 12.3. TensorFlow_MNIST_with_comments_Part_1.ipynb 3.89KB
3.1 3.9. Population variance known, z-score_lesson.xlsx 11.21KB
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3.1 Another Way to Define a Function - Solution_Py3.ipynb 1.98KB
3.1 A Simple Example of Clustering - Exercise.ipynb 3.62KB
3.1 Audiobooks-data.csv 710.77KB
3.1 Course Notes - Section 2.pdf 578.08KB
3.1 FAQ_The_Data_Science_Course.pdf 306.10KB
3.1 Heatmaps.ipynb 1.82KB
3.1 Help Yourself with Methods - Exercise_Py3.ipynb 1.91KB
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3.1 Minimal_example_Part_3.ipynb 6.79KB
3.1 Numbers and Boolean Values - Lecture_Py3.ipynb 3.36KB
3.1 Probability Cheat Sheet.pdf 320.28KB
3.1 sklearn - Simple Linear Regression_with_comments.ipynb 6.06KB
3.1 TensorFlow_MNIST_Part1_with_comments.ipynb 3.97KB
3.1 The Double Equality Sign - Exercise_Py3.ipynb 838B
3.1 While Loops and Incrementing - Exercise_Py3.ipynb 1.12KB
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3.2 Countries-exercise.csv 8.27KB
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3.2 Heatmaps_with_comments.ipynb 17.66KB
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3.3 A Simple Example of Clustering - Solution.ipynb 4.65KB
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3. A Note on Multicollinearity.html 849B
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 48.83MB
3. Business Case Balancing the Dataset.mp4 30.43MB
3. Business Case Balancing the Dataset.srt 4.50KB
3. Checking the Content of the Data Set.mp4 61.91MB
3. Checking the Content of the Data Set.srt 7.04KB
3. Computing Expected Values.mp4 75.69MB
3. Computing Expected Values.srt 6.68KB
3. Confidence Intervals; Population Variance Known; Z-score.mp4 78.20MB
3. Confidence Intervals; Population Variance Known; Z-score.srt 9.80KB
3. Correlation vs Regression.mp4 14.73MB
3. Correlation vs Regression.srt 2.10KB
3. DeepMind and Deep Learning.html 1.05KB
3. Defining a Function in Python - Part II.mp4 11.14MB
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3. Deploying the 'absenteeism_module' - Part II.mp4 54.25MB
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3. Difference between Classification and Clustering.mp4 36.16MB
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3. Digging into a Deep Net.mp4 59.36MB
3. Digging into a Deep Net.srt 6.70KB
3. Download All Resources and Important FAQ.html 21.37KB
3. EXERCISE - Reasons vs Probability.html 397B
3. Heatmaps.mp4 29.62MB
3. Heatmaps.srt 6.34KB
3. Introducing the Data Set.mp4 40.87MB
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3. Introduction to Nested For Loops.mp4 29.47MB
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3. Levels of Measurement.mp4 54.38MB
3. Levels of Measurement.srt 4.54KB
3. Logical and Identity Operators.mp4 30.05MB
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3. Logistic vs Logit Function.mp4 86.50MB
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3. MNIST Relevant Packages.mp4 18.91MB
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3. Modules and Packages.mp4 8.51MB
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3. Momentum.mp4 16.43MB
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3. Null vs Alternative Hypothesis.html 165B
3. Numbers and Boolean Values in Python.mp4 17.06MB
3. Numbers and Boolean Values in Python.srt 3.68KB
3. Permutations and How to Use Them.mp4 42.73MB
3. Permutations and How to Use Them.srt 4.07KB
3. Probability in Data Science.mp4 63.49MB
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3. Real Life Examples of Traditional Data.mp4 29.94MB
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3. Scalars and Vectors.mp4 33.85MB
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3. Selecting the Inputs for the Logistic Regression.mp4 16.76MB
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3. State-of-the-Art Method - (Xavier) Glorot Initialization.mp4 17.15MB
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3. TensorFlow 1 vs TensorFlow 2.mp4 22.00MB
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3. The Double Equality Sign.mp4 5.99MB
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3. The Importance of Working with a Balanced Dataset.mp4 39.41MB
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3. Training the Model.mp4 28.72MB
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3. Types of Probability Distributions.mp4 71.06MB
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3. Using Methods.mp4 37.60MB
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3. Ways Sets Can Interact.mp4 47.43MB
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3. What are Data Connectivity, APIs, and Endpoints.mp4 104.08MB
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3. What is a Distribution.html 165B
3. What is the difference between Analysis and Analytics.mp4 53.55MB
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3. What is Validation.mp4 32.71MB
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3. While Loops and Incrementing.mp4 28.44MB
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3. Why Python.mp4 75.07MB
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30. Correlation Coefficient.mp4 29.38MB
30. Correlation Coefficient.srt 4.71KB
31. Correlation.html 165B
31. Working on Education, Children, and Pets.mp4 39.60MB
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32.1 2.12. Correlation_exercise_solution.xlsx 29.48KB
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32.2 Absenteeism Exercise - EXERCISES and SOLUTIONS.ipynb 4.13KB
32. Correlation Coefficient Exercise.html 81B
32. Final Remarks of this Section.mp4 21.64MB
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33. A Note on Exporting Your Data as a .csv File.html 883B
4.1 0.6.4 Using a Function in another Function - Solution_Py3.ipynb 1.60KB
4.1 1.01. Simple linear regression.csv 922B
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4.2 TensorFlow_Audiobooks_Preprocessing_with_comments.ipynb 11.19KB
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4.3 sklearn - Linear Regression - Practical Example (Part 3).html 134B
4.3 sklearn - Simple Linear Regression_with_comments.ipynb 28.35KB
4.3 TensorFlow_Audiobooks_Preprocessing.ipynb 5.58KB
4. Adjusted R-Squared.html 165B
4. Analyzing Reasons vs Probability in Tableau.mp4 59.33MB
4. Analyzing Reasons vs Probability in Tableau.srt 9.54KB
4. A Note on TensorFlow 2 Syntax.mp4 6.76MB
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4. An overview of CNNs.mp4 58.79MB
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4. Basic NN Example (Part 4).mp4 61.13MB
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4. Building a Logistic Regression.mp4 17.11MB
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4. Business Case Preprocessing.mp4 103.41MB
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4. Business Case Preprocessing the Data.mp4 84.33MB
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4. Clustering Categorical Data.mp4 21.23MB
4. Clustering Categorical Data.srt 3.24KB
4. Computing Expected Values.html 165B
4. Confidence Intervals; Population Variance Known; Z-score; Exercise.html 81B
4. Correlation vs Regression.html 165B
4. Exporting the Obtained Data Set as a .csv.html 998B
4. How to Use a Function within a Function.mp4 8.14MB
4. How to Use a Function within a Function.srt 2.03KB
4. Introducing the Data Set.html 165B
4. Introduction to Terms with Multiple Meanings.mp4 27.86MB
4. Introduction to Terms with Multiple Meanings.srt 4.05KB
4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp4 29.09MB
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4. Levels of Measurement.html 165B
4. Lists with the range() Function.mp4 25.80MB
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4. Logical and Identity Operators.html 165B
4. Math Prerequisites.mp4 14.56MB
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4. MNIST Model Outline.mp4 56.38MB
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4. MNIST Preprocess the Data - Create a Validation Set and Scale It.srt 6.27KB
4. Modules and Packages.html 165B
4. Non-Linearities and their Purpose.mp4 27.69MB
4. Non-Linearities and their Purpose.srt 3.88KB
4. Numbers and Boolean Values in Python.html 165B
4. Permutations and How to Use Them.html 165B
4. Practical Example Linear Regression (Part 3).mp4 23.69MB
4. Practical Example Linear Regression (Part 3).srt 4.12KB
4. Preprocessing Categorical Data.mp4 18.60MB
4. Preprocessing Categorical Data.srt 2.76KB
4. Rejection Region and Significance Level.mp4 82.61MB
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4. Scalars and Vectors.html 165B
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 6.69KB
4. Standardizing the Data.mp4 20.60MB
4. Standardizing the Data.srt 4.19KB
4. Techniques for Working with Big Data.mp4 75.50MB
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4. TensorFlow Intro.mp4 47.69MB
4. TensorFlow Intro.srt 5.20KB
4. The Double Equality Sign.html 165B
4. The ELIF Statement.mp4 25.08MB
4. The ELIF Statement.srt 6.60KB
4. The Normal Distribution.mp4 49.86MB
4. The Normal Distribution.srt 4.90KB
4. Training, Validation, and Test Datasets.mp4 25.19MB
4. Training, Validation, and Test Datasets.srt 3.60KB
4. Training the Model.html 165B
4. Triple Nested For Loops.mp4 46.59MB
4. Triple Nested For Loops.srt 8.00KB
4. Types of Probability Distributions.html 165B
4. Using Methods.html 165B
4. Ways Sets Can Interact.html 165B
4. What are Data Connectivity, APIs, and Endpoints.html 165B
4. What is the difference between Analysis and Analytics.html 165B
4. Why Python.html 165B
5.10 Minimal_example_Exercise_6_Solution.ipynb 61.76KB
5.1 1.04. Real-life example.csv 219.83KB
5.1 12.5. TensorFlow_MNIST_with_comments_Part_3.ipynb 7.31KB
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5.1 Building a Logistic Regression - Solution.ipynb 4.44KB
5.1 Categorical.csv 10.34KB
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5.1 List Slicing - Lecture_Py3.ipynb 5.02KB
5.1 Minimal_example_Exercise_1_Solution.ipynb 69.00KB
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5.1 Reassign Values - Solution_Py3.ipynb 2.12KB
5.1 Shortcuts-for-Jupyter.pdf 619.17KB
5.1 Strings - Solution_Py3.ipynb 5.45KB
5.1 TensorFlow_Audiobooks_Preprocessing_Exercise_Solution.ipynb 10.03KB
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5.2 Minimal_example_All_Exercises.ipynb 12.89KB
5.2 real_estate_price_size_year.csv 2.35KB
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5.2 sklearn - Dummies and VIF - Exercise Solution.ipynb 370.22KB
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5.3 Minimal_example_Exercise_5_Solution.ipynb 68.88KB
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5.3 Reassign Values - Lecture_Py3.ipynb 3.08KB
5.3 sklearn - Dummies and VIF - Exercise.ipynb 344.62KB
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5.8 Minimal_example_Exercise_3.c. Solution.ipynb 70.13KB
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5. Activation Functions.mp4 25.09MB
5. Activation Functions.srt 5.25KB
5. Actual Introduction to TensorFlow.mp4 17.41MB
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5. A Note on Boolean Values.mp4 8.90MB
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5. A Note on Normalization.html 733B
5. An Overview of RNNs.mp4 25.26MB
5. An Overview of RNNs.srt 3.71KB
5. Basic NN Example Exercises.html 1.66KB
5. Binary and One-Hot Encoding.mp4 28.94MB
5. Binary and One-Hot Encoding.srt 4.81KB
5. Building a Logistic Regression - Exercise.html 87B
5. Business Analytics, Data Analytics, and Data Science An Introduction.mp4 64.51MB
5. Business Analytics, Data Analytics, and Data Science An Introduction.srt 10.63KB
5. Business Case Preprocessing Exercise.html 383B
5. Business Case Preprocessing the Data - Exercise.html 370B
5. Categorical Variables - Visualization Techniques.mp4 36.64MB
5. Categorical Variables - Visualization Techniques.srt 6.30KB
5. Characteristics of Discrete Distributions.mp4 22.71MB
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5. Clustering Categorical Data - Exercise.html 87B
5. Conditional Statements and Functions.mp4 15.68MB
5. Conditional Statements and Functions.srt 3.51KB
5. Confidence Interval Clarifications.mp4 57.03MB
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5. Dummies and Variance Inflation Factor - Exercise.html 76B
5. EXERCISE - Transportation Expense vs Probability.html 553B
5. Frequency.mp4 61.73MB
5. Frequency.srt 6.42KB
5. Geometrical Representation of the Linear Regression Model.mp4 5.13MB
5. Geometrical Representation of the Linear Regression Model.srt 1.64KB
5. How to Reassign Values.mp4 4.01MB
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5. Intersection of Sets.mp4 26.97MB
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5. Learning Rate Schedules Visualized.mp4 9.11MB
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5. Linear Algebra and Geometry.mp4 49.79MB
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5. List Comprehensions.mp4 55.45MB
5. List Comprehensions.srt 12.35KB
5. List Slicing.mp4 30.76MB
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5. Lists with the range() Function.html 165B
5. MNIST Loss and Optimization Algorithm.mp4 25.86MB
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5. MNIST Preprocess the Data - Scale the Test Data - Exercise.html 79B
5. Multiple Linear Regression Exercise.html 76B
5. N-Fold Cross Validation.mp4 20.70MB
5. N-Fold Cross Validation.srt 4.17KB
5. Python Strings.mp4 24.16MB
5. Python Strings.srt 7.10KB
5. Rejection Region and Significance Level.html 165B
5. Simple Operations with Factorials.mp4 36.12MB
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5. Splitting the Data for Training and Testing.mp4 52.77MB
5. Splitting the Data for Training and Testing.srt 8.10KB
5. Taking a Closer Look at APIs.mp4 115.59MB
5. Taking a Closer Look at APIs.srt 10.39KB
5. Techniques for Working with Big Data.html 165B
5. The Normal Distribution.html 165B
5. Types of File Formats Supporting TensorFlow.mp4 16.40MB
5. Types of File Formats Supporting TensorFlow.srt 3.50KB
5. Types of Machine Learning.mp4 45.10MB
5. Types of Machine Learning.srt 36.14MB
5. What's Regression Analysis - a Quick Refresher.html 2.84KB
5. What is the Standard Library.mp4 18.04MB
5. What is the Standard Library.srt 3.56KB
5. Why Jupyter.mp4 44.31MB
5. Why Jupyter.srt 4.64KB
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6. Activation Functions Softmax Activation.mp4 25.92MB
6. Activation Functions Softmax Activation.srt 4.46KB
6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp4 26.36MB
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6. Analyzing Transportation Expense vs Probability in Tableau.mp4 40.64MB
6. Analyzing Transportation Expense vs Probability in Tableau.srt 7.21KB
6. An Invaluable Coding Tip.mp4 23.05MB
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6. Anonymous (Lambda) Functions.mp4 38.54MB
6. Anonymous (Lambda) Functions.srt 9.88KB
6. A Note on Boolean Values.html 165B
6. An Overview of non-NN Approaches.mp4 44.77MB
6. An Overview of non-NN Approaches.srt 5.12KB
6. Business Analytics, Data Analytics, and Data Science An Introduction.html 165B
6. Business Case Load the Preprocessed Data.mp4 17.57MB
6. Business Case Load the Preprocessed Data.srt 4.70KB
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6. Calculating the Accuracy of the Model.srt 5.19KB
6. Categorical Variables - Visualization Techniques.html 165B
6. Characteristics of Discrete Distributions.html 165B
6. Conditional Statements and Loops.mp4 27.76MB
6. Conditional Statements and Loops.srt 7.46KB
6. Creating a Data Provider.mp4 76.34MB
6. Creating a Data Provider.srt 7.75KB
6. Early Stopping or When to Stop Training.mp4 24.18MB
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6. Fitting the Model and Assessing its Accuracy.mp4 41.62MB
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6. Frequency.html 165B
6. Functions Containing a Few Arguments.mp4 6.02MB
6. Functions Containing a Few Arguments.srt 1.33KB
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6. How to Choose the Number of Clusters.mp4 44.14MB
6. How to Choose the Number of Clusters.srt 7.37KB
6. How to Reassign Values.html 165B
6. Intersection of Sets.html 165B
6. Linear Algebra and Geometry.html 165B
6. MNIST Preprocess the Data - Shuffle and Batch.mp4 41.52MB
6. MNIST Preprocess the Data - Shuffle and Batch.srt 9.26KB
6. Outlining the Model with TensorFlow 2.mp4 34.69MB
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6. Practical Example Linear Regression (Part 4).mp4 56.04MB
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6. Python Strings.html 165B
6. Real Life Examples of Big Data.mp4 22.03MB
6. Real Life Examples of Big Data.srt 1.88KB
6. Simple Linear Regression with sklearn - Exercise.html 76B
6. Simple Operations with Factorials.html 165B
6. Student's T Distribution.mp4 35.43MB
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6. Taking a Closer Look at APIs.html 165B
6. Test for Significance of the Model (F-Test).mp4 16.43MB
6. Test for Significance of the Model (F-Test).srt 2.55KB
6. The Standard Normal Distribution.mp4 22.50MB
6. The Standard Normal Distribution.srt 3.94KB
6. Tuples.mp4 29.49MB
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6. Types of Machine Learning.html 165B
6. Using a Statistical Approach towards the Solution to the Exercise.mp4 20.18MB
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6. What is the Standard Library.html 165B
6. Why Jupyter.html 165B
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7.1 TensorFlow_MNIST_Part3_with_comments.ipynb 8.61KB
7.2 2.3. Categorical variables. Visualization techniques_exercise_solution.xlsx 41.11KB
7.2 365_DataScience_Diagram.pdf 323.08KB
7.2 All In - Solution_Py3.ipynb 1.90KB
7.2 Countries_exercise.csv 8.27KB
7.2 Dictionaries - Exercise_Py3.ipynb 2.92KB
7.2 Notable Built-In Functions in Python - Exercise_Py3.ipynb 3.66KB
7.2 sklearn - Multiple Linear Regression_with_comments.ipynb 8.65KB
7.2 TensorFlow_Audiobooks_Outlining_the_model.ipynb 9.36KB
7.3 All In - Lecture_Py3.ipynb 1.62KB
7.3 Dictionaries - Lecture_Py3.ipynb 4.35KB
7.3 How to Choose the Number of Clusters - Solution.ipynb 8.49KB
7.3 Notable Built-In Functions in Python - Lecture_Py3.ipynb 4.51KB
7.3 sklearn - Multiple Linear Regression.ipynb 7.79KB
7.3 Statistics - PDF with Excel Solutions that don't visualize properly.pdf 289.12KB
7. Adam (Adaptive Moment Estimation).mp4 22.35MB
7. Adam (Adaptive Moment Estimation).srt 3.33KB
7. Add Comments.mp4 4.68MB
7. Add Comments.srt 1.80KB
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. Backpropagation.mp4 34.95MB
7. Backpropagation.srt 4.46KB
7. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4 38.49MB
7. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.srt 7.36KB
7. Built-in Functions in Python.mp4 22.02MB
7. Built-in Functions in Python.srt 4.21KB
7. Business Case Load the Preprocessed Data - Exercise.html 79B
7. Business Case Model Outline.mp4 53.12MB
7. Business Case Model Outline.srt 6.94KB
7. Business Intelligence (BI) Techniques.mp4 89.94MB
7. Business Intelligence (BI) Techniques.srt 8.63KB
7. Categorical Variables Exercise.html 81B
7. Communication between Software Products through Text Files.mp4 60.35MB
7. Communication between Software Products through Text Files.srt 5.47KB
7. Conditional Statements, Functions, and Loops.mp4 9.49MB
7. Conditional Statements, Functions, and Loops.srt 2.41KB
7. Continuing with BI, ML, and AI.mp4 108.98MB
7. Continuing with BI, ML, and AI.srt 11.87KB
7. Creating a Summary Table with the Coefficients and Intercept.mp4 38.88MB
7. Creating a Summary Table with the Coefficients and Intercept.srt 6.62KB
7. Dictionaries.mp4 41.68MB
7. Dictionaries.srt 8.43KB
7. Discrete Distributions The Uniform Distribution.mp4 24.40MB
7. Discrete Distributions The Uniform Distribution.srt 2.73KB
7. Dropping a Column from a DataFrame in Python.mp4 61.77MB
7. Dropping a Column from a DataFrame in Python.srt 7.81KB
7. Dummy Variables - Exercise.html 713B
7. Events and Their Complements.mp4 59.15MB
7. Events and Their Complements.srt 6.71KB
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 4.81KB
7. Installing Python and Jupyter.mp4 50.99MB
7. Installing Python and Jupyter.srt 8.84KB
7. Interpreting the Result and Extracting the Weights and Bias.mp4 30.27MB
7. Interpreting the Result and Extracting the Weights and Bias.srt 6.23KB
7. MNIST Batching and Early Stopping.mp4 12.85MB
7. MNIST Batching and Early Stopping.srt 2.92KB
7. MNIST Preprocess the Data - Shuffle and Batch - Exercise.html 79B
7. Multiple Linear Regression with sklearn.mp4 20.08MB
7. Multiple Linear Regression with sklearn.srt 4.17KB
7. OLS Assumptions.mp4 21.85MB
7. OLS Assumptions.srt 3.03KB
7. Python Packages Installation.mp4 40.58MB
7. Python Packages Installation.srt 5.62KB
7. Solving Variations with Repetition.mp4 34.00MB
7. Solving Variations with Repetition.srt 3.47KB
7. Student's T Distribution.html 165B
7. The Linear Model (Linear Algebraic Version).mp4 28.44MB
7. The Linear Model (Linear Algebraic Version).srt 3.88KB
7. The Standard Normal Distribution.html 165B
7. Type I Error and Type II Error.html 165B
7. Understanding Logistic Regression Tables.mp4 30.55MB
7. Understanding Logistic Regression Tables.srt 5.56KB
7. Union of Sets.mp4 57.19MB
7. Union of Sets.srt 5.53KB
8.1 12.8. TensorFlow_MNIST_with_comments_Part_6.ipynb 11.50KB
8.1 2.4. Numerical variables. Frequency distribution table_lesson.xlsx 11.44KB
8.1 3.11. Population variance unknown, t-score_lesson.xlsx 10.78KB
8.1 3.4.Standard-normal-distribution-exercise.xlsx 11.99KB
8.1 4.4. Test for the mean. Population variance known_lesson.xlsx 10.96KB
8.1 5.5. TensorFlow_Minimal_example_Part_3.ipynb 8.65KB
8.1 Iterating over Dictionaries - Lecture_Py3.ipynb 1.08KB
8.1 Simple linear regression_with_comments.ipynb 4.06KB
8.1 sklearn - Linear Regression - Practical Example (Part 5)_with_comments.ipynb 711.05KB
8.1 sklearn - Multiple Linear Regression and Adjusted R-squared.ipynb 9.11KB
8.1 TensorFlow_Audiobooks_Machine_Learning_Part2_with_comments.ipynb 19.69KB
8.1 TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments.ipynb 12.73KB
8.1 TensorFlow_Minimal_example_complete_with_comments.ipynb 82.29KB
8.1 TensorFlow_MNIST_Part4_with_comments.ipynb 10.49KB
8.1 Tensors.ipynb 2.08KB
8.1 Understanding Logistic Regression Tables - Solution.ipynb 4.79KB
8.2 1.04. Real-life example.csv 219.83KB
8.2 3.11. The t-table.xlsx 15.85KB
8.2 3.4.Standard-normal-distribution-exercise-solution.xlsx 24.04KB
8.2 Bank_data.csv 19.55KB
8.2 Iterating over Dictionaries - Exercise_Py3.ipynb 2.19KB
8.2 Simple linear regression.ipynb 3.79KB
8.2 sklearn - Multiple Linear Regression and Adjusted R-squared_with_comments.ipynb 10.41KB
8.2 TensorFlow_Audiobooks_optimizing_the_algorithm.ipynb 10.64KB
8.2 TensorFlow_Minimal_example_complete.ipynb 76.85KB
8.3 1.01. Simple linear regression.csv 922B
8.3 1.02. Multiple linear regression.csv 1.07KB
8.3 Iterating over Dictionaries - Solution_Py3.ipynb 2.87KB
8.3 sklearn - Linear Regression - Practical Example (Part 5).ipynb 698.36KB
8.3 Understanding Logistic Regression Tables - Exercise.ipynb 3.16KB
8. Add Comments.html 165B
8. Backpropagation Picture.mp4 19.51MB
8. Backpropagation Picture.srt 3.97KB
8. Basic NN Example with TF Loss Function and Gradient Descent.mp4 32.51MB
8. Basic NN Example with TF Loss Function and Gradient Descent.srt 4.83KB
8. Business Case Learning and Interpreting the Result.mp4 31.18MB
8. Business Case Learning and Interpreting the Result.srt 6.27KB
8. Business Case Optimization.mp4 41.52MB
8. Business Case Optimization.srt 6.60KB
8. Business Intelligence (BI) Techniques.html 165B
8. Calculating the Adjusted R-Squared in sklearn.mp4 30.88MB
8. Calculating the Adjusted R-Squared in sklearn.srt 6.27KB
8. Communication between Software Products through Text Files.html 165B
8. Confidence Intervals; Population Variance Unknown; T-score.mp4 32.20MB
8. Confidence Intervals; Population Variance Unknown; T-score.srt 5.71KB
8. Continuing with BI, ML, and AI.html 165B
8. Customizing a TensorFlow 2 Model.mp4 22.91MB
8. Customizing a TensorFlow 2 Model.srt 4.11KB
8. Dictionaries.html 165B
8. Discrete Distributions The Uniform Distribution.html 165B
8. Events and Their Complements.html 165B
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. How to Iterate over Dictionaries.mp4 29.65MB
8. How to Iterate over Dictionaries.srt 7.93KB
8. Importing Modules in Python.html 165B
8. Interpreting the Coefficients for Our Problem.mp4 52.37MB
8. Interpreting the Coefficients for Our Problem.srt 7.89KB
8. MNIST Learning.mp4 46.68MB
8. MNIST Learning.srt 10.19KB
8. MNIST Outline the Model.mp4 28.23MB
8. MNIST Outline the Model.srt 7.20KB
8. Numerical Variables - Frequency Distribution Table.mp4 25.85MB
8. Numerical Variables - Frequency Distribution Table.srt 4.36KB
8. OLS Assumptions.html 165B
8. Practical Example Linear Regression (Part 5).mp4 57.88MB
8. Practical Example Linear Regression (Part 5).srt 10.59KB
8. Pros and Cons of K-Means Clustering.mp4 37.71MB
8. Pros and Cons of K-Means Clustering.srt 4.61KB
8. Python Functions.html 165B
8. Solving Variations with Repetition.html 165B
8. Test for the Mean. Population Variance Known.mp4 54.22MB
8. Test for the Mean. Population Variance Known.srt 8.14KB
8. The Linear Model.html 165B
8. The Standard Normal Distribution Exercise.html 81B
8. Understanding Jupyter's Interface - the Notebook Dashboard.mp4 13.80MB
8. Understanding Jupyter's Interface - the Notebook Dashboard.srt 3.73KB
8. Understanding Logistic Regression Tables - Exercise.html 87B
8. Union of Sets.html 165B
8. What is a Tensor.mp4 22.53MB
8. What is a Tensor.srt 3.61KB
9.1 1.02. Multiple linear regression.csv 1.07KB
9.1 12.9. TensorFlow_MNIST_with_comments.ipynb 13.03KB
9.1 3.11. Population variance unknown, t-score_exercise.xlsx 10.62KB
9.1 365_DataScience.png 6.93MB
9.1 4.4. Test for the mean. Population variance known_exercise.xlsx 11.03KB
9.1 5.6. TensorFlow_Minimal_example_complete.ipynb 12.15KB
9.1 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf 182.38KB
9.1 Line Continuation - Lecture_Py3.ipynb 779B
9.1 Logistic Regression prior to Custom Scaler.html 219B
9.1 real_estate_price_size.csv 1.86KB
9.1 TensorFlow_Audiobooks_Machine_Learning_Part3_with_comments.ipynb 10.06KB
9.1 TensorFlow_Audiobooks_optimizing_the_algorithm.ipynb 10.64KB
9.1 TensorFlow_Minimal_example_All_exercises.ipynb 83.62KB
9.1 TensorFlow_MNIST_Part5_with_comments.ipynb 10.99KB
9.2 3.11. Population variance unknown, t-score_exercise_solution.xlsx 11.10KB
9.2 4.4. Test for the mean. Population variance known_exercise_solution.xlsx 11.22KB
9.2 Line Continuation - Solution_Py3.ipynb 1.50KB
9.2 Simple Linear Regression Exercise Solution.ipynb 3.57KB
9.2 sklearn - Multiple Linear Regression and Adjusted R-squared - Exercise.ipynb 9.83KB
9.2 TensorFlow_Audiobooks_optimizing_the_algorithm_with_comments.ipynb 12.73KB
9.2 TensorFlow_Minimal_example_Exercise_1_Solution.ipynb 27.96KB
9.3 3.11.The-t-table.xlsx 15.85KB
9.3 Line Continuation - Exercise_Py3.ipynb 1.14KB
9.3 Simple Linear Regression Exercise.ipynb 2.78KB
9.3 sklearn - Multiple Linear Regression and Adjusted R-squared - Exercise Solution.ipynb 10.31KB
9.3 TensorFlow_Minimal_Example_Exercise_2_1_Solution.ipynb 83.68KB
9.4 TensorFlow_Minimal_Example_Exercise_2_2_Solution.ipynb 77.52KB
9.5 TensorFlow_Minimal_Example_Exercise_3_Solution.ipynb 84.44KB
9. A1 Linearity.mp4 12.61MB
9. A1 Linearity.srt 2.36KB
9. A Breakdown of our Data Science Infographic.mp4 67.74MB
9. A Breakdown of our Data Science Infographic.srt 5.10KB
9. Backpropagation - A Peek into the Mathematics of Optimization.html 539B
9. Basic NN Example with TF Model Output.mp4 37.39MB
9. Basic NN Example with TF Model Output.srt 7.93KB
9. Basic NN with TensorFlow Exercises.html 1.29KB
9. Business Case Interpretation.mp4 25.75MB
9. Business Case Interpretation.srt 2.94KB
9. Business Case Setting an Early Stopping Mechanism.mp4 49.81MB
9. Business Case Setting an Early Stopping Mechanism.srt 7.82KB
9. Calculating the Adjusted R-Squared in sklearn - Exercise.html 76B
9. Central Limit Theorem.mp4 62.89MB
9. Central Limit Theorem.srt 5.63KB
9. Confidence Intervals; Population Variance Unknown; T-score; Exercise.html 81B
9. Discrete Distributions The Bernoulli Distribution.mp4 34.13MB
9. Discrete Distributions The Bernoulli Distribution.srt 3.85KB
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 Select the Loss and the Optimizer.mp4 13.90MB
9. MNIST Select the Loss and the Optimizer.srt 3.02KB
9. Mutually Exclusive Sets.mp4 25.40MB
9. Mutually Exclusive Sets.srt 2.52KB
9. Numerical Variables - Frequency Distribution Table.html 165B
9. Prerequisites for Coding in the Jupyter Notebooks.mp4 30.58MB
9. Prerequisites for Coding in the Jupyter Notebooks.srt 7.79KB
9. Real Life Examples of Business Intelligence (BI).mp4 29.54MB
9. Real Life Examples of Business Intelligence (BI).srt 2.13KB
9. Software Integration - Explained.mp4 63.70MB
9. Software Integration - Explained.srt 6.71KB
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. Standardizing only the Numerical Variables (Creating a Custom Scaler).mp4 41.19MB
9. Standardizing only the Numerical Variables (Creating a Custom Scaler).srt 5.03KB
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 3.09KB
9. To Standardize or not to Standardize.mp4 30.11MB
9. To Standardize or not to Standardize.srt 5.88KB
9. Understanding Line Continuation.mp4 2.35MB
9. Understanding Line Continuation.srt 1.13KB
9. What do the Odds Actually Mean.mp4 32.28MB
9. What do the Odds Actually Mean.srt 4.79KB
9. What is a Tensor.html 165B
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