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1.1 1.04. Real-life example.csv |
219.83KB |
1.1 2.13. Practical example. Descriptive statistics_lesson.xlsx |
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1.1 3.17. Practical example. Confidence intervals_lesson.xlsx |
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1.1 4.10.Hypothesis-testing-section-practical-example.xlsx |
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1.1 Absenteeism_predictions.csv |
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1.1 Absenteeism_preprocessed.csv |
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1.1 Additional-Python-Tools-Solutions.ipynb |
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1.1 Arithmetic Operators - Exercise_Py3.ipynb |
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1.1 Audiobooks_data.csv |
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1.1 Audiobooks_data.csv |
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1.1 Comparison Operators - Solution_Py3.ipynb |
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1.1 Course_Notes_Cluster_Analysis.pdf |
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1.1 Course_Notes_Logistic_Regression.pdf |
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1.1 Course notes_descriptive_statistics.pdf |
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1.1 Course notes_hypothesis_testing.pdf |
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1.1 Course notes_inferential statistics.pdf |
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1.1 Course notes_regression_analysis.pdf |
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1.1 Course notes_regression_analysis.pdf |
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1.1 Course Notes - Basic Probability.pdf |
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1.1 Course Notes - Bayesian Inference.pdf |
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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 |
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1.1 data_preprocessing_homework.pdf |
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1.1 Defining a Function in Python - Lecture_Py3.ipynb |
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1.1 For Loops - Solution_Py3.ipynb |
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1.1 Introduction to the If Statement - Lecture_Py3.ipynb |
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1.1 Lists - Solution_Py3.ipynb |
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1.1 Minimal_example_Part_1.ipynb |
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1.1 model.original |
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1.1 Probability in Finance Homework.pdf |
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1.1 Statistics Glossary.xlsx |
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1.1 Variables - Lecture_Py3.ipynb |
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1.2 Absenteeism_data.csv |
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1.2 Additional-Python-Tools-Exercises.ipynb |
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1.2 Arithmetic Operators - Lecture_Py3.ipynb |
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1.2 Course notes_descriptive_statistics.pdf |
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1.2 For Loops - Exercise_Py3.ipynb |
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1.2 Glossary.xlsx |
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1.2 Introduction to the If Statement - Solution_Py3.ipynb |
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1.2 Lists - Exercise_Py3.ipynb |
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1.2 Probability in Finance Solutions.pdf |
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1.2 scaler.original |
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1.2 Shortcuts-for-Jupyter.pdf |
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1.2 sklearn - Linear Regression - Practical Example (Part 1)_with_comments.ipynb |
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1.2 Variables - Exercise_Py3.ipynb |
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1.3 absenteeism_module.py |
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1.3 Additional-Python-Tools-Lectures.ipynb |
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1.3 Arithmetic Operators - Solution_Py3.ipynb |
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1.3 Comparison Operators - Exercise_Py3.ipynb |
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1.3 df_preprocessed.csv |
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1.3 For Loops - Lecture_Py3.ipynb |
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1.3 Introduction to the If Statement - Exercise_Py3.ipynb |
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1.3 Lists - Lecture_Py3.ipynb |
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1.3 Python Introduction - Course Notes.pdf |
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1.3 sklearn - Linear Regression - Practical Example (Part 1).ipynb |
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1.4 Absenteeism Exercise - Integration.ipynb |
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1.4 Variables - Solution_Py3.ipynb |
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1.5 Absenteeism_new_data.csv |
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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 |
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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 |
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1. Are You Sure You're All Set.html |
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1. Basic NN Example (Part 1).mp4 |
20.60MB |
1. Basic NN Example (Part 1).srt |
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1. Bonus Lecture Next Steps.html |
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1. Business Case Exploring the Dataset and Identifying Predictors.mp4 |
66.27MB |
1. Business Case Exploring the Dataset and Identifying Predictors.srt |
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1. Business Case Getting Acquainted with the Dataset.mp4 |
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1. Business Case Getting Acquainted with the Dataset.srt |
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1. Comparison Operators.mp4 |
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1. Comparison Operators.srt |
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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 |
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1. Debunking Common Misconceptions.mp4 |
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1. Debunking Common Misconceptions.srt |
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1. Defining a Function in Python.mp4 |
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1. Defining a Function in Python.srt |
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1. EXERCISE - Age vs Probability.html |
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1. Exploring the Problem with a Machine Learning Mindset.mp4 |
27.55MB |
1. Exploring the Problem with a Machine Learning Mindset.srt |
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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 |
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1. For Loops.mp4 |
23.60MB |
1. For Loops.srt |
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1. Fundamentals of Combinatorics.mp4 |
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1. Fundamentals of Combinatorics.srt |
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1. Fundamentals of Probability Distributions.mp4 |
73.40MB |
1. Fundamentals of Probability Distributions.srt |
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1. Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 |
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1. Game Plan for this Python, SQL, and Tableau Business Exercise.srt |
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1. How to Install TensorFlow 2.0.mp4 |
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1. How to Install TensorFlow 2.0.srt |
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1. Introduction.mp4 |
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1. Introduction.srt |
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1. Introduction to Cluster Analysis.mp4 |
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1. Introduction to Cluster Analysis.srt |
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1. Introduction to Logistic Regression.mp4 |
27.07MB |
1. Introduction to Logistic Regression.srt |
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1. Introduction to Neural Networks.mp4 |
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1. Introduction to Neural Networks.srt |
42.94MB |
1. Introduction to Programming.mp4 |
58.54MB |
1. Introduction to Programming.srt |
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1. Introduction to Regression Analysis.mp4 |
17.32MB |
1. Introduction to Regression Analysis.srt |
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1. K-Means Clustering.mp4 |
27.29MB |
1. K-Means Clustering.srt |
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1. Lists.mp4 |
37.79MB |
1. Lists.srt |
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1. MNIST The Dataset.mp4 |
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1. MNIST The Dataset.srt |
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1. MNIST What is the MNIST Dataset.mp4 |
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1. MNIST What is the MNIST Dataset.srt |
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1. Multiple Linear Regression.mp4 |
21.53MB |
1. Multiple Linear Regression.srt |
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1. Necessary Programming Languages and Software Used in Data Science.mp4 |
103.52MB |
1. Necessary Programming Languages and Software Used in Data Science.srt |
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1. Null vs Alternative Hypothesis.mp4 |
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1. Null vs Alternative Hypothesis.srt |
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1. Object Oriented Programming.mp4 |
33.59MB |
1. Object Oriented Programming.srt |
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1. Population and Sample.mp4 |
58.11MB |
1. Population and Sample.srt |
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1. Practical Example Descriptive Statistics.mp4 |
160.46MB |
1. Practical Example Descriptive Statistics.srt |
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1. Practical Example Hypothesis Testing.mp4 |
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1. Practical Example Hypothesis Testing.srt |
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1. Practical Example Inferential Statistics.mp4 |
102.66MB |
1. Practical Example Inferential Statistics.srt |
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1. Practical Example Linear Regression (Part 1).mp4 |
97.08MB |
1. Practical Example Linear Regression (Part 1).srt |
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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 |
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1. Summary on What You've Learned.mp4 |
39.75MB |
1. Summary on What You've Learned.srt |
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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 |
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1. The IF Statement.mp4 |
10.81MB |
1. The IF Statement.srt |
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1. The Linear Regression Model.mp4 |
57.37MB |
1. The Linear Regression Model.srt |
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1. The Reason Behind These Disciplines.mp4 |
81.19MB |
1. The Reason Behind These Disciplines.srt |
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1. Types of Clustering.mp4 |
44.58MB |
1. Types of Clustering.srt |
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1. Types of Data.mp4 |
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1. Types of Data.srt |
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1. Using Arithmetic Operators in Python.mp4 |
18.93MB |
1. Using Arithmetic Operators in Python.srt |
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1. Using the .format() Method.mp4 |
47.64MB |
1. Using the .format() Method.srt |
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1. Variables.mp4 |
14.09MB |
1. Variables.srt |
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1. What are Confidence Intervals.mp4 |
49.99MB |
1. What are Confidence Intervals.srt |
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1. What are Data, Servers, Clients, Requests, and Responses.mp4 |
69.03MB |
1. What are Data, Servers, Clients, Requests, and Responses.srt |
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1. What is a Layer.mp4 |
12.51MB |
1. What is a Layer.srt |
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1. What is a Matrix.mp4 |
33.59MB |
1. What is a Matrix.srt |
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1. What is Initialization.mp4 |
21.76MB |
1. What is Initialization.srt |
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1. What is Overfitting.mp4 |
31.08MB |
1. What is Overfitting.srt |
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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 |
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1. What to Expect from the Following Sections.html |
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1. What to Expect from this Part.mp4 |
31.10MB |
1. What to Expect from this Part.srt |
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10.1 1.02. Multiple linear regression.csv |
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10.1 2.4.Numerical-variables.Frequency-distribution-table-exercise-solution.xlsx |
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10.1 Adding and subtracting matrices.ipynb |
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10.1 Binary predictors.ipynb |
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10.1 Indexing Elements - Exercise_Py3.ipynb |
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10.1 Online p-value calculator.pdf |
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10.1 TensorFlow_Minimal_Example_Exercise_1_Solution.ipynb |
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10.1 TensorFlow_MNIST_Exercises_All.ipynb |
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10.1 TensorFlow_MNIST_Part6_with_comments.ipynb |
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10.2 2.02. Binary predictors.csv |
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10.2 Indexing Elements - Lecture_Py3.ipynb |
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10.2 sklearn - Feature Selection with F-regression.ipynb |
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10.2 TensorFlow_Minimal_Example_Exercise_2_3_Solution.ipynb |
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10.3 Indexing Elements - Solution_Py3.ipynb |
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10.3 sklearn - Feature Selection with F-regression_with_comments.ipynb |
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10.3 TensorFlow_Minimal_Example_All_Exercises.ipynb |
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10.4 TensorFlow_Minimal_Example_Exercise_4_Solution.ipynb |
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10.5 TensorFlow_Minimal_Example_Exercise_3_Solution.ipynb |
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10.6 TensorFlow_Minimal_Example_Exercise_2_2_Solution.ipynb |
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10.7 TensorFlow_Minimal_Example_Exercise_2_1_Solution.ipynb |
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10.8 TensorFlow_Minimal_Example_Exercise_2_4_Solution.ipynb |
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10. A1 Linearity.html |
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10. A Breakdown of our Data Science Infographic.html |
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10. Addition and Subtraction of Matrices.mp4 |
32.62MB |
10. Addition and Subtraction of Matrices.srt |
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10. Analyzing the Reasons for Absence.mp4 |
40.58MB |
10. Analyzing the Reasons for Absence.srt |
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10. Basic NN Example with TF Exercises.html |
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10. Binary Predictors in a Logistic Regression.mp4 |
38.43MB |
10. Binary Predictors in a Logistic Regression.srt |
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10. Business Case Testing the Model.mp4 |
11.20MB |
10. Business Case Testing the Model.srt |
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10. Central Limit Theorem.html |
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10. Discrete Distributions The Bernoulli Distribution.html |
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10. Feature Selection (F-regression).mp4 |
29.51MB |
10. Feature Selection (F-regression).srt |
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10. Indexing Elements.mp4 |
5.93MB |
10. Indexing Elements.srt |
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10. Interpreting the Coefficients of the Logistic Regression.mp4 |
40.40MB |
10. Interpreting the Coefficients of the Logistic Regression.srt |
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10. Jupyter's Interface.html |
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10. Margin of Error.mp4 |
47.24MB |
10. Margin of Error.srt |
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10. MNIST Exercises.html |
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10. MNIST Learning.mp4 |
40.96MB |
10. MNIST Learning.srt |
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10. Mutually Exclusive Sets.html |
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10. Numerical Variables Exercise.html |
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10. p-value.mp4 |
55.87MB |
10. p-value.srt |
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10. Relationship between Clustering and Regression.mp4 |
9.93MB |
10. Relationship between Clustering and Regression.srt |
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10. Setting an Early Stopping Mechanism - Exercise.html |
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10. Software Integration - Explained.html |
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10. Solving Variations without Repetition.html |
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10. Techniques for Working with Traditional Methods.mp4 |
111.65MB |
10. Techniques for Working with Traditional Methods.srt |
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10. The Linear Model with Multiple Inputs.html |
165B |
10. Using Seaborn for Graphs.mp4 |
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10. Using Seaborn for Graphs.srt |
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11.10 8. TensorFlow_MNIST_Learning_rate_Part_1_Solution.ipynb |
20.58KB |
11.10 9. TensorFlow_MNIST_Learning_rate_Part_2_Solution.ipynb |
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11.11 3. TensorFlow_MNIST_Width_and_Depth_Solution.ipynb |
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11.11 7. TensorFlow_MNIST_Batch_size_Part_2_Solution.ipynb |
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11.1 2.5. The Histogram_lesson.xlsx |
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11.1 6. TensorFlow_MNIST_Batch_size_Part_1_Solution.ipynb |
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11.1 8. TensorFlow_MNIST_Learning_rate_Part_1_Solution.ipynb |
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11.1 Bank_data.csv |
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11.1 Combinations With Repetition.pdf |
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11.1 Logistic Regression prior to Backward Elimination.html |
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11.1 Market segmentation example_with_comments.ipynb |
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11.1 sklearn - How to properly include p-values.ipynb |
12.71KB |
11.1 TensorFlow_Audiobooks_Machine_learning_Homework.ipynb |
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11.1 TensorFlow_Audiobooks_Machine_Learning_with_comments.ipynb |
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11.2 1.02. Multiple linear regression.csv |
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11.2 1. TensorFlow_MNIST_Width_Solution.ipynb |
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11.2 2. TensorFlow_MNIST_Depth_Solution.ipynb |
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11.2 3.12. Example.csv |
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11.2 Binary Predictors in a Logistic Regression - Exercise.ipynb |
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11.2 TensorFlow_Audiobooks_Preprocessing_with_comments.ipynb |
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11.3 0. TensorFlow_MNIST_take_note_of_time_Solution.ipynb |
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11.3 5. TensorFlow_MNIST_Activation_functions_Part_2_Solution.ipynb |
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11.3 Audiobooks_data.csv |
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11.3 Binary Predictors in a Logistic Regression - Solution.ipynb |
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11.3 Market segmentation example.ipynb |
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11.4 5. TensorFlow_MNIST_Activation_functions_Part_2_Solution.ipynb |
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11.4 9. TensorFlow_MNIST_Learning_rate_Part_2_Solution.ipynb |
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11.5 6. TensorFlow_MNIST_Batch_size_Part_1_Solution.ipynb |
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11.5 TensorFlow_MNIST_around_98_percent_accuracy.ipynb |
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11.6 4. TensorFlow_MNIST_Activation_functions_Part_1_Solution.ipynb |
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11.6 7. TensorFlow_MNIST_Batch_size_Part_2_Solution.ipynb |
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11.7 1. TensorFlow_MNIST_Width_Solution.ipynb |
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11.7 TensorFlow_MNIST_All_Exercises.ipynb |
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11.8 2. TensorFlow_MNIST_Depth_Solution.ipynb |
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11.8 TensorFlow_MNIST_around_98_percent_accuracy.ipynb |
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11.9 3. TensorFlow_MNIST_Width_and_Depth_Solution.ipynb |
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11.9 4. TensorFlow_MNIST_Activation_functions_Part_1_Solution.ipynb |
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11. A2 No Endogeneity.mp4 |
35.68MB |
11. A2 No Endogeneity.srt |
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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 |
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11. Binary Predictors in a Logistic Regression - Exercise.html |
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11. Business Case A Comment on the Homework.mp4 |
36.38MB |
11. Business Case A Comment on the Homework.srt |
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11. Business Case Testing the Model.mp4 |
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11. Business Case Testing the Model.srt |
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11. Dependence and Independence of Sets.mp4 |
34.79MB |
11. Dependence and Independence of Sets.srt |
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11. Discrete Distributions The Binomial Distribution.mp4 |
68.83MB |
11. Discrete Distributions The Binomial Distribution.srt |
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11. How to Interpret the Regression Table.mp4 |
44.64MB |
11. How to Interpret the Regression Table.srt |
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11. Indexing Elements.html |
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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 |
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11. MNIST - Exercises.html |
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11. MNIST Solutions.html |
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11. Obtaining Dummies from a Single Feature.mp4 |
81.12MB |
11. Obtaining Dummies from a Single Feature.srt |
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11. p-value.html |
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11. Solving Combinations.mp4 |
57.34MB |
11. Solving Combinations.srt |
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11. Standard error.mp4 |
22.77MB |
11. Standard error.srt |
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11. Techniques for Working with Traditional Methods.html |
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11. The Histogram.mp4 |
13.78MB |
11. The Histogram.srt |
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11. The Linear model with Multiple Inputs and Multiple Outputs.mp4 |
38.31MB |
11. The Linear model with Multiple Inputs and Multiple Outputs.srt |
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12.1 1.02. Multiple linear regression.csv |
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12.1 3.13. Confidence intervals. Two means. Dependent samples_lesson.xlsx |
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12.1 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx |
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12.1 Accuracy_with_comments.ipynb |
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12.1 Errors when adding scalars, vectors, and matrices in Python.ipynb |
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12.1 Market segmentation example_Part2.ipynb |
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12.1 Structure Your Code with Indentation - Solution_Py3.ipynb |
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12.1 TensorFlow_Audiobooks_Machine_learning_Homework.ipynb |
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12.1 TensorFlow_Audiobooks_Machine_Learning_with_comments.ipynb |
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12.1 TensorFlow_MNIST_complete.ipynb |
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12.2 Accuracy.ipynb |
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12.2 Audiobooks_data.csv |
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12.2 Market segmentation example_Part2_with_comments.ipynb |
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12.2 sklearn - Multiple Linear Regression Summary Table.ipynb |
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12.2 Structure Your Code with Indentation - Lecture_Py3.ipynb |
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12.2 TensorFlow_MNIST_complete_with_comments.ipynb |
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12.3 sklearn - Multiple Linear Regression Summary Table_with_comments.ipynb |
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12.3 Structure Your Code with Indentation - Exercise_Py3.ipynb |
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12.3 TensorFlow_Audiobooks_Preprocessing_with_comments.ipynb |
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12. A2 No Endogeneity.html |
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12. Business Case Final Exercise.html |
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12. Business Case Final Exercise.html |
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12. Calculating the Accuracy of the Model.mp4 |
32.86MB |
12. Calculating the Accuracy of the Model.srt |
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12. Confidence intervals. Two means. Dependent samples.mp4 |
70.47MB |
12. Confidence intervals. Two means. Dependent samples.srt |
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12. Creating a Summary Table with P-values.mp4 |
12.31MB |
12. Creating a Summary Table with P-values.srt |
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12. Dependence and Independence of Sets.html |
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12. Discrete Distributions The Binomial Distribution.html |
165B |
12. Errors when Adding Matrices.mp4 |
11.18MB |
12. Errors when Adding Matrices.srt |
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12. EXERCISE - Obtaining Dummies from a Single Feature.html |
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12. How to Interpret the Regression Table.html |
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13. Confidence intervals. Two means. Dependent samples Exercise.html |
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17. Combinatorics in Real-Life The Lottery.mp4 |
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2. Introduction to Programming.html |
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2. Necessary Programming Languages and Software Used in Data Science.html |
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3. A Simple Example of Clustering - Exercise.html |
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3. Confidence Intervals; Population Variance Known; Z-score.mp4 |
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3. Download All Resources and Important FAQ.html |
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3. EXERCISE - Reasons vs Probability.html |
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8. How to Iterate over Dictionaries.mp4 |
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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 |