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[UdemyCourseDownloader] The Data Science Course 2018 Complete Data Science Bootcamp
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11. Statistics - Practical Example Descriptive Statistics (Size: 9.20 GB) (Files: 1071)
11. Statistics - Practical Example Descriptive Statistics
1. Practical Example Descriptive Statistics.mp4
1. Practical Example Descriptive Statistics.srt
1. Practical Example Descriptive Statistics.vtt
1.1 2.13. Practical example. Descriptive statistics_lesson.xlsx.xlsx
2. Practical Example Descriptive Statistics Exercise.html
2.1 2.13. Practical-example.Descriptive-statistics-exercise-solution.xlsx.xlsx
2.2 2.13.Practical-example.Descriptive-statistics-exercise.xlsx.xlsx
udemycoursedownloader.com.url
1. Part 1 Introduction
1. A Practical Example What You Will Learn in This Course.mp4
1. A Practical Example What You Will Learn in This Course.srt
1. A Practical Example What You Will Learn in This Course.vtt
2. What Does the Course Cover.mp4
2. What Does the Course Cover.srt
2. What Does the Course Cover.vtt
2. The Field of Data Science - The Various Data Science Disciplines
1. Data Science and Business Buzzwords Why are there so many.mp4
1. Data Science and Business Buzzwords Why are there so many.srt
1. Data Science and Business Buzzwords Why are there so many.vtt
2. Data Science and Business Buzzwords Why are there so many.html
3. What is the difference between Analysis and Analytics.mp4
3. What is the difference between Analysis and Analytics.srt
3. What is the difference between Analysis and Analytics.vtt
4. What is the difference between Analysis and Analytics.html
5. Business Analytics, Data Analytics, and Data Science An Introduction.mp4
5. Business Analytics, Data Analytics, and Data Science An Introduction.srt
5. Business Analytics, Data Analytics, and Data Science An Introduction.vtt
5.1 365_DataScience_Diagram.pdf.pdf
6. Business Analytics, Data Analytics, and Data Science An Introduction.html
7. Continuing with BI, ML, and AI.mp4
7. Continuing with BI, ML, and AI.srt
7. Continuing with BI, ML, and AI.vtt
7.1 365_DataScience_Diagram.pdf.pdf
7.2 365_DataScience.png.png
8. Continuing with BI, ML, and AI.html
9. A Breakdown of our Data Science Infographic.mp4
9. A Breakdown of our Data Science Infographic.srt
9. A Breakdown of our Data Science Infographic.vtt
9.1 365_DataScience.png.png
10. A Breakdown of our Data Science Infographic.html
3. The Field of Data Science - Connecting the Data Science Disciplines
1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4
1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.srt
1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.vtt
2. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.html
4. The Field of Data Science - The Benefits of Each Discipline
1. The Reason behind these Disciplines.mp4
1. The Reason behind these Disciplines.srt
1. The Reason behind these Disciplines.vtt
2. The Reason behind these Disciplines.html
5. The Field of Data Science - Popular Data Science Techniques
1. Techniques for Working with Traditional Data.mp4
1. Techniques for Working with Traditional Data.srt
1. Techniques for Working with Traditional Data.vtt
2. Techniques for Working with Traditional Data.html
3. Real Life Examples of Traditional Data.mp4
3. Real Life Examples of Traditional Data.srt
3. Real Life Examples of Traditional Data.vtt
4. Techniques for Working with Big Data.mp4
4. Techniques for Working with Big Data.srt
4. Techniques for Working with Big Data.vtt
5. Techniques for Working with Big Data.html
6. Real Life Examples of Big Data.mp4
6. Real Life Examples of Big Data.srt
6. Real Life Examples of Big Data.vtt
7. Business Intelligence (BI) Techniques.mp4
7. Business Intelligence (BI) Techniques.srt
7. Business Intelligence (BI) Techniques.vtt
8. Business Intelligence (BI) Techniques.html
9. Real Life Examples of Business Intelligence (BI).mp4
9. Real Life Examples of Business Intelligence (BI).srt
9. Real Life Examples of Business Intelligence (BI).vtt
10. Techniques for Working with Traditional Methods.mp4
10. Techniques for Working with Traditional Methods.srt
10. Techniques for Working with Traditional Methods.vtt
11. Techniques for Working with Traditional Methods.html
12. Real Life Examples of Traditional Methods.mp4
12. Real Life Examples of Traditional Methods.srt
12. Real Life Examples of Traditional Methods.vtt
13. Machine Learning (ML) Techniques.mp4
13. Machine Learning (ML) Techniques.srt
13. Machine Learning (ML) Techniques.vtt
14. Machine Learning (ML) Techniques.html
15. Types of Machine Learning.mp4
15. Types of Machine Learning.srt
15. Types of Machine Learning.vtt
16. Types of Machine Learning.html
17. Real Life Examples of Machine Learning (ML).mp4
17. Real Life Examples of Machine Learning (ML).srt
17. Real Life Examples of Machine Learning (ML).vtt
18. Real Life Examples of Machine Learning (ML).html
6. The Field of Data Science - Popular Data Science Tools
1. Necessary Programming Languages and Software Used in Data Science.mp4
1. Necessary Programming Languages and Software Used in Data Science.srt
1. Necessary Programming Languages and Software Used in Data Science.vtt
2. Necessary Programming Languages and Software Used in Data Science.html
7. The Field of Data Science - Careers in Data Science
1. Finding the Job - What to Expect and What to Look for.mp4
1. Finding the Job - What to Expect and What to Look for.srt
1. Finding the Job - What to Expect and What to Look for.vtt
2. Finding the Job - What to Expect and What to Look for.html
8. The Field of Data Science - Debunking Common Misconceptions
1. Debunking Common Misconceptions.mp4
1. Debunking Common Misconceptions.srt
1. Debunking Common Misconceptions.vtt
2. Debunking Common Misconceptions.html
9. Part 2 Statistics
1. Population and Sample.mp4
1. Population and Sample.srt
1. Population and Sample.vtt
1.1 Glossary.xlsx.xlsx
1.2 Course notes_descriptive_statistics.pdf.pdf
2. Population and Sample.html
10. Statistics - Descriptive Statistics
1. Types of Data.mp4
1. Types of Data.srt
1. Types of Data.vtt
1.1 Course notes_descriptive_statistics.pdf.pdf
2. Types of Data.html
3. Levels of Measurement.mp4
3. Levels of Measurement.srt
3. Levels of Measurement.vtt
4. Levels of Measurement.html
5. Categorical Variables - Visualization Techniques.mp4
5. Categorical Variables - Visualization Techniques.srt
5. Categorical Variables - Visualization Techniques.vtt
5.1 2.3.Categorical-variables.Visualization-techniques-lesson.xlsx.xlsx
6. Categorical Variables Exercise.html
6.1 2.3. Categorical variables. Visualization techniques_exercise_solution.xlsx.xlsx
6.2 2.3. Categorical variables. Visualization techniques_exercise.xlsx.xlsx
7. Numerical Variables - Frequency Distribution Table.mp4
7. Numerical Variables - Frequency Distribution Table.srt
7. Numerical Variables - Frequency Distribution Table.vtt
7.1 2.4. Numerical variables. Frequency distribution table_lesson.xlsx.xlsx
8. Numerical Variables Exercise.html
8.1 2.4. Numerical variables. Frequency distribution table_exercise_solution.xlsx.xlsx
8.2 2.4. Numerical variables. Frequency distribution table_exercise.xlsx.xlsx
9. The Histogram.mp4
9. The Histogram.srt
9. The Histogram.vtt
9.1 2.5. The Histogram_lesson.xlsx.xlsx
10. Histogram Exercise.html
10.1 2.5.The-Histogram-exercise.xlsx.xlsx
10.2 2.5.The-Histogram-exercise-solution.xlsx.xlsx
11. Cross Table and Scatter Plot.mp4
11. Cross Table and Scatter Plot.srt
11. Cross Table and Scatter Plot.vtt
11.1 2.6. Cross table and scatter plot.xlsx.xlsx
12. Cross Tables and Scatter Plots Exercise.html
12.1 2.6. Cross table and scatter plot_exercise_solution.xlsx.xlsx
12.2 2.6. Cross table and scatter plot_exercise.xlsx.xlsx
13. Mean, median and mode.mp4
13. Mean, median and mode.srt
13. Mean, median and mode.vtt
13.1 2.7. Mean, median and mode_lesson.xlsx.xlsx
14. Mean, Median and Mode Exercise.html
14.1 2.7. Mean, median and mode_exercise_solution.xlsx.xlsx
14.2 2.7. Mean, median and mode_exercise.xlsx.xlsx
15. Skewness.mp4
15. Skewness.srt
15. Skewness.vtt
15.1 2.8. Skewness_lesson.xlsx.xlsx
16. Skewness Exercise.html
16.1 2.8. Skewness_exercise.xlsx.xlsx
16.2 2.8. Skewness_exercise_solution.xlsx.xlsx
17. Variance.mp4
17. Variance.srt
17. Variance.vtt
17.1 2.9. Variance_lesson.xlsx.xlsx
18. Variance Exercise.html
18.1 2.9. Variance_exercise.xlsx.xlsx
18.2 2.9. Variance_exercise_solution.xlsx.xlsx
19. Standard Deviation and Coefficient of Variation.mp4
19. Standard Deviation and Coefficient of Variation.srt
19. Standard Deviation and Coefficient of Variation.vtt
19.1 2.10. Standard deviation and coefficient of variation_lesson.xlsx.xlsx
20. Standard Deviation and Coefficient of Variation Exercise.html
20.1 2.10. Standard deviation and coefficient of variation_exercise_solution.xlsx.xlsx
20.2 2.10. Standard deviation and coefficient of variation_exercise.xlsx.xlsx
21. Covariance.mp4
21. Covariance.srt
21. Covariance.vtt
21.1 2.11. Covariance_lesson.xlsx.xlsx
22. Covariance Exercise.html
22.1 2.11. Covariance_exercise.xlsx.xlsx
22.2 2.11. Covariance_exercise_solution.xlsx.xlsx
23. Correlation Coefficient.mp4
23. Correlation Coefficient.srt
23. Correlation Coefficient.vtt
24. Correlation Coefficient Exercise.html
24.1 2.12. Correlation_exercise.xlsx.xlsx
24.2 2.12. Correlation_exercise_solution.xlsx.xlsx
Udemy Course downloader.txt
12. Statistics - Inferential Statistics Fundamentals
1. Introduction.mp4
1. Introduction.srt
1. Introduction.vtt
1.1 Course notes_inferential statistics.pdf.pdf
2. What is a Distribution.mp4
2. What is a Distribution.srt
2. What is a Distribution.vtt
2.1 Course notes_inferential statistics.pdf.pdf
2.2 3.2. What is a distribution_lesson.xlsx.xlsx
3. What is a Distribution.html
4. The Normal Distribution.mp4
4. The Normal Distribution.srt
4. The Normal Distribution.vtt
5. The Normal Distribution.html
6. The Standard Normal Distribution.mp4
6. The Standard Normal Distribution.srt
6. The Standard Normal Distribution.vtt
6.1 3.4. Standard normal distribution_lesson.xlsx.xlsx
7. The Standard Normal Distribution Exercise.html
7.1 3.4. Standard normal distribution_exercise.xlsx.xlsx
7.2 3.4. Standard normal distribution_exercise_solution.xlsx.xlsx
8. Central Limit Theorem.mp4
8. Central Limit Theorem.srt
8. Central Limit Theorem.vtt
9. Central Limit Theorem.html
10. Standard error.mp4
10. Standard error.srt
10. Standard error.vtt
11. Estimators and Estimates.mp4
11. Estimators and Estimates.srt
11. Estimators and Estimates.vtt
12. Estimators and Estimates.html
13. Statistics - Inferential Statistics Confidence Intervals
1. What are Confidence Intervals.mp4
1. What are Confidence Intervals.srt
1. What are Confidence Intervals.vtt
2. What are Confidence Intervals.html
3. Confidence Intervals; Population Variance Known; z-score.mp4
3. Confidence Intervals; Population Variance Known; z-score.srt
3. Confidence Intervals; Population Variance Known; z-score.vtt
3.1 3.9. Population variance known, z-score_lesson.xlsx.xlsx
3.2 3.9. The z-table.xlsx.xlsx
4. Confidence Intervals; Population Variance Known; z-score; Exercise.html
4.1 3.9. Population variance known, z-score_exercise.xlsx.xlsx
4.2 3.9. The z-table.xlsx.xlsx
4.3 3.9. Population variance known, z-score_exercise_solution.xlsx.xlsx
5. Student's T Distribution.mp4
5. Student's T Distribution.srt
5. Student's T Distribution.vtt
6. Student's T Distribution.html
7. Confidence Intervals; Population Variance Unknown; t-score.mp4
7. Confidence Intervals; Population Variance Unknown; t-score.srt
7. Confidence Intervals; Population Variance Unknown; t-score.vtt
7.1 3.11. Population variance unknown, t-score_lesson.xlsx.xlsx
7.2 3.11. The t-table.xlsx.xlsx
8. Confidence Intervals; Population Variance Unknown; t-score; Exercise.html
8.1 3.11. Population variance unknown, t-score_exercise.xlsx.xlsx
8.2 3.11. Population variance unknown, t-score_exercise_solution.xlsx.xlsx
9. Margin of Error.mp4
9. Margin of Error.srt
9. Margin of Error.vtt
10. Margin of Error.html
11. Confidence intervals. Two means. Dependent samples.mp4
11. Confidence intervals. Two means. Dependent samples.srt
11. Confidence intervals. Two means. Dependent samples.vtt
11.1 3.13. Confidence intervals. Two means. Dependent samples_lesson.xlsx.xlsx
12. Confidence intervals. Two means. Dependent samples Exercise.html
12.1 3.13. Confidence intervals. Two means. Dependent samples_exercise_solution.xlsx.xlsx
12.2 3.13. Confidence intervals. Two means. Dependent samples_exercise.xlsx.xlsx
13. Confidence intervals. Two means. Independent samples (Part 1).mp4
13. Confidence intervals. Two means. Independent samples (Part 1).srt
13. Confidence intervals. Two means. Independent samples (Part 1).vtt
13.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_lesson.xlsx.xlsx
14. Confidence intervals. Two means. Independent samples (Part 1) Exercise.html
14.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise_solution.xlsx.xlsx
14.2 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise.xlsx.xlsx
15. Confidence intervals. Two means. Independent samples (Part 2).mp4
15. Confidence intervals. Two means. Independent samples (Part 2).srt
15. Confidence intervals. Two means. Independent samples (Part 2).vtt
15.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_lesson.xlsx.xlsx
16. Confidence intervals. Two means. Independent samples (Part 2) Exercise.html
16.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise_solution.xlsx.xlsx
16.2 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise.xlsx.xlsx
17. Confidence intervals. Two means. Independent samples (Part 3).mp4
17. Confidence intervals. Two means. Independent samples (Part 3).srt
17. Confidence intervals. Two means. Independent samples (Part 3).vtt
14. Statistics - Practical Example Inferential Statistics
1. Practical Example Inferential Statistics.mp4
1. Practical Example Inferential Statistics.srt
1. Practical Example Inferential Statistics.vtt
1.1 3.17. Practical example. Confidence intervals_lesson.xlsx.xlsx
2. Practical Example Inferential Statistics Exercise.html
2.1 3.17. Practical example. Confidence intervals_exercise.xlsx.xlsx
2.2 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx.xlsx
15. Statistics - Hypothesis Testing
1. The Null vs Alternative Hypothesis.mp4
1. The Null vs Alternative Hypothesis.srt
1. The Null vs Alternative Hypothesis.vtt
1.1 Course notes_hypothesis_testing.pdf.pdf
2. Further Reading on Null and Alternative Hypothesis.html
3. The Null vs Alternative Hypothesis.html
4. Rejection Region and Significance Level.mp4
4. Rejection Region and Significance Level.srt
4. Rejection Region and Significance Level.vtt
4.1 Course notes_hypothesis_testing.pdf.pdf
5. Rejection Region and Significance Level.html
6. Type I Error and Type II Error.mp4
6. Type I Error and Type II Error.srt
6. Type I Error and Type II Error.vtt
7. Type I Error and Type II Error.html
8. Test for the Mean. Population Variance Known.mp4
8. Test for the Mean. Population Variance Known.srt
8. Test for the Mean. Population Variance Known.vtt
8.1 4.4. Test for the mean. Population variance known_lesson.xlsx.xlsx
9. Test for the Mean. Population Variance Known Exercise.html
9.1 4.4. Test for the mean. Population variance known_exercise.xlsx.xlsx
9.2 4.4. Test for the mean. Population variance known_exercise_solution.xlsx.xlsx
10. p-value.mp4
10. p-value.srt
10. p-value.vtt
10.1 Online p-value calculator.pdf.pdf
11. p-value.html
12. Test for the Mean. Population Variance Unknown.mp4
12. Test for the Mean. Population Variance Unknown.srt
12. Test for the Mean. Population Variance Unknown.vtt
12.1 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx.xlsx
13. Test for the Mean. Population Variance Unknown Exercise.html
13.1 4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx.xlsx
13.2 4.6.Test-for-the-mean.Population-variance-unknown-exercise.xlsx.xlsx
14. Test for the Mean. Dependent Samples.mp4
14. Test for the Mean. Dependent Samples.srt
14. Test for the Mean. Dependent Samples.vtt
14.1 4.7. Test for the mean. Dependent samples_lesson.xlsx.xlsx
15. Test for the Mean. Dependent Samples Exercise.html
15.1 4.7. Test for the mean. Dependent samples_exercise.xlsx.xlsx
15.2 4.7. Test for the mean. Dependent samples_exercise_solution.xlsx.xlsx
16. Test for the mean. Independent samples (Part 1).mp4
16. Test for the mean. Independent samples (Part 1).srt
16. Test for the mean. Independent samples (Part 1).vtt
16.1 4.8. Test for the mean. Independent samples (Part 1)_lesson.xlsx.xlsx
17. Test for the mean. Independent samples (Part 2).mp4
17. Test for the mean. Independent samples (Part 2).srt
17. Test for the mean. Independent samples (Part 2).vtt
17.1 4.9. Test for the mean. Independent samples (Part 2)_lesson.xlsx.xlsx
18. Test for the mean. Independent samples (Part 2) Exercise.html
18.1 4.9. Test for the mean. Independent samples (Part 2)_exercise.xlsx.xlsx
18.2 4.9. Test for the mean. Independent samples (Part 2)_exercise_solution.xlsx.xlsx
16. Statistics - Practical Example Hypothesis Testing
1. Practical Example Hypothesis Testing.mp4
1. Practical Example Hypothesis Testing.srt
1. Practical Example Hypothesis Testing.vtt
1.1 4.10.Hypothesis-testing-section-practical-example.xlsx.xlsx
2. Practical Example Hypothesis Testing Exercise.html
2.1 4.10. Hypothesis testing section_practical example_exercise.xlsx.xlsx
2.2 4.10.Hypothesis-testing-section-practical-example-exercise-solution.xlsx.xlsx
17. Part 3 Introduction to Python
1. Introduction to Programming.mp4
1. Introduction to Programming.srt
1. Introduction to Programming.vtt
2. Introduction to Programming.html
3. Why Python.mp4
3. Why Python.srt
3. Why Python.vtt
4. Why Python.html
5. Why Jupyter.mp4
5. Why Jupyter.srt
5. Why Jupyter.vtt
6. Why Jupyter.html
7. Installing Python and Jupyter.mp4
7. Installing Python and Jupyter.srt
7. Installing Python and Jupyter.vtt
8. Understanding Jupyter's Interface - the Notebook Dashboard.mp4
8. Understanding Jupyter's Interface - the Notebook Dashboard.srt
8. Understanding Jupyter's Interface - the Notebook Dashboard.vtt
9. Prerequisites for Coding in the Jupyter Notebooks.mp4
9. Prerequisites for Coding in the Jupyter Notebooks.srt
9. Prerequisites for Coding in the Jupyter Notebooks.vtt
10. Jupyter's Interface.html
18. Python - Variables and Data Types
1. Variables.mp4
1. Variables.srt
1. Variables.vtt
1.1 Variables - Resources.html
2. Variables.html
3. Numbers and Boolean Values in Python.mp4
3. Numbers and Boolean Values in Python.srt
3. Numbers and Boolean Values in Python.vtt
3.1 Numbers and Boolean Values - Resources.html
4. Numbers and Boolean Values in Python.html
5. Python Strings.mp4
5. Python Strings.srt
5. Python Strings.vtt
5.1 Strings - Resources.html
6. Python Strings.html
19. Python - Basic Python Syntax
1. Using Arithmetic Operators in Python.mp4
1. Using Arithmetic Operators in Python.srt
1. Using Arithmetic Operators in Python.vtt
1.1 Arithmetic Operators - Resources.html
2. Using Arithmetic Operators in Python.html
3. The Double Equality Sign.mp4
3. The Double Equality Sign.srt
3. The Double Equality Sign.vtt
3.1 The Double Equality Sign - Resources.html
4. The Double Equality Sign.html
5. How to Reassign Values.mp4
5. How to Reassign Values.srt
5. How to Reassign Values.vtt
5.1 Reassign Values - Resources.html
6. How to Reassign Values.html
7. Add Comments.mp4
7. Add Comments.srt
7. Add Comments.vtt
7.1 Add Comments - Resources.html
8. Add Comments.html
9. Understanding Line Continuation.mp4
9. Understanding Line Continuation.srt
9. Understanding Line Continuation.vtt
9.1 Line Continuation - Resources.html
10. Indexing Elements.mp4
10. Indexing Elements.srt
10. Indexing Elements.vtt
10.1 Indexing Elements - Resources.html
11. Indexing Elements.html
12. Structuring with Indentation.mp4
12. Structuring with Indentation.srt
12. Structuring with Indentation.vtt
12.1 Structure Your Code with Indentation - Resources.html
13. Structuring with Indentation.html
20. Python - Other Python Operators
1. Comparison Operators.mp4
1. Comparison Operators.srt
1. Comparison Operators.vtt
1.1 Comparison Operators - Resources.html
2. Comparison Operators.html
3. Logical and Identity Operators.mp4
3. Logical and Identity Operators.srt
3. Logical and Identity Operators.vtt
3.1 Logical and Identity Operators - Resources.html
4. Logical and Identity Operators.html
21. Python - Conditional Statements
1. The IF Statement.mp4
1. The IF Statement.srt
1. The IF Statement.vtt
1.1 Introduction to the If Statement - Resources.html
2. The IF Statement.html
3. The ELSE Statement.mp4
3. The ELSE Statement.srt
3. The ELSE Statement.vtt
3.1 Add an Else Statement - Resources.html
4. The ELIF Statement.mp4
4. The ELIF Statement.srt
4. The ELIF Statement.vtt
4.1 Else if, for Brief - Elif - Resources.html
5. A Note on Boolean Values.mp4
5. A Note on Boolean Values.srt
5. A Note on Boolean Values.vtt
5.1 A Note on Boolean Values - Resources.html
6. A Note on Boolean Values.html
22. Python - Python Functions
1. Defining a Function in Python.mp4
1. Defining a Function in Python.srt
1. Defining a Function in Python.vtt
1.1 Defining a Function in Python - Resources.html
2. How to Create a Function with a Parameter.mp4
2. How to Create a Function with a Parameter.srt
2. How to Create a Function with a Parameter.vtt
2.1 Creating a Function with a Parameter - Resources.html
3. Defining a Function in Python - Part II.mp4
3. Defining a Function in Python - Part II.srt
3. Defining a Function in Python - Part II.vtt
3.1 Another Way to Define a Function - Resources.html
4. How to Use a Function within a Function.mp4
4. How to Use a Function within a Function.srt
4. How to Use a Function within a Function.vtt
4.1 Using a Function in Another Function - Resources.html
5. Conditional Statements and Functions.mp4
5. Conditional Statements and Functions.srt
5. Conditional Statements and Functions.vtt
5.1 Combining Conditional Statements and Functions - Resources.html
6. Functions Containing a Few Arguments.mp4
6. Functions Containing a Few Arguments.srt
6. Functions Containing a Few Arguments.vtt
6.1 Creating Functions Containing a Few Arguments - Resources.html
7. Built-in Functions in Python.mp4
7. Built-in Functions in Python.srt
7. Built-in Functions in Python.vtt
7.1 Notable Built-In Functions in Python - Resources.html
8. Python Functions.html
23. Python - Sequences
1. Lists.mp4
1. Lists.srt
1. Lists.vtt
1.1 Lists - Resources.html
2. Lists.html
3. Using Methods.mp4
3. Using Methods.srt
3. Using Methods.vtt
3.1 Help Yourself with Methods - Resources.html
4. Using Methods.html
5. List Slicing.mp4
5. List Slicing.srt
5. List Slicing.vtt
5.1 List Slicing - Resources.html
6. Tuples.mp4
6. Tuples.srt
6. Tuples.vtt
6.1 Tuples - Resources.html
7. Dictionaries.mp4
7. Dictionaries.srt
7. Dictionaries.vtt
7.1 Dictionaries - Resources.html
8. Dictionaries.html
24. Python - Iterations
1. For Loops.mp4
1. For Loops.srt
1. For Loops.vtt
1.1 For Loops - Resources.html
2. For Loops.html
3. While Loops and Incrementing.mp4
3. While Loops and Incrementing.srt
3. While Loops and Incrementing.vtt
3.1 While Loops and Incrementing - Resources.html
4. Lists with the range() Function.mp4
4. Lists with the range() Function.srt
4. Lists with the range() Function.vtt
4.1 Create Lists with the range() Function - Resources.html
5. Lists with the range() Function.html
6. Conditional Statements and Loops.mp4
6. Conditional Statements and Loops.srt
6. Conditional Statements and Loops.vtt
6.1 Use Conditional Statements and Loops Together - Resources.html
7. Conditional Statements, Functions, and Loops.mp4
7. Conditional Statements, Functions, and Loops.srt
7. Conditional Statements, Functions, and Loops.vtt
7.1 All In - Conditional Statements, Functions, and Loops - Resources.html
8. How to Iterate over Dictionaries.mp4
8. How to Iterate over Dictionaries.srt
8. How to Iterate over Dictionaries.vtt
8.1 Iterating over Dictionaries - Resources.html
25. Python - Advanced Python Tools
1. Object Oriented Programming.mp4
1. Object Oriented Programming.srt
1. Object Oriented Programming.vtt
2. Object Oriented Programming.html
3. Modules and Packages.mp4
3. Modules and Packages.srt
3. Modules and Packages.vtt
4. Modules and Packages.html
5. What is the Standard Library.mp4
5. What is the Standard Library.srt
5. What is the Standard Library.vtt
6. What is the Standard Library.html
7. Importing Modules in Python.mp4
7. Importing Modules in Python.srt
7. Importing Modules in Python.vtt
8. Importing Modules in Python.html
26. Part 4 Advanced Statistical Methods in Python
1. Introduction to Regression Analysis.mp4
1. Introduction to Regression Analysis.srt
1. Introduction to Regression Analysis.vtt
2. Introduction to Regression Analysis.html
27. Advanced Statistical Methods - Linear regression
1. The Linear Regression Model.mp4
1. The Linear Regression Model.srt
1. The Linear Regression Model.vtt
2. The Linear Regression Model.html
3. Correlation vs Regression.mp4
3. Correlation vs Regression.srt
3. Correlation vs Regression.vtt
4. Correlation vs Regression.html
5. Geometrical Representation of the Linear Regression Model.mp4
5. Geometrical Representation of the Linear Regression Model.srt
5. Geometrical Representation of the Linear Regression Model.vtt
6. Python Packages Installation.mp4
6. Python Packages Installation.srt
6. Python Packages Installation.vtt
7. First Regression in Python.mp4
7. First Regression in Python.srt
7. First Regression in Python.vtt
7.1 Simple linear regression - Lecture.html
7.2 Simple linear regression - Exercise.html
8. First Regression in Python Exercise.html
8.1 Simple Linear Regression Exercise.html
9. Using Seaborn for Graphs.mp4
9. Using Seaborn for Graphs.srt
9. Using Seaborn for Graphs.vtt
10. How to Interpret the Regression Table.mp4
10. How to Interpret the Regression Table.srt
10. How to Interpret the Regression Table.vtt
11. Decomposition of Variability.mp4
11. Decomposition of Variability.srt
11. Decomposition of Variability.vtt
12. Decomposition of Variability.html
13. What is the OLS.mp4
13. What is the OLS.srt
13. What is the OLS.vtt
14. R-Squared.mp4
14. R-Squared.srt
14. R-Squared.vtt
15. R-Squared.html
28. Advanced Statistical Methods - Multiple Linear Regression
1. Multiple Linear Regression.mp4
1. Multiple Linear Regression.srt
1. Multiple Linear Regression.vtt
2. Adjusted R-Squared.mp4
2. Adjusted R-Squared.srt
2. Adjusted R-Squared.vtt
2.1 Multiple linear regression - Lecture.html
3. Adjusted R-Squared.html
4. Multiple Linear Regression Exercise.html
4.1 Multiple Linear Regression Exercise.html
5. Test for Significance of the Model (F-Test).mp4
5. Test for Significance of the Model (F-Test).srt
5. Test for Significance of the Model (F-Test).vtt
6. OLS Assumptions.mp4
6. OLS Assumptions.srt
6. OLS Assumptions.vtt
7. OLS Assumptions.html
8. A1 Linearity.mp4
8. A1 Linearity.srt
8. A1 Linearity.vtt
9. A1 Linearity.html
10. A2 No Endogeneity.mp4
10. A2 No Endogeneity.srt
10. A2 No Endogeneity.vtt
11. A2 No Endogeneity.html
12. A3 Normality and Homoscedasticity.mp4
12. A3 Normality and Homoscedasticity.srt
12. A3 Normality and Homoscedasticity.vtt
13. A4 No Autocorrelation.mp4
13. A4 No Autocorrelation.srt
13. A4 No Autocorrelation.vtt
14. A4 No autocorrelation.html
15. A5 No Multicollinearity.mp4
15. A5 No Multicollinearity.srt
15. A5 No Multicollinearity.vtt
16. A5 No Multicollinearity.html
17. Dealing with Categorical Data - Dummy Variables.mp4
17. Dealing with Categorical Data - Dummy Variables.srt
17. Dealing with Categorical Data - Dummy Variables.vtt
17.1 Dummies - Lecture.html
18. Dealing with Categorical Data - Dummy Variables.html
18.1 Dummy variables Exercise.html
19. Making Predictions with the Linear Regression.mp4
19. Making Predictions with the Linear Regression.srt
19. Making Predictions with the Linear Regression.vtt
19.1 Making predictions - Lecture.html
29. Advanced Statistical Methods - Logistic Regression
1. Introduction to Logistic Regression.mp4
1. Introduction to Logistic Regression.srt
1. Introduction to Logistic Regression.vtt
2. A Simple Example in Python.mp4
2. A Simple Example in Python.srt
2. A Simple Example in Python.vtt
2.1 Simple logistic regression example.html
3. Logistic vs Logit Function.mp4
3. Logistic vs Logit Function.srt
3. Logistic vs Logit Function.vtt
4. Building a Logistic Regression.mp4
4. Building a Logistic Regression.srt
4. Building a Logistic Regression.vtt
4.1 Building a logistic regression.html
5. An Invaluable Coding Tip.mp4
5. An Invaluable Coding Tip.srt
5. An Invaluable Coding Tip.vtt
6. Understanding Logistic Regression Tables.mp4
6. Understanding Logistic Regression Tables.srt
6. Understanding Logistic Regression Tables.vtt
7. What do the Odds Actually Mean.mp4
7. What do the Odds Actually Mean.srt
7. What do the Odds Actually Mean.vtt
8. Binary Predictors in a Logistic Regression.mp4
8. Binary Predictors in a Logistic Regression.srt
8. Binary Predictors in a Logistic Regression.vtt
8.1 Binary predictors.html
9. Calculating the Accuracy of the Model.mp4
9. Calculating the Accuracy of the Model.srt
9. Calculating the Accuracy of the Model.vtt
9.1 Accuracy.html
10. Underfitting and Overfitting.mp4
10. Underfitting and Overfitting.srt
10. Underfitting and Overfitting.vtt
11. Testing the Model.mp4
11. Testing the Model.srt
11. Testing the Model.vtt
11.1 Test dataset.html
30. Advanced Statistical Methods - Cluster Analysis
1. Introduction to Cluster Analysis.mp4
1. Introduction to Cluster Analysis.srt
1. Introduction to Cluster Analysis.vtt
2. Some Examples of Clusters.mp4
2. Some Examples of Clusters.srt
2. Some Examples of Clusters.vtt
3. Difference between Classification and Clustering.mp4
3. Difference between Classification and Clustering.srt
3. Difference between Classification and Clustering.vtt
4. Math Prerequisites.mp4
4. Math Prerequisites.srt
4. Math Prerequisites.vtt
31. Advanced Statistical Methods - K-Means Clustering
1. K-Means Clustering.mp4
1. K-Means Clustering.srt
1. K-Means Clustering.vtt
2. A Simple Example of Clustering.mp4
2. A Simple Example of Clustering.srt
2. A Simple Example of Clustering.vtt
2.1 Country clusters.html
3. Clustering Categorical Data.mp4
3. Clustering Categorical Data.srt
3. Clustering Categorical Data.vtt
3.1 Clustering categorical data.html
4. How to Choose the Number of Clusters.mp4
4. How to Choose the Number of Clusters.srt
4. How to Choose the Number of Clusters.vtt
4.1 Selecting the number of clusters.html
5. Pros and Cons of K-Means Clustering.mp4
5. Pros and Cons of K-Means Clustering.srt
5. Pros and Cons of K-Means Clustering.vtt
6. To Standardize or to not Standardize.mp4
6. To Standardize or to not Standardize.srt
6. To Standardize or to not Standardize.vtt
7. Relationship between Clustering and Regression.mp4
7. Relationship between Clustering and Regression.srt
7. Relationship between Clustering and Regression.vtt
8. Market Segmentation with Cluster Analysis (Part 1).mp4
8. Market Segmentation with Cluster Analysis (Part 1).srt
8. Market Segmentation with Cluster Analysis (Part 1).vtt
8.1 Market segmentation example.html
9. Market Segmentation with Cluster Analysis (Part 2).mp4
9. Market Segmentation with Cluster Analysis (Part 2).srt
9. Market Segmentation with Cluster Analysis (Part 2).vtt
9.1 Market segmentation example (Part 2).html
10. How is Clustering Useful.mp4
10. How is Clustering Useful.srt
10. How is Clustering Useful.vtt
32. Advanced Statistical Methods - Other Types of Clustering
1. Types of Clustering.mp4
1. Types of Clustering.srt
1. Types of Clustering.vtt
2. Dendrogram.mp4
2. Dendrogram.srt
2. Dendrogram.vtt
3. Heatmaps.mp4
3. Heatmaps.srt
3. Heatmaps.vtt
3.1 Heatmaps.html
33. Part 5 Mathematics
1. What is a matrix.mp4
1. What is a matrix.srt
1. What is a matrix.vtt
2. What is a Matrix.html
3. Scalars and Vectors.mp4
3. Scalars and Vectors.srt
3. Scalars and Vectors.vtt
4. Scalars and Vectors.html
5. Linear Algebra and Geometry.mp4
5. Linear Algebra and Geometry.srt
5. Linear Algebra and Geometry.vtt
6. Linear Algebra and Geometry.html
7. Arrays in Python - A Convenient Way To Represent Matrices.mp4
7. Arrays in Python - A Convenient Way To Represent Matrices.srt
7. Arrays in Python - A Convenient Way To Represent Matrices.vtt
7.1 Arrays in Python Notebook.html
8. What is a Tensor.mp4
8. What is a Tensor.srt
8. What is a Tensor.vtt
8.1 Tensors Notebook.html
9. What is a Tensor.html
10. Addition and Subtraction of Matrices.mp4
10. Addition and Subtraction of Matrices.srt
10. Addition and Subtraction of Matrices.vtt
10.1 Addition and Subtraction of Matrices Python Notebook.html
11. Addition and Subtraction of Matrices.html
12. Errors when Adding Matrices.mp4
12. Errors when Adding Matrices.srt
12. Errors when Adding Matrices.vtt
12.1 Errors when Adding Matrices Python Notebook.html
13. Transpose of a Matrix.mp4
13. Transpose of a Matrix.srt
13. Transpose of a Matrix.vtt
13.1 Transpose of a Matrix Python Notebook.html
14. Dot Product.mp4
14. Dot Product.srt
14. Dot Product.vtt
14.1 Dot Product Python Notebook.html
15. Dot Product of Matrices.mp4
15. Dot Product of Matrices.srt
15. Dot Product of Matrices.vtt
15.1 Dot Product of Matrices Python Notebook.html
16. Why is Linear Algebra Useful.mp4
16. Why is Linear Algebra Useful.srt
16. Why is Linear Algebra Useful.vtt
34. Part 6 Deep Learning
1. What to Expect from this Part.mp4
1. What to Expect from this Part.srt
1. What to Expect from this Part.vtt
2. What is Machine Learning.html
35. Deep Learning - Introduction to Neural Networks
1. Introduction to Neural Networks.mp4
1. Introduction to Neural Networks.srt
1. Introduction to Neural Networks.vtt
1.1 Course Notes - Section 2.pdf.pdf
2. Introduction to Neural Networks.html
3. Training the Model.mp4
3. Training the Model.srt
3. Training the Model.vtt
3.1 Course Notes - Section 2.pdf.pdf
4. Training the Model.html
5. Types of Machine Learning.mp4
5. Types of Machine Learning.srt
5. Types of Machine Learning.vtt
6. Types of Machine Learning.html
7. The Linear Model (Linear Algebraic Version).mp4
7. The Linear Model (Linear Algebraic Version).srt
7. The Linear Model (Linear Algebraic Version).vtt
8. The Linear Model.html
9. The Linear Model with Multiple Inputs.mp4
9. The Linear Model with Multiple Inputs.srt
9. The Linear Model with Multiple Inputs.vtt
10. The Linear Model with Multiple Inputs.html
11. The Linear model with Multiple Inputs and Multiple Outputs.mp4
11. The Linear model with Multiple Inputs and Multiple Outputs.srt
11. The Linear model with Multiple Inputs and Multiple Outputs.vtt
12. The Linear model with Multiple Inputs and Multiple Outputs.html
13. Graphical Representation of Simple Neural Networks.mp4
13. Graphical Representation of Simple Neural Networks.srt
13. Graphical Representation of Simple Neural Networks.vtt
14. Graphical Representation of Simple Neural Networks.html
15. What is the Objective Function.mp4
15. What is the Objective Function.srt
15. What is the Objective Function.vtt
16. What is the Objective Function.html
17. Common Objective Functions L2-norm Loss.mp4
17. Common Objective Functions L2-norm Loss.srt
17. Common Objective Functions L2-norm Loss.vtt
18. Common Objective Functions L2-norm Loss.html
19. Common Objective Functions Cross-Entropy Loss.mp4
19. Common Objective Functions Cross-Entropy Loss.srt
19. Common Objective Functions Cross-Entropy Loss.vtt
20. Common Objective Functions Cross-Entropy Loss.html
21. Optimization Algorithm 1-Parameter Gradient Descent.mp4
21. Optimization Algorithm 1-Parameter Gradient Descent.srt
21. Optimization Algorithm 1-Parameter Gradient Descent.vtt
21.1 GD-function-example.xlsx.xlsx
22. Optimization Algorithm 1-Parameter Gradient Descent.html
23. Optimization Algorithm n-Parameter Gradient Descent.mp4
23. Optimization Algorithm n-Parameter Gradient Descent.srt
23. Optimization Algorithm n-Parameter Gradient Descent.vtt
24. Optimization Algorithm n-Parameter Gradient Descent.html
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy
1. Basic NN Example (Part 1).mp4
1. Basic NN Example (Part 1).srt
1. Basic NN Example (Part 1).vtt
1.1 Shortcuts-for-Jupyter.pdf.pdf
1.2 Bais NN Example Part 1.html
2. Basic NN Example (Part 2).mp4
2. Basic NN Example (Part 2).srt
2. Basic NN Example (Part 2).vtt
2.1 Basic NN Example (Part 2).html
3. Basic NN Example (Part 3).mp4
3. Basic NN Example (Part 3).srt
3. Basic NN Example (Part 3).vtt
3.1 Basic NN Example (Part 3).html
4. Basic NN Example (Part 4).mp4
4. Basic NN Example (Part 4).srt
4. Basic NN Example (Part 4).vtt
4.1 Basic NN Example (Part 4).html
5. Basic NN Example Exercises.html
5.1 Basic NN Example Exercise 5 Solution.html
5.2 Basic NN Example (All Exercises).html
5.3 Basic NN Example Exercise 4 Solution.html
5.4 Basic NN Example Exercise 1 Solution.html
5.5 Basic NN Example Exercise 2 Solution.html
5.6 Basic NN Example Exercise 3d Solution.html
5.7 Basic NN Example Exercise 3b Solution.html
5.8 Basic NN Example Exercise 3c Solution.html
5.9 Basic NN Example Exercise 3a Solution.html
5.10 Basic NN Example Exercise 6 Solution.html
37. Deep Learning - TensorFlow Introduction
1. How to Install TensorFlow.mp4
1. How to Install TensorFlow.srt
1. How to Install TensorFlow.vtt
1.1 Shortcuts-for-Jupyter.pdf.pdf
2. A Note on Installation of Packages in Anaconda.html
3. TensorFlow Outline and Logic.mp4
3. TensorFlow Outline and Logic.srt
3. TensorFlow Outline and Logic.vtt
4. Actual Introduction to TensorFlow.mp4
4. Actual Introduction to TensorFlow.srt
4. Actual Introduction to TensorFlow.vtt
4.1 Shortcuts-for-Jupyter.pdf.pdf
5. Types of File Formats, supporting Tensors.mp4
5. Types of File Formats, supporting Tensors.srt
5. Types of File Formats, supporting Tensors.vtt
5.1 Basic NN Example with TensorFlow (Part 1).html
6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4
6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.srt
6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.vtt
6.1 Basic NN Example with TensorFlow (Part 2).html
7. Basic NN Example with TF Loss Function and Gradient Descent.mp4
7. Basic NN Example with TF Loss Function and Gradient Descent.srt
7. Basic NN Example with TF Loss Function and Gradient Descent.vtt
7.1 Basic NN Example with TensorFlow (Part 3).html
8. Basic NN Example with TF Model Output.mp4
8. Basic NN Example with TF Model Output.srt
8. Basic NN Example with TF Model Output.vtt
8.1 Basic NN Example with TensorFlow (Complete).html
9. Basic NN Example with TF Exercises.html
9.1 Basic NN Example with TensorFlow Exercise 2.4 Solution.html
9.2 Basic NN Example with TensorFlow Exercise 2.1 Solution.html
9.3 Basic NN Example with TensorFlow (All Exercises).html
9.4 Basic NN Example with TensorFlow Exercise 3 Solution.html
9.5 Basic NN Example with TensorFlow Exercise 2.2 Solution.html
9.6 Basic NN Example with TensorFlow Exercise 4 Solution.html
9.7 Basic NN Example with TensorFlow Exercise 1 Solution.html
9.8 Basic NN Example with TensorFlow Exercise 2.3 Solution.html
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks
1. What is a Layer.mp4
1. What is a Layer.srt
1. What is a Layer.vtt
1.1 Course Notes - Section 6.pdf.pdf
2. What is a Deep Net.mp4
2. What is a Deep Net.srt
2. What is a Deep Net.vtt
2.1 Course Notes - Section 6.pdf.pdf
3. Digging into a Deep Net.mp4
3. Digging into a Deep Net.srt
3. Digging into a Deep Net.vtt
4. Non-Linearities and their Purpose.mp4
4. Non-Linearities and their Purpose.srt
4. Non-Linearities and their Purpose.vtt
5. Activation Functions.mp4
5. Activation Functions.srt
5. Activation Functions.vtt
6. Activation Functions Softmax Activation.mp4
6. Activation Functions Softmax Activation.srt
6. Activation Functions Softmax Activation.vtt
7. Backpropagation.mp4
7. Backpropagation.srt
7. Backpropagation.vtt
8. Backpropagation picture.mp4
8. Backpropagation picture.srt
8. Backpropagation picture.vtt
9. Backpropagation - A Peek into the Mathematics of Optimization.html
9.1 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf.pdf
39. Deep Learning - Overfitting
1. What is Overfitting.mp4
1. What is Overfitting.srt
1. What is Overfitting.vtt
2. Underfitting and Overfitting for Classification.mp4
2. Underfitting and Overfitting for Classification.srt
2. Underfitting and Overfitting for Classification.vtt
3. What is Validation.mp4
3. What is Validation.srt
3. What is Validation.vtt
4. Training, Validation, and Test Datasets.mp4
4. Training, Validation, and Test Datasets.srt
4. Training, Validation, and Test Datasets.vtt
5. N-Fold Cross Validation.mp4
5. N-Fold Cross Validation.srt
5. N-Fold Cross Validation.vtt
6. Early Stopping or When to Stop Training.mp4
6. Early Stopping or When to Stop Training.srt
6. Early Stopping or When to Stop Training.vtt
40. Deep Learning - Initialization
1. What is Initialization.mp4
1. What is Initialization.srt
1. What is Initialization.vtt
2. Types of Simple Initializations.mp4
2. Types of Simple Initializations.srt
2. Types of Simple Initializations.vtt
3. State-of-the-Art Method - (Xavier) Glorot Initialization.mp4
3. State-of-the-Art Method - (Xavier) Glorot Initialization.srt
3. State-of-the-Art Method - (Xavier) Glorot Initialization.vtt
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules
1. Stochastic Gradient Descent.mp4
1. Stochastic Gradient Descent.srt
1. Stochastic Gradient Descent.vtt
2. Problems with Gradient Descent.mp4
2. Problems with Gradient Descent.srt
2. Problems with Gradient Descent.vtt
3. Momentum.mp4
3. Momentum.srt
3. Momentum.vtt
4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp4
4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.srt
4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.vtt
5. Learning Rate Schedules Visualized.mp4
5. Learning Rate Schedules Visualized.srt
5. Learning Rate Schedules Visualized.vtt
6. Adaptive Learning Rate Schedules ( AdaGrad and RMSprop ).mp4
6. Adaptive Learning Rate Schedules ( AdaGrad and RMSprop ).srt
6. Adaptive Learning Rate Schedules ( AdaGrad and RMSprop ).vtt
7. Adam (Adaptive Moment Estimation).mp4
7. Adam (Adaptive Moment Estimation).srt
7. Adam (Adaptive Moment Estimation).vtt
42. Deep Learning - Preprocessing
1. Preprocessing Introduction.mp4
1. Preprocessing Introduction.srt
1. Preprocessing Introduction.vtt
2. Types of Basic Preprocessing.mp4
2. Types of Basic Preprocessing.srt
2. Types of Basic Preprocessing.vtt
3. Standardization.mp4
3. Standardization.srt
3. Standardization.vtt
4. Preprocessing Categorical Data.mp4
4. Preprocessing Categorical Data.srt
4. Preprocessing Categorical Data.vtt
5. Binary and One-Hot Encoding.mp4
5. Binary and One-Hot Encoding.srt
5. Binary and One-Hot Encoding.vtt
43. Deep Learning - Classifying on the MNIST Dataset
1. MNIST What is the MNIST Dataset.mp4
1. MNIST What is the MNIST Dataset.srt
1. MNIST What is the MNIST Dataset.vtt
2. MNIST How to Tackle the MNIST.mp4
2. MNIST How to Tackle the MNIST.srt
2. MNIST How to Tackle the MNIST.vtt
3. MNIST Relevant Packages.mp4
3. MNIST Relevant Packages.srt
3. MNIST Relevant Packages.vtt
3.1 TensorFlow MNIST Part 1 with Comments.html
4. MNIST Model Outline.mp4
4. MNIST Model Outline.srt
4. MNIST Model Outline.vtt
4.1 TensorFlow MNIST Part 2 with Comments.html
5. MNIST Loss and Optimization Algorithm.mp4
5. MNIST Loss and Optimization Algorithm.srt
5. MNIST Loss and Optimization Algorithm.vtt
5.1 TensorFlow MNIST Part 3 with Comments.html
6. Calculating the Accuracy of the Model.mp4
6. Calculating the Accuracy of the Model.srt
6. Calculating the Accuracy of the Model.vtt
6.1 TensorFlow MNIST Part 4 with Comments.html
7. MNIST Batching and Early Stopping.mp4
7. MNIST Batching and Early Stopping.srt
7. MNIST Batching and Early Stopping.vtt
7.1 TensorFlow MNIST Part 5 with Comments.html
8. MNIST Learning.mp4
8. MNIST Learning.srt
8. MNIST Learning.vtt
8.1 TensorFlow MNIST Part 6 with Comments.html
9. MNIST Results and Testing.mp4
9. MNIST Results and Testing.srt
9. MNIST Results and Testing.vtt
9.1 TensorFlow MNIST Complete Code with Comments.html
10. MNIST Exercises.html
10.1 TensorFlow MNIST All Exercises.html
11. MNIST Solutions.html
11.1 TensorFlow MNIST 'Time' Solution.html
11.2 TensorFlow MNIST '1. Width' Solution.html
11.3 TensorFlow MNIST '3. Width and Depth' Solution.html
11.4 TensorFlow MNIST '2. Depth' Solution.html
11.5 TensorFlow MNIST '8. Learning Rate (Part 1)' Solution.html
11.6 TensorFlow MNIST '9. Learning Rate (Part 2)' Solution.html
11.7 TensorFlow MNIST '7. Batch size (Part 2)' Solution.html
11.8 TensorFlow MNIST '4. Activation Functions (Part 1)' Solution.html
11.9 TensorFlow MNIST '6. Batch size (Part 1)' Solution.html
11.10 TensorFlow MNIST '5. Activation Functions (Part 2)' Solution.html
11.11 TensorFlow MNIST 'Around 98% Accuracy' Solution.html
44. Deep Learning - Business Case Example
1. Business Case Getting acquainted with the dataset.mp4
1. Business Case Getting acquainted with the dataset.srt
1. Business Case Getting acquainted with the dataset.vtt
1.1 Audiobooks_data.csv.csv
2. Business Case Outlining the Solution.mp4
2. Business Case Outlining the Solution.srt
2. Business Case Outlining the Solution.vtt
3. The Importance of Working with a Balanced Dataset.mp4
3. The Importance of Working with a Balanced Dataset.srt
3. The Importance of Working with a Balanced Dataset.vtt
4. Business Case Preprocessing.mp4
4. Business Case Preprocessing.srt
4. Business Case Preprocessing.vtt
4.1 Audiobooks Preprocessing.html
5. Business Case Preprocessing Exercise.html
5.1 Preprocessing Exercise.html
6. Creating a Data Provider.mp4
6. Creating a Data Provider.srt
6. Creating a Data Provider.vtt
6.1 Creating a Data Provider (Class).html
7. Business Case Model Outline.mp4
7. Business Case Model Outline.srt
7. Business Case Model Outline.vtt
7.1 TensorFlow Business Case Model Outline.html
8. Business Case Optimization.mp4
8. Business Case Optimization.srt
8. Business Case Optimization.vtt
8.1 TensorFlow Business Case Optimization.html
9. Business Case Interpretation.mp4
9. Business Case Interpretation.srt
9. Business Case Interpretation.vtt
9.1 TensorFlow Business Case Interpretation.html
10. Business Case Testing the Model.mp4
10. Business Case Testing the Model.srt
10. Business Case Testing the Model.vtt
11. Business Case A Comment on the Homework.mp4
11. Business Case A Comment on the Homework.srt
11. Business Case A Comment on the Homework.vtt
11.1 TensorFlow Business Case Homework.html
12. Business Case Final Exercise.html
12.1 TensorFlow Business Case Homework.html
45. Deep Learning - Conclusion
1. Summary of What You Learned.mp4
1. Summary of What You Learned.srt
1. Summary of What You Learned.vtt
2. What's Further out there in terms of Machine Learning.mp4
2. What's Further out there in terms of Machine Learning.srt
2. What's Further out there in terms of Machine Learning.vtt
3. An overview of CNNs.mp4
3. An overview of CNNs.srt
3. An overview of CNNs.vtt
4. DeepMind and Deep Learning.html
5. An Overview of RNNs.mp4
5. An Overview of RNNs.srt
5. An Overview of RNNs.vtt
6. An Overview of non-NN Approaches.mp4
6. An Overview of non-NN Approaches.srt
6. An Overview of non-NN Approaches.vtt
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