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The Data Science Course Complete Data Science Bootcamp 2025 Dec

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Files:

01. A Practical Example What You Will Learn in This Course.mp4 10.8 MB
01. A Practical Example What You Will Learn in This Course.vtt 6.8 KB
02. What Does the Course Cover.mp4 9.6 MB
02. What Does the Course Cover.vtt 5.4 KB
03. Download All Resources and Important FAQ.html 21.3 KB
03. FAQ-The-Data-Science-Course.pdf 306.1 KB
01. Data Science and Business Buzzwords Why are there so Many.mp4 15.6 MB
01. Data Science and Business Buzzwords Why are there so Many.vtt 7.4 KB
02. What is the difference between Analysis and Analytics.mp4 11.2 MB
02. What is the difference between Analysis and Analytics.vtt 5.1 KB
03. Business Analytics, Data Analytics, and Data Science An Introduction.mp4 14.6 MB
03. Business Analytics, Data Analytics, and Data Science An Introduction.vtt 9.6 KB
04. Continuing with BI, ML, and AI.mp4 47.6 MB
04. Continuing with BI, ML, and AI.vtt 13.1 KB
05. Traditional AI vs. Generative AI.mp4 24.5 MB
05. Traditional AI vs. Generative AI.vtt 6.9 KB
06. More Examples of Generative AI.mp4 30.5 MB
06. More Examples of Generative AI.vtt 6.9 KB
07. A Breakdown of our Data Science Infographic.mp4 45.4 MB
07. A Breakdown of our Data Science Infographic.vtt 5.1 KB
03. 365-DataScience-Diagram.pdf 323.1 KB
04. 365-DataScience-Diagram.pdf 323.1 KB
04. 365-DataScience.png 6.9 MB
07. 365-DataScience.png 6.9 MB
01. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 83.5 MB
01. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.vtt 9.2 KB
01. The Reason Behind These Disciplines.mp4 46.8 MB
01. The Reason Behind These Disciplines.vtt 6.5 KB
01. Techniques for Working with Traditional Data.mp4 107.2 MB
01. Techniques for Working with Traditional Data.vtt 11.0 KB
02. Real Life Examples of Traditional Data.mp4 18.4 MB
02. Real Life Examples of Traditional Data.vtt 2.3 KB
03. Techniques for Working with Big Data.mp4 62.1 MB
03. Techniques for Working with Big Data.vtt 5.8 KB
04. Real Life Examples of Big Data.mp4 13.0 MB
04. Real Life Examples of Big Data.vtt 1.9 KB
05. Business Intelligence (BI) Techniques.mp4 52.9 MB
05. Business Intelligence (BI) Techniques.vtt 8.8 KB
06. Real Life Examples of Business Intelligence (BI).mp4 24.6 MB
06. Real Life Examples of Business Intelligence (BI).vtt 2.3 KB
07. Techniques for Working with Traditional Methods.mp4 76.0 MB
07. Techniques for Working with Traditional Methods.vtt 11.5 KB
08. Real Life Examples of Traditional Methods.mp4 36.7 MB
08. Real Life Examples of Traditional Methods.vtt 5.4 KB
09. Machine Learning (ML) Techniques.mp4 49.4 MB
09. Machine Learning (ML) Techniques.vtt 9.3 KB
10. Types of Machine Learning.mp4 69.5 MB
10. Types of Machine Learning.vtt 10.9 KB
11. Evolution and Latest Trends of Machine Learning (ML).mp4 27.3 MB
11. Evolution and Latest Trends of Machine Learning (ML).vtt 7.8 KB
12. Real Life Examples of Machine Learning (ML).mp4 27.7 MB
12. Real Life Examples of Machine Learning (ML).vtt 3.0 KB
01. Necessary Programming Languages and Software Used in Data Science.mp4 82.4 MB
01. Necessary Programming Languages and Software Used in Data Science.vtt 8.0 KB
01. Finding the Job - What to Expect and What to Look for.mp4 40.0 MB
01. Finding the Job - What to Expect and What to Look for.vtt 4.7 KB
01. Debunking Common Misconceptions.mp4 58.9 MB
01. Debunking Common Misconceptions.vtt 5.5 KB
01. The Basic Probability Formula.mp4 29.4 MB
01. The Basic Probability Formula.vtt 9.0 KB
02. Computing Expected Values.mp4 45.7 MB
02. Computing Expected Values.vtt 7.0 KB
03. Frequency.mp4 37.4 MB
03. Frequency.vtt 6.9 KB
04. Events and Their Complements.mp4 25.8 MB
04. Events and Their Complements.vtt 7.1 KB
01. Course-Notes-Basic-Probability.pdf 371.1 KB
01. Fundamentals of Combinatorics.mp4 5.9 MB
01. Fundamentals of Combinatorics.vtt 1.5 KB
02. Permutations and How to Use Them.mp4 17.5 MB
02. Permutations and How to Use Them.vtt 4.4 KB
03. Simple Operations with Factorials.mp4 10.5 MB
03. Simple Operations with Factorials.vtt 3.4 KB
04. Solving Variations with Repetition.mp4 13.9 MB
04. Solving Variations with Repetition.vtt 3.8 KB
05. Solving Variations without Repetition.mp4 18.3 MB
05. Solving Variations without Repetition.vtt 4.9 KB
06. Solving Combinations.mp4 23.6 MB
06. Solving Combinations.vtt 6.0 KB
07. Symmetry of Combinations.mp4 13.7 MB
07. Symmetry of Combinations.vtt 4.5 KB
08. Solving Combinations with Separate Sample Spaces.mp4 20.3 MB
08. Solving Combinations with Separate Sample Spaces.vtt 4.1 KB
09. Combinatorics in Real-Life The Lottery.mp4 16.4 MB
09. Combinatorics in Real-Life The Lottery.vtt 4.2 KB
10. A Recap of Combinatorics.mp4 12.1 MB
10. A Recap of Combinatorics.vtt 3.8 KB
11. A Practical Example of Combinatorics.mp4 80.7 MB
11. A Practical Example of Combinatorics.vtt 15.2 KB
01. Course-Notes-Combinatorics.pdf 226.1 KB
06. Combinations-With-Repetition.pdf 207.4 KB
07. Symmetry-Explained.pdf 85.0 KB
11. Additional-Exercises-Combinatorics-Solutions.pdf 245.7 KB
11. Additional-Exercises-Combinatorics.pdf 106.6 KB
01. Sets and Events.mp4 17.7 MB
01. Sets and Events.vtt 5.5 KB
02. Ways Sets Can Interact.mp4 11.3 MB
02. Ways Sets Can Interact.vtt 4.6 KB
03. Intersection of Sets.mp4 11.0 MB
03. Intersection of Sets.vtt 2.6 KB
04. Union of Sets.mp4 24.2 MB
04. Union of Sets.vtt 6.3 KB
05. Mutually Exclusive Sets.mp4 10.6 MB
05. Mutually Exclusive Sets.vtt 2.8 KB
06. Dependence and Independence of Sets.mp4 14.9 MB
06. Dependence and Independence of Sets.vtt 3.5 KB
07. The Conditional Probability Formula.mp4 20.1 MB
07. The Conditional Probability Formula.vtt 5.9 KB
08. The Law of Total Probability.mp4 14.2 MB
08. The Law of Total Probability.vtt 3.9 KB
09. The Additive Rule.mp4 11.1 MB
09. The Additive Rule.vtt 2.7 KB
10. The Multiplication Law.mp4 20.2 MB
10. The Multiplication Law.vtt 4.7 KB
11. Bayes' Law.mp4 21.3 MB
11. Bayes' Law.vtt 7.7 KB
12. A Practical Example of Bayesian Inference.mp4 139.2 MB
12. A Practical Example of Bayesian Inference.vtt 20.1 KB
01. Course-Notes-Bayesian-Inference.pdf 386.0 KB
12. Bayesian-Homework-Solutions.pdf 30.4 KB
12. Bayesian-Homework.pdf 27.3 KB
12. CDS-2017-2018-Hamilton.pdf 845.3 KB
01. Fundamentals of Probability Distributions.mp4 19.4 MB
01. Fundamentals of Probability Distributions.vtt 8.4 KB
02. Types of Probability Distributions.mp4 35.6 MB
02. Types of Probability Distributions.vtt 10.4 KB
03. Characteristics of Discrete Distributions.mp4 9.4 MB
03. Characteristics of Discrete Distributions.vtt 2.6 KB
04. Discrete Distributions The Uniform Distribution.mp4 10.3 MB
04. Discrete Distributions The Uniform Distribution.vtt 2.9 KB
05. Discrete Distributions The Bernoulli Distribution.mp4 15.1 MB
05. Discrete Distributions The Bernoulli Distribution.vtt 5.2 KB
06. Discrete Distributions The Binomial Distribution.mp4 30.6 MB
06. Discrete Distributions The Binomial Distribution.vtt 8.8 KB
07. Discrete Distributions The Poisson Distribution.mp4 23.9 MB
07. Discrete Distributions The Poisson Distribution.vtt 7.2 KB
08. Characteristics of Continuous Distributions.mp4 21.3 MB
08. Characteristics of Continuous Distributions.vtt 9.2 KB
09. Continuous Distributions The Normal Distribution.mp4 20.0 MB
09. Continuous Distributions The Normal Distribution.vtt 5.1 KB
10. Continuous Distributions The Standard Normal Distribution.mp4 21.1 MB
10. Continuous Distributions The Standard Normal Distribution.vtt 5.7 KB
11. Continuous Distributions The Students' T Distribution.mp4 9.2 MB
11. Continuous Distributions The Students' T Distribution.vtt 3.2 KB
12. Continuous Distributions The Chi-Squared Distribution.mp4 11.2 MB
12. Continuous Distributions The Chi-Squared Distribution.vtt 3.0 KB
13. Continuous Distributions The Exponential Distribution.mp4 16.0 MB
13. Continuous Distributions The Exponential Distribution.vtt 4.5 KB
14. Continuous Distributions The Logistic Distribution.mp4 16.2 MB
14. Continuous Distributions The Logistic Distribution.vtt 5.4 KB
15. A Practical Example of Probability Distributions.mp4 138.3 MB
15. A Practical Example of Probability Distributions.vtt 21.1 KB
01. Course-Notes-Probability-Distributions.pdf 463.9 KB
07. Poisson-Expected-Value-and-Variance.pdf 146.0 KB
08. Solving-Integrals.pdf 343.9 KB
09. Normal-Distribution-Exp-and-Var.pdf 144.1 KB
15. Customers-Membership-post.xlsx 15.6 KB
15. Customers-Membership.xlsx 9.7 KB
15. Daily-Views-post.xlsx 20.2 KB
15. Daily-Views.xlsx 9.5 KB
15. FIFA19-post.csv 8.6 MB
15. FIFA19.csv 8.6 MB
01. Probability in Finance.mp4 40.3 MB
01. Probability in Finance.vtt 10.1 KB
02. Probability in Statistics.mp4 31.6 MB
02. Probability in Statistics.vtt 9.1 KB
03. Probability in Data Science.mp4 14.2 MB
03. Probability in Data Science.vtt 7.1 KB
01. Probability-in-Finance-Homework.pdf 110.7 KB
01. Probability-in-Finance-Solutions.pdf 184.5 KB
03. Probability-Cheat-Sheet.pdf 320.3 KB
01. Population and Sample.mp4 35.1 MB
01. Population and Sample.vtt 5.8 KB
01. Course-notes-descriptive-statistics.pdf 482.2 KB
01. Statistics-Glossary.xlsx 20.3 KB
01. Types of Data.mp4 43.2 MB
01. Types of Data.vtt 5.8 KB
02. Levels of Measurement.mp4 32.2 MB
02. Levels of Measurement.vtt 4.8 KB
03. Categorical Variables - Visualization Techniques.mp4 27.5 MB
03. Categorical Variables - Visualization Techniques.vtt 6.7 KB
04. Categorical Variables Exercise.html 81 bytes
05. Numerical Variables - Frequency Distribution Table.mp4 17.7 MB
05. Numerical Variables - Frequency Distribution Table.vtt 4.5 KB
06. Numerical Variables Exercise.html 81 bytes
07. The Histogram.mp4 9.6 MB
07. The Histogram.vtt 3.4 KB
08. Histogram Exercise.html 81 bytes
09. Cross Tables and Scatter Plots.mp4 19.7 MB
09. Cross Tables and Scatter Plots.vtt 6.9 KB
10. Cross Tables and Scatter Plots Exercise.html 81 bytes
11. Mean, median and mode.mp4 24.5 MB
11. Mean, median and mode.vtt 6.0 KB
12. Mean, Median and Mode Exercise.html 81 bytes
13. Skewness.mp4 13.3 MB
13. Skewness.vtt 3.7 KB
14. Skewness Exercise.html 81 bytes
15. Variance.mp4 23.5 MB
15. Variance.vtt 8.2 KB
16. Variance Exercise.html 522 bytes
17. Standard Deviation and Coefficient of Variation.mp4 20.1 MB
17. Standard Deviation and Coefficient of Variation.vtt 6.3 KB
18. Standard Deviation and Coefficient of Variation Exercise.html 81 bytes
19. Covariance.mp4 18.4 MB
19. Covariance.vtt 5.1 KB
20. Covariance Exercise.html 81 bytes
21. Correlation Coefficient.mp4 19.3 MB
21. Correlation Coefficient.vtt 5.0 KB
22. Correlation Coefficient Exercise.html 81 bytes
01. Course-notes-descriptive-statistics.pdf 482.2 KB
01. Glossary.xlsx 20.0 KB
03. 2.3.Categorical-variables.Visualization-techniques-lesson.xlsx 30.8 KB
04. 2.3.Categorical-variables.Visualization-techniques-exercise-solution.xlsx 41.1 KB
04. 2.3.Categorical-variables.Visualization-techniques-exercise.xlsx 15.2 KB
04. Statistics-PDF-with-Excel-Solutions-that-dont-visualize-properly.pdf 289.1 KB
05. 2.4.Numerical-variables.Frequency-distribution-table-lesson.xlsx 11.4 KB
06. 2.4.Numerical-variables.Frequency-distribution-table-exercise-solution.xlsx 13.2 KB
07. 2.5.The-Histogram-lesson.xlsx 18.6 KB
08. 2.5.The-Histogram-exercise-solution.xlsx 17.1 KB
08. 2.5.The-Histogram-exercise.xlsx 15.5 KB
08. Statistics-PDF-with-Excel-Solutions-that-dont-visualize-properly.pdf 289.1 KB
09. 2.6.Cross-table-and-scatter-plot.xlsx 26.1 KB
10. 2.6.Cross-table-and-scatter-plot-exercise-solution.xlsx 40.4 KB
10. 2.6.Cross-table-and-scatter-plot-exercise.xlsx 16.3 KB
11. 2.7.Mean-median-and-mode-lesson.xlsx 10.5 KB
12. 2.7.Mean-median-and-mode-exercise-solution.xlsx 11.4 KB
12. 2.7.Mean-median-and-mode-exercise.xlsx 10.9 KB
13. 2.8.Skewness-lesson.xlsx 34.6 KB
14. 2.8.Skewness-exercise-solution.xlsx 19.8 KB
14. 2.8.Skewness-exercise.xlsx 9.5 KB
15. 2.9.Variance-lesson.xlsx 10.1 KB
16. 2.9.Variance-exercise-solution.xlsx 11.1 KB
16. 2.9.Variance-exercise.xlsx 10.8 KB
17. 2.10.Standard-deviation-and-coefficient-of-variation-lesson.xlsx 11.0 KB
18. 2.10.Standard-deviation-and-coefficient-of-variation-exercise-solution.xlsx 12.6 KB
18. 2.10.Standard-deviation-and-coefficient-of-variation-exercise.xlsx 11.6 KB
19. 2.11.Covariance-lesson.xlsx 24.9 KB
20. 2.11.Covariance-exercise-solution.xlsx 29.5 KB
20. 2.11.Covariance-exercise.xlsx 20.2 KB
22. 2.12.Correlation-exercise-solution.xlsx 29.5 KB
22. 2.12.Correlation-exercise.xlsx 29.3 KB
01. Practical Example Descriptive Statistics.mp4 130.5 MB
01. Practical Example Descriptive Statistics.vtt 21.0 KB
02. Practical Example Descriptive Statistics Exercise.html 81 bytes
01. 2.13.Practical-example.Descriptive-statistics-lesson.xlsx 146.5 KB
02. 2.13.Practical-example.Descriptive-statistics-exercise-solution.xlsx 146.4 KB
02. 2.13.Practical-example.Descriptive-statistics-exercise.xlsx 120.3 KB
01. Introduction.mp4 3.1 MB
01. Introduction.vtt 1.7 KB
02. What is a Distribution.mp4 17.2 MB
02. What is a Distribution.vtt 5.9 KB
03. The Normal Distribution.mp4 13.1 MB
03. The Normal Distribution.vtt 5.2 KB
04. The Standard Normal Distribution.mp4 8.6 MB
04. The Standard Normal Distribution.vtt 4.0 KB
05. The Standard Normal Distribution Exercise.html 81 bytes
06. Central Limit Theorem.mp4 23.2 MB
06. Central Limit Theorem.vtt 5.6 KB
07. Standard error.mp4 13.5 MB
07. Standard error.vtt 2.1 KB
08. Estimators and Estimates.mp4 27.7 MB
08. Estimators and Estimates.vtt 4.0 KB
01. Course-notes-inferential-statistics.pdf 382.3 KB
02. 3.2.What-is-a-distribution-lesson.xlsx 19.5 KB
02. Course-notes-inferential-statistics.pdf 382.3 KB
04. 3.4.Standard-normal-distribution-lesson.xlsx 10.4 KB
05. 3.4.Standard-normal-distribution-exercise-solution.xlsx 24.0 KB
05. 3.4.Standard-normal-distribution-exercise.xlsx 12.0 KB
01. What are Confidence Intervals.mp4 28.6 MB
01. What are Confidence Intervals.vtt 3.2 KB
02. Confidence Intervals; Population Variance Known; Z-score.mp4 52.2 MB
02. Confidence Intervals; Population Variance Known; Z-score.vtt 9.6 KB
03. Confidence Intervals; Population Variance Known; Z-score; Exercise.html 81 bytes
04. Confidence Interval Clarifications.mp4 18.9 MB
04. Confidence Interval Clarifications.vtt 5.6 KB
05. Student's T Distribution.mp4 13.7 MB
05. Student's T Distribution.vtt 4.6 KB
06. Confidence Intervals; Population Variance Unknown; T-score.mp4 13.7 MB
06. Confidence Intervals; Population Variance Unknown; T-score.vtt 5.4 KB
07. Confidence Intervals; Population Variance Unknown; T-score; Exercise.html 81 bytes
08. Margin of Error.mp4 23.1 MB
08. Margin of Error.vtt 6.4 KB
09. Confidence intervals. Two means. Dependent samples.mp4 45.0 MB
09. Confidence intervals. Two means. Dependent samples.vtt 8.5 KB
10. Confidence intervals. Two means. Dependent samples Exercise.html 81 bytes
11. Confidence intervals. Two means. Independent Samples (Part 1).mp4 12.0 MB
11. Confidence intervals. Two means. Independent Samples (Part 1).vtt 6.3 KB
12. Confidence intervals. Two means. Independent Samples (Part 1). Exercise.html 81 bytes
13. Confidence intervals. Two means. Independent Samples (Part 2).mp4 14.6 MB
13. Confidence intervals. Two means. Independent Samples (Part 2).vtt 4.7 KB
14. Confidence intervals. Two means. Independent Samples (Part 2). Exercise.html 81 bytes
15. Confidence intervals. Two means. Independent Samples (Part 3).mp4 6.9 MB
15. Confidence intervals. Two means. Independent Samples (Part 3).vtt 2.0 KB
02. 3.9.Population-variance-known-z-score-lesson.xlsx 11.2 KB
02. 3.9.The-z-table.xlsx 25.6 KB
03. 3.9.Population-variance-known-z-score-exercise-solution.xlsx 11.2 KB
03. 3.9.Population-variance-known-z-score-exercise.xlsx 10.8 KB
03. 3.9.The-z-table.xlsx 25.6 KB
06. 3.11.Population-variance-unknown-t-score-lesson.xlsx 10.8 KB
06. 3.11.The-t-table.xlsx 15.8 KB
07. 3.11.Population-variance-unknown-t-score-exercise-solution.xlsx 11.1 KB
07. 3.11.Population-variance-unknown-t-score-exercise.xlsx 10.6 KB
07. 3.11.The-t-table.xlsx 15.8 KB
09. 3.13.Confidence-intervals.Two-means.Dependent-samples-lesson.xlsx 10.5 KB
10. 3.13.Confidence-intervals.Two-means.Dependent-samples-exercise-solution.xlsx 14.2 KB
10. 3.13.Confidence-intervals.Two-means.Dependent-samples-exercise.xlsx 13.7 KB
11. 3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-lesson.xlsx 9.8 KB
12. 3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-exercise-solution.xlsx 10.1 KB
12. 3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-exercise.xlsx 9.8 KB
13. 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-lesson.xlsx 9.5 KB
14. 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-exercise-solution.xlsx 9.8 KB
14. 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-exercise.xlsx 9.2 KB
01. Practical Example Inferential Statistics.mp4 69.0 MB
01. Practical Example Inferential Statistics.vtt 13.9 KB
02. Practical Example Inferential Statistics Exercise.html 81 bytes
01. 3.17.Practical-example.Confidence-intervals-lesson.xlsx 1.7 MB
02. 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx 1.8 MB
02. 3.17.Practical-example.Confidence-intervals-exercise.xlsx 1.7 MB
01. Null vs Alternative Hypothesis.mp4 31.9 MB
01. Null vs Alternative Hypothesis.vtt 7.2 KB
02. Further Reading on Null and Alternative Hypothesis.html 2.3 KB
03. Rejection Region and Significance Level.mp4 38.7 MB
03. Rejection Region and Significance Level.vtt 8.6 KB
04. Type I Error and Type II Error.mp4 15.3 MB
04. Type I Error and Type II Error.vtt 5.5 KB
05. Test for the Mean. Population Variance Known.mp4 36.9 MB
05. Test for the Mean. Population Variance Known.vtt 8.1 KB
06. Test for the Mean. Population Variance Known Exercise.html 81 bytes
07. p-value.mp4 33.7 MB
07. p-value.vtt 5.3 KB
08. Test for the Mean. Population Variance Unknown.mp4 19.7 MB
08. Test for the Mean. Population Variance Unknown.vtt 6.0 KB
09. Test for the Mean. Population Variance Unknown Exercise.html 81 bytes
10. Test for the Mean. Dependent Samples.mp4 32.8 MB
10. Test for the Mean. Dependent Samples.vtt 6.8 KB
11. Test for the Mean. Dependent Samples Exercise.html 81 bytes
12. Test for the mean. Independent Samples (Part 1).mp4 15.4 MB
12. Test for the mean. Independent Samples (Part 1).vtt 5.5 KB
13. Test for the mean. Independent Samples (Part 1). Exercise.html 81 bytes
14. Test for the mean. Independent Samples (Part 2).mp4 24.4 MB
14. Test for the mean. Independent Samples (Part 2).vtt 5.4 KB
15. Test for the mean. Independent Samples (Part 2). Exercise.html 81 bytes
01. Course-notes-hypothesis-testing.pdf 656.4 KB
03. Course-notes-hypothesis-testing.pdf 656.4 KB
05. 4.4.Test-for-the-mean.Population-variance-known-lesson.xlsx 11.0 KB
06. 4.4.Test-for-the-mean.Population-variance-known-exercise-solution.xlsx 11.2 KB
06. 4.4.Test-for-the-mean.Population-variance-known-exercise.xlsx 11.0 KB
07. Online-p-value-calculator.pdf 1.2 MB
08. 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx 14.5 KB
09. 4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx 12.6 KB
09. 4.6.Test-for-the-mean.Population-variance-unknown-exercise.xlsx 11.3 KB
10. 4.7.Test-for-the-mean.Dependent-samples-lesson.xlsx 9.8 KB
11. 4.7.Test-for-the-mean.Dependent-samples-exercise-solution.xlsx 14.4 KB
11. 4.7.Test-for-the-mean.Dependent-samples-exercise.xlsx 12.8 KB
12. 4.8.Test-for-the-mean.Independent-samples-Part-1-lesson.xlsx 9.6 KB
13. 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise-solution.xlsx 11.2 KB
13. 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise.xlsx 10.8 KB
14. 4.9.Test-for-the-mean.Independent-samples-Part-2-lesson.xlsx 9.3 KB
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02. Practical Example Hypothesis Testing Exercise.html 81 bytes
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02. Why Python.mp4 12.2 MB
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02. Using Methods.mp4 30.4 MB
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03. Lists with the range() Function.mp4 16.1 MB
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01. Object Oriented Programming.mp4 8.7 MB
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02. Modules and Packages.mp4 2.1 MB
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03. What is the Standard Library.mp4 5.1 MB
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04. Importing Modules in Python.mp4 9.9 MB
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01. Introduction to Regression Analysis.mp4 3.6 MB
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01. Course-notes-regression-analysis.pdf 312.2 KB
01. The Linear Regression Model.mp4 13.5 MB
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02. Correlation vs Regression.mp4 3.8 MB
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03. Geometrical Representation of the Linear Regression Model.mp4 2.3 MB
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04. Python Packages Installation.mp4 23.7 MB
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05. First Regression in Python.mp4 29.6 MB
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06. First Regression in Python Exercise.html 1.3 KB
07. Using Seaborn for Graphs.mp4 7.4 MB
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08. How to Interpret the Regression Table.mp4 28.7 MB
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09. Decomposition of Variability.mp4 8.8 MB
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10. What is the OLS.mp4 22.5 MB
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11. R-Squared.mp4 11.2 MB
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02. Adjusted R-Squared.mp4 34.2 MB
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05. OLS Assumptions.mp4 5.3 MB
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06. A1 Linearity.mp4 3.6 MB
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13. Making Predictions with the Linear Regression.mp4 16.3 MB
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02. How are we Going to Approach this Section.mp4 5.3 MB
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10. Feature Selection (F-regression).mp4 20.5 MB
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11. A Note on Calculation of P-values with sklearn.html 372 bytes
12. Creating a Summary Table with P-values.mp4 6.4 MB
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01. Practical Example Linear Regression (Part 1).mp4 84.7 MB
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02. Practical Example Linear Regression (Part 2).mp4 31.9 MB
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01. Introduction to Logistic Regression.mp4 5.9 MB
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02. A Simple Example in Python.mp4 21.9 MB
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06. An Invaluable Coding Tip.mp4 18.8 MB
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10. Binary Predictors in a Logistic Regression.mp4 24.9 MB
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15. Testing the Model.mp4 21.6 MB
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16. Testing the Model - Exercise.html 87 bytes
01. Course-Notes-Logistic-Regression.pdf 335.2 KB
02. 2.01.Admittance.csv 1.6 KB
02. Admittance-with-comments.ipynb 5.3 KB
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02. Course-Notes-Logistic-Regression.pdf 335.2 KB
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05. Building-a-Logistic-Regression-Exercise.ipynb 2.9 KB
05. Building-a-Logistic-Regression-Solution.ipynb 4.4 KB
05. Example-bank-data.csv 6.2 KB
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08. Understanding-Logistic-Regression-Tables-Exercise.ipynb 3.2 KB
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01. Introduction to Cluster Analysis.mp4 14.5 MB
01. Introduction to Cluster Analysis.vtt 5.0 KB
02. Some Examples of Clusters.mp4 35.9 MB
02. Some Examples of Clusters.vtt 6.3 KB
03. Difference between Classification and Clustering.mp4 9.7 MB
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04. Math Prerequisites.mp4 5.3 MB
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01. Course-Notes-Cluster-Analysis.pdf 208.7 KB
02. Course-Notes-Cluster-Analysis.pdf 208.7 KB
01. K-Means Clustering.mp4 10.8 MB
01. K-Means Clustering.vtt 6.6 KB
02. A Simple Example of Clustering.mp4 34.2 MB
02. A Simple Example of Clustering.vtt 9.7 KB
03. A Simple Example of Clustering - Exercise.html 87 bytes
04. Clustering Categorical Data.mp4 10.4 MB
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05. Clustering Categorical Data - Exercise.html 87 bytes
06. How to Choose the Number of Clusters.mp4 26.9 MB
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07. How to Choose the Number of Clusters - Exercise.html 87 bytes
08. Pros and Cons of K-Means Clustering.mp4 11.1 MB
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09. To Standardize or not to Standardize.mp4 10.9 MB
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10. Relationship between Clustering and Regression.mp4 3.5 MB
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11. Market Segmentation with Cluster Analysis (Part 1).mp4 28.0 MB
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12. Market Segmentation with Cluster Analysis (Part 2).mp4 34.1 MB
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13. How is Clustering Useful.mp4 37.4 MB
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14. EXERCISE Species Segmentation with Cluster Analysis (Part 1).html 87 bytes
15. EXERCISE Species Segmentation with Cluster Analysis (Part 2).html 87 bytes
02. 3.01.Country-clusters.csv 200 bytes
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03. A-Simple-Example-of-Clustering-Exercise.ipynb 3.6 KB
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14. Species-Segmentation-with-Cluster-Analysis-Part-1-Exercise.ipynb 4.5 KB
14. Species-Segmentation-with-Cluster-Analysis-Part-1-Solution.ipynb 7.4 KB
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15. Species-Segmentation-with-Cluster-Analysis-Part-2-Exercise.ipynb 10.7 KB
15. Species-Segmentation-with-Cluster-Analysis-Part-2-Solution.ipynb 15.3 KB
01. Types of Clustering.mp4 9.0 MB
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02. Dendrogram.mp4 18.3 MB
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03. Heatmaps.mp4 18.5 MB
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03. Country-clusters-standardized.csv 244 bytes
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01. Traditional data science methods and the role of ChatGPT.mp4 26.2 MB
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02. How to install ChatGPT.mp4 5.2 MB
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03. How ChatGPT can boost your productivity.mp4 5.4 MB
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04. Data Preprocessing with ChatGPT.mp4 28.7 MB
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05. First attempt at machine learning with ChatGPT.mp4 36.7 MB
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06. Analyzing a client database with ChatGPT in Python.mp4 21.6 MB
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07. Analyzing a client database with ChatGPT in Python – analyzing top products.mp4 15.2 MB
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09. Exploratory data analysis (EDA) with ChatGPT - histogram and scatter plot.mp4 21.6 MB
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10. Exploratory data analysis (EDA) with ChatGPT - correlation matrix, outlier detec.mp4 33.7 MB
10. Exploratory data analysis (EDA) with ChatGPT - correlation matrix, outlier detec.vtt 7.6 KB
11. Assignment 1.html 1.6 KB
12. Hypothesis testing with ChatGPT.mp4 14.4 MB
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13. Marvels comic book database Intro to Regular Expressions (RegEx).mp4 15.0 MB
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14. Decoding comic book data Python Regular Expressions and ChatGPT.mp4 33.1 MB
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16. Algorithm recommendation Movie Database Analysis with ChatGPT.mp4 17.3 MB
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18. Ethical principles in data and AI utilization.mp4 14.7 MB
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10. Properties-analysis.ipynb 286.5 KB
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12. Students-Hypothesis-Testing.ipynb 5.6 KB
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01. Intro to the Case Study.mp4 10.4 MB
01. Intro to the Case Study.vtt 3.7 KB
02. The Naive Bayes Algorithm.mp4 42.1 MB
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03. Tokenization and Vectorization.mp4 15.8 MB
03. Tokenization and Vectorization.vtt 7.9 KB
04. Imbalanced Data Sets.mp4 6.6 MB
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05. Overcome Imbalanced Data in Machine Learning.mp4 14.6 MB
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06. Loading the Dataset and Preprocessing.mp4 14.8 MB
06. Loading the Dataset and Preprocessing.vtt 3.7 KB
07. Optimizing User Reviews Data Preprocessing & EDA.mp4 18.7 MB
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08. Reg Ex for Analyzing Text Review Data.mp4 16.2 MB
08. Reg Ex for Analyzing Text Review Data.vtt 5.1 KB
09. Understanding Differences between Multinomial and Bernouilli Naive Bayes.mp4 13.9 MB
09. Understanding Differences between Multinomial and Bernouilli Naive Bayes.vtt 5.4 KB
10. Machine Learning with Naïve Bayes (First Attempt).mp4 28.1 MB
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11. Machine Learning with Naïve Bayes – converting the problem to a binary one.mp4 18.9 MB
11. Machine Learning with Naïve Bayes – converting the problem to a binary one.vtt 6.7 KB
12. Testing the Model on New Data.mp4 20.8 MB
12. Testing the Model on New Data.vtt 6.9 KB
12. 365-User-Reviews-Naive-Bayes-Sentiment-Analysis.ipynb 1.7 MB
12. user-courses-review-test-set.csv 19.6 KB
01. What is a Matrix.mp4 11.9 MB
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02. Scalars and Vectors.mp4 8.5 MB
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03. Linear Algebra and Geometry.mp4 13.7 MB
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04. Arrays in Python - A Convenient Way To Represent Matrices.mp4 19.0 MB
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05. What is a Tensor.mp4 15.5 MB
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06. Addition and Subtraction of Matrices.mp4 22.1 MB
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07. Errors when Adding Matrices.mp4 5.8 MB
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08. Transpose of a Matrix.mp4 14.2 MB
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09. Dot Product.mp4 12.8 MB
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10. Dot Product of Matrices.mp4 34.3 MB
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11. Why is Linear Algebra Useful.mp4 88.5 MB
11. Why is Linear Algebra Useful.vtt 11.5 KB
04. Scalars-Vectors-and-Matrices.ipynb 4.5 KB
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01. What to Expect from this Part.mp4 11.7 MB
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01. Introduction to Neural Networks.mp4 10.5 MB
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02. Training the Model.mp4 7.7 MB
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03. Types of Machine Learning.mp4 13.1 MB
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04. The Linear Model (Linear Algebraic Version).mp4 8.0 MB
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05. The Linear Model with Multiple Inputs.mp4 7.9 MB
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06. The Linear model with Multiple Inputs and Multiple Outputs.mp4 16.6 MB
06. The Linear model with Multiple Inputs and Multiple Outputs.vtt 4.8 KB
07. Graphical Representation of Simple Neural Networks.mp4 7.8 MB
07. Graphical Representation of Simple Neural Networks.vtt 2.7 KB
08. What is the Objective Function.mp4 6.2 MB
08. What is the Objective Function.vtt 2.3 KB
09. Common Objective Functions L2-norm Loss.mp4 5.5 MB
09. Common Objective Functions L2-norm Loss.vtt 2.9 KB
10. Common Objective Functions Cross-Entropy Loss.mp4 9.8 MB
10. Common Objective Functions Cross-Entropy Loss.vtt 5.4 KB
11. Optimization Algorithm 1-Parameter Gradient Descent.mp4 23.6 MB
11. Optimization Algorithm 1-Parameter Gradient Descent.vtt 8.8 KB
12. Optimization Algorithm n-Parameter Gradient Descent.mp4 16.8 MB
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01. Course-Notes-Section-2.pdf 578.1 KB
02. Course-Notes-Section-2.pdf 578.1 KB
11. GD-function-example.xlsx 42.3 KB
01. Basic NN Example (Part 1).mp4 9.3 MB
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02. Basic NN Example (Part 2).mp4 15.2 MB
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05. Basic NN Example Exercises.html 1.7 KB
01. Minimal-example-Part-1.ipynb 1.2 KB
01. Shortcuts-for-Jupyter.pdf 619.2 KB
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01. How to Install TensorFlow 2.0.mp4 27.3 MB
01. How to Install TensorFlow 2.0.vtt 6.6 KB
02. TensorFlow Outline and Comparison with Other Libraries.mp4 15.3 MB
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03. TensorFlow 1 vs TensorFlow 2.mp4 15.3 MB
03. TensorFlow 1 vs TensorFlow 2.vtt 4.0 KB
04. A Note on TensorFlow 2 Syntax.mp4 4.6 MB
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05. Types of File Formats Supporting TensorFlow.mp4 8.9 MB
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06. Outlining the Model with TensorFlow 2.mp4 27.0 MB
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07. Interpreting the Result and Extracting the Weights and Bias.mp4 25.9 MB
07. Interpreting the Result and Extracting the Weights and Bias.vtt 6.7 KB
08. Customizing a TensorFlow 2 Model.mp4 16.8 MB
08. Customizing a TensorFlow 2 Model.vtt 4.3 KB
09. Basic NN with TensorFlow Exercises.html 1.3 KB
01. Shortcuts-for-Jupyter.pdf 619.2 KB
05. TensorFlow-Minimal-example-Part1.ipynb 1.7 KB
06. TensorFlow-Minimal-example-Part2.ipynb 9.1 KB
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08. TensorFlow-Minimal-example-complete-with-comments.ipynb 82.3 KB
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09. TensorFlow-Minimal-example-All-exercises.ipynb 83.6 KB
09. TensorFlow-Minimal-example-Exercise-1-Solution.ipynb 28.0 KB
09. TensorFlow-Minimal-Example-Exercise-2-1-Solution.ipynb 83.7 KB
09. TensorFlow-Minimal-Example-Exercise-2-2-Solution.ipynb 77.5 KB
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01. What is a Layer.mp4 5.2 MB
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02. What is a Deep Net.mp4 9.1 MB
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03. Digging into a Deep Net.mp4 23.7 MB
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04. Non-Linearities and their Purpose.mp4 22.5 MB
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05. Activation Functions.mp4 8.8 MB
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06. Activation Functions Softmax Activation.mp4 8.7 MB
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07. Backpropagation.mp4 20.3 MB
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08. Backpropagation Picture.mp4 8.1 MB
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09. Backpropagation - A Peek into the Mathematics of Optimization.html 543 bytes
01. Course-Notes-Section-6.pdf 936.4 KB
02. Course-Notes-Section-6.pdf 936.4 KB
09. Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf 182.4 KB
01. What is Overfitting.mp4 10.8 MB
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02. Underfitting and Overfitting for Classification.mp4 14.0 MB
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03. What is Validation.mp4 8.4 MB
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04. Training, Validation, and Test Datasets.mp4 9.4 MB
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05. N-Fold Cross Validation.mp4 6.2 MB
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06. Early Stopping or When to Stop Training.mp4 10.3 MB
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01. What is Initialization.mp4 8.9 MB
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02. Types of Simple Initializations.mp4 5.7 MB
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03. State-of-the-Art Method - (Xavier) Glorot Initialization.mp4 5.5 MB
03. State-of-the-Art Method - (Xavier) Glorot Initialization.vtt 3.8 KB
01. Stochastic Gradient Descent.mp4 7.8 MB
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02. Problems with Gradient Descent.mp4 3.7 MB
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03. Momentum.mp4 5.2 MB
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04. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp4 17.5 MB
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05. Learning Rate Schedules Visualized.mp4 3.2 MB
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06. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp4 8.5 MB
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07. Adam (Adaptive Moment Estimation).mp4 7.1 MB
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01. Preprocessing Introduction.mp4 9.2 MB
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02. Types of Basic Preprocessing.mp4 3.2 MB
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03. Standardization.mp4 12.1 MB
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04. Preprocessing Categorical Data.mp4 5.4 MB
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05. Binary and One-Hot Encoding.mp4 8.5 MB
05. Binary and One-Hot Encoding.vtt 5.3 KB
01. MNIST The Dataset.mp4 4.5 MB
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02. MNIST How to Tackle the MNIST.mp4 7.9 MB
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03. MNIST Importing the Relevant Packages and Loading the Data.mp4 12.2 MB
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04. MNIST Preprocess the Data - Create a Validation Set and Scale It.mp4 22.9 MB
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05. MNIST Preprocess the Data - Scale the Test Data - Exercise.html 79 bytes
06. MNIST Preprocess the Data - Shuffle and Batch.mp4 32.7 MB
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07. MNIST Preprocess the Data - Shuffle and Batch - Exercise.html 79 bytes
08. MNIST Outline the Model.mp4 22.1 MB
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09. MNIST Select the Loss and the Optimizer.mp4 10.7 MB
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10. MNIST Learning.mp4 31.0 MB
10. MNIST Learning.vtt 8.0 KB
11. MNIST - Exercises.html 2.0 KB
12. MNIST Testing the Model.mp4 22.6 MB
12. MNIST Testing the Model.vtt 6.0 KB
03. TensorFlow-MNIST-Part1-with-comments.ipynb 4.0 KB
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11. 5.TensorFlow-MNIST-Activation-functions-Part-2-Solution.ipynb 14.7 KB
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11. 8.TensorFlow-MNIST-Learning-rate-Part-1-Solution.ipynb 20.6 KB
11. 9.TensorFlow-MNIST-Learning-rate-Part-2-Solution.ipynb 15.8 KB
11. TensorFlow-MNIST-All-Exercises.ipynb 16.7 KB
11. TensorFlow-MNIST-around-98-percent-accuracy.ipynb 15.0 KB
12. TensorFlow-MNIST-complete-with-comments.ipynb 14.5 KB
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01. Business Case Exploring the Dataset and Identifying Predictors.mp4 51.3 MB
01. Business Case Exploring the Dataset and Identifying Predictors.vtt 11.0 KB
02. Business Case Outlining the Solution.mp4 3.0 MB
02. Business Case Outlining the Solution.vtt 1.9 KB
03. Business Case Balancing the Dataset.mp4 22.3 MB
03. Business Case Balancing the Dataset.vtt 4.3 KB
04. Business Case Preprocessing the Data.mp4 73.8 MB
04. Business Case Preprocessing the Data.vtt 13.6 KB
05. Business Case Preprocessing the Data - Exercise.html 370 bytes
06. Business Case Load the Preprocessed Data.mp4 13.8 MB
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07. Business Case Load the Preprocessed Data - Exercise.html 79 bytes
08. Business Case Learning and Interpreting the Result.mp4 29.4 MB
08. Business Case Learning and Interpreting the Result.vtt 6.3 KB
09. Business Case Setting an Early Stopping Mechanism.mp4 43.8 MB
09. Business Case Setting an Early Stopping Mechanism.vtt 8.1 KB
10. Setting an Early Stopping Mechanism - Exercise.html 192 bytes
11. Business Case Testing the Model.mp4 8.2 MB
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12. Business Case Final Exercise.html 433 bytes
01. Audiobooks-data.csv 710.8 KB
04. TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb 11.2 KB
04. TensorFlow-Audiobooks-Preprocessing.ipynb 5.6 KB
05. TensorFlow-Audiobooks-Preprocessing-Exercise-Solution.ipynb 10.0 KB
05. TensorFlow-Audiobooks-Preprocessing-Exercise.ipynb 8.6 KB
07. TensorFlow-Audiobooks-Machine-Learning-Part1-with-comments.ipynb 4.6 KB
08. TensorFlow-Audiobooks-Machine-Learning-Part2-with-comments.ipynb 19.7 KB
09. TensorFlow-Audiobooks-Machine-Learning-Part3-with-comments.ipynb 10.1 KB
11. TensorFlow-Audiobooks-Machine-Learning-with-comments.ipynb 12.0 KB
12. TensorFlow-Audiobooks-Machine-Learning-with-comments.ipynb 12.0 KB
01. Summary on What You've Learned.mp4 9.8 MB
01. Summary on What You've Learned.vtt 5.5 KB
02. What's Further out there in terms of Machine Learning.mp4 4.8 MB
02. What's Further out there in terms of Machine Learning.vtt 2.7 KB
03. DeepMind and Deep Learning.html 1.1 KB
04. An overview of CNNs.mp4 13.4 MB
04. An overview of CNNs.vtt 6.4 KB
05. An Overview of RNNs.mp4 7.0 MB
05. An Overview of RNNs.vtt 4.0 KB
06. An Overview of non-NN Approaches.mp4 16.1 MB
06. An Overview of non-NN Approaches.vtt 5.7 KB
01. READ ME!!!!.html 564 bytes
02. How to Install TensorFlow 1.mp4 5.0 MB
02. How to Install TensorFlow 1.vtt 3.4 KB
03. A Note on Installing Packages in Anaconda.html 2.3 KB
04. TensorFlow Intro.mp4 16.9 MB
04. TensorFlow Intro.vtt 5.3 KB
05. Actual Introduction to TensorFlow.mp4 9.0 MB
05. Actual Introduction to TensorFlow.vtt 2.3 KB
06. Types of File Formats, supporting Tensors.mp4 8.9 MB
06. Types of File Formats, supporting Tensors.vtt 3.4 KB
07. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4 17.7 MB
07. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.vtt 8.0 KB
08. Basic NN Example with TF Loss Function and Gradient Descent.mp4 13.6 MB
08. Basic NN Example with TF Loss Function and Gradient Descent.vtt 4.9 KB
09. Basic NN Example with TF Model Output.mp4 17.1 MB
09. Basic NN Example with TF Model Output.vtt 7.9 KB
10. Basic NN Example with TF Exercises.html 1.6 KB
05. Shortcuts-for-Jupyter.pdf 619.2 KB
06. 5.3.TensorFlow-Minimal-example-Part-1.ipynb 3.4 KB
07. 5.4.TensorFlow-Minimal-example-Part-2.ipynb 6.2 KB
08. 5.5.TensorFlow-Minimal-example-Part-3.ipynb 8.6 KB
09. 5.6.TensorFlow-Minimal-example-complete.ipynb 12.1 KB
10. TensorFlow-Minimal-Example-All-Exercises.ipynb 14.0 KB
10. TensorFlow-Minimal-Example-Exercise-1-Solution.ipynb 23.6 KB
10. TensorFlow-Minimal-Example-Exercise-2-1-Solution.ipynb 25.5 KB
10. TensorFlow-Minimal-Example-Exercise-2-2-Solution.ipynb 25.5 KB
10. TensorFlow-Minimal-Example-Exercise-2-3-Solution.ipynb 50.0 KB
10. TensorFlow-Minimal-Example-Exercise-2-4-Solution.ipynb 21.7 KB
10. TensorFlow-Minimal-Example-Exercise-3-Solution.ipynb 26.7 KB
10. TensorFlow-Minimal-Example-Exercise-4-Solution.ipynb 27.0 KB
01. MNIST What is the MNIST Dataset.mp4 4.8 MB
01. MNIST What is the MNIST Dataset.vtt 3.6 KB
02. MNIST How to Tackle the MNIST.mp4 8.0 MB
02. MNIST How to Tackle the MNIST.vtt 3.8 KB
03. MNIST Relevant Packages.mp4 11.2 MB
03. MNIST Relevant Packages.vtt 2.2 KB
04. MNIST Model Outline.mp4 34.7 MB
04. MNIST Model Outline.vtt 9.2 KB
05. MNIST Loss and Optimization Algorithm.mp4 15.8 MB
05. MNIST Loss and Optimization Algorithm.vtt 3.6 KB
06. Calculating the Accuracy of the Model.mp4 24.4 MB
06. Calculating the Accuracy of the Model.vtt 5.3 KB
07. MNIST Batching and Early Stopping.mp4 9.5 MB
07. MNIST Batching and Early Stopping.vtt 2.8 KB
08. MNIST Learning.mp4 31.8 MB
08. MNIST Learning.vtt 10.5 KB
09. MNIST Results and Testing.mp4 38.1 MB
09. MNIST Results and Testing.vtt 8.2 KB
10. MNIST Exercises.html 2.2 KB
11. MNIST Solutions.html 2.2 KB
03. 12.3.TensorFlow-MNIST-with-comments-Part-1.ipynb 3.9 KB
04. 12.4.TensorFlow-MNIST-with-comments-Part-2.ipynb 6.1 KB
05. 12.5.TensorFlow-MNIST-with-comments-Part-3.ipynb 7.3 KB
06. 12.6.TensorFlow-MNIST-with-comments-Part-4.ipynb 7.9 KB
07. 12.7.TensorFlow-MNIST-with-comments-Part-5.ipynb 8.5 KB
08. 12.8.TensorFlow-MNIST-with-comments-Part-6.ipynb 11.5 KB
09. 12.9.TensorFlow-MNIST-with-comments.ipynb 13.0 KB
10. TensorFlow-MNIST-Exercises-All.ipynb 15.5 KB
11. 0.TensorFlow-MNIST-take-note-of-time-Solution.ipynb 14.0 KB
11. 1.TensorFlow-MNIST-Width-Solution.ipynb 14.0 KB
11. 2.TensorFlow-MNIST-Depth-Solution.ipynb 14.9 KB
11. 3.TensorFlow-MNIST-Width-and-Depth-Solution.ipynb 16.8 KB
11. 4.TensorFlow-MNIST-Activation-functions-Part-1-Solution.ipynb 14.3 KB
11. 5.TensorFlow-MNIST-Activation-functions-Part-2-Solution.ipynb 13.9 KB
11. 6.TensorFlow-MNIST-Batch-size-Part-1-Solution.ipynb 14.3 KB
11. 7.TensorFlow-MNIST-Batch-size-Part-2-Solution.ipynb 14.2 KB
11. 8.TensorFlow-MNIST-Learning-rate-Part-1-Solution.ipynb 14.1 KB
11. 9.TensorFlow-MNIST-Learning-rate-Part-2-Solution.ipynb 15.2 KB
11. TensorFlow-MNIST-around-98-percent-accuracy.ipynb 17.7 KB
01. Business Case Getting Acquainted with the Dataset.mp4 60.3 MB
01. Business Case Getting Acquainted with the Dataset.vtt 11.0 KB
02. Business Case Outlining the Solution.mp4 4.2 MB
02. Business Case Outlining the Solution.vtt 2.6 KB
03. The Importance of Working with a Balanced Dataset.mp4 27.3 MB
03. The Importance of Working with a Balanced Dataset.vtt 4.4 KB
04. Business Case Preprocessing.mp4 74.4 MB
04. Business Case Preprocessing.vtt 13.6 KB
05. Business Case Preprocessing Exercise.html 389 bytes
06. Creating a Data Provider.mp4 56.3 MB
06. Creating a Data Provider.vtt 8.3 KB
07. Business Case Model Outline.mp4 42.5 MB
07. Business Case Model Outline.vtt 7.2 KB
08. Business Case Optimization.mp4 26.9 MB
08. Business Case Optimization.vtt 6.9 KB
09. Business Case Interpretation.mp4 18.6 MB
09. Business Case Interpretation.vtt 3.1 KB
10. Business Case Testing the Model.mp4 4.4 MB
10. Business Case Testing the Model.vtt 2.7 KB
11. Business Case A Comment on the Homework.mp4 20.6 MB
11. Business Case A Comment on the Homework.vtt 5.4 KB
12. Business Case Final Exercise.html 443 bytes
01. Audiobooks-data.csv 710.8 KB
03. Audiobooks-data.csv 710.8 KB
04. Audiobooks-data.csv 710.8 KB
04. TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb 11.2 KB
04. TensorFlow-Audiobooks-Preprocessing.ipynb 5.6 KB
05. Audiobooks-data.csv 710.8 KB
05. TensorFlow-Audiobooks-Preprocessing-Exercise-Solution.ipynb 10.0 KB
05. TensorFlow-Audiobooks-Preprocessing-Exercise.ipynb 8.6 KB
07. TensorFlow-Audiobooks-Outlining-the-model-with-comments.ipynb 10.3 KB
07. TensorFlow-Audiobooks-Outlining-the-model.ipynb 9.4 KB
08. TensorFlow-Audiobooks-optimizing-the-algorithm-with-comments.ipynb 12.7 KB
08. TensorFlow-Audiobooks-optimizing-the-algorithm.ipynb 10.6 KB
09. TensorFlow-Audiobooks-optimizing-the-algorithm-with-comments.ipynb 12.7 KB
09. TensorFlow-Audiobooks-optimizing-the-algorithm.ipynb 10.6 KB
11. Audiobooks-data.csv 710.8 KB
11. TensorFlow-Audiobooks-Machine-learning-Homework.ipynb 14.4 KB
11. TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb 11.2 KB
12. Audiobooks-data.csv 710.8 KB
12. TensorFlow-Audiobooks-Machine-learning-Homework.ipynb 14.4 KB
12. TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb 11.2 KB
01. What are Data, Servers, Clients, Requests, and Responses.mp4 19.5 MB
01. What are Data, Servers, Clients, Requests, and Responses.vtt 6.3 KB
02. What are Data Connectivity, APIs, and Endpoints.mp4 60.2 MB
02. What are Data Connectivity, APIs, and Endpoints.vtt 9.2 KB
03. Taking a Closer Look at APIs.mp4 24.5 MB
03. Taking a Closer Look at APIs.vtt 10.9 KB
04. Communication between Software Products through Text Files.mp4 17.5 MB
04. Communication between Software Products through Text Files.vtt 5.8 KB
05. Software Integration - Explained.mp4 16.0 MB
05. Software Integration - Explained.vtt 7.0 KB
01. Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 19.7 MB
01. Game Plan for this Python, SQL, and Tableau Business Exercise.vtt 5.6 KB
02. The Business Task.mp4 11.3 MB
02. The Business Task.vtt 4.1 KB
03. Introducing the Data Set.mp4 24.2 MB
03. Introducing the Data Set.vtt 4.3 KB
01. What to Expect from the Following Sections.html 2.5 KB
02. Importing the Absenteeism Data in Python.mp4 19.5 MB
02. Importing the Absenteeism Data in Python.vtt 4.0 KB
03. Checking the Content of the Data Set.mp4 54.0 MB
03. Checking the Content of the Data Set.vtt 7.1 KB
04. Introduction to Terms with Multiple Meanings.mp4 18.0 MB
04. Introduction to Terms with Multiple Meanings.vtt 4.3 KB
05. What's Regression Analysis - a Quick Refresher.html 2.8 KB
06. Using a Statistical Approach towards the Solution to the Exercise.mp4 9.9 MB
06. Using a Statistical Approach towards the Solution to the Exercise.vtt 3.0 KB
07. Dropping a Column from a DataFrame in Python.mp4 41.2 MB
07. Dropping a Column from a DataFrame in Python.vtt 8.2 KB
08. EXERCISE - Dropping a Column from a DataFrame in Python.html 870 bytes
09. SOLUTION - Dropping a Column from a DataFrame in Python.html 114 bytes
10. Analyzing the Reasons for Absence.mp4 27.6 MB
10. Analyzing the Reasons for Absence.vtt 6.0 KB
11. Obtaining Dummies from a Single Feature.mp4 69.8 MB
11. Obtaining Dummies from a Single Feature.vtt 10.6 KB
12. EXERCISE - Obtaining Dummies from a Single Feature.html 129 bytes
13. SOLUTION - Obtaining Dummies from a Single Feature.html 117 bytes
14. Dropping a Dummy Variable from the Data Set.html 2.3 KB
15. More on Dummy Variables A Statistical Perspective.mp4 5.8 MB
15. More on Dummy Variables A Statistical Perspective.vtt 1.7 KB
16. Classifying the Various Reasons for Absence.mp4 51.3 MB
16. Classifying the Various Reasons for Absence.vtt 10.5 KB
17. Using .concat() in Python.mp4 27.3 MB
17. Using .concat() in Python.vtt 5.2 KB
18. EXERCISE - Using .concat() in Python.html 189 bytes
19. SOLUTION - Using .concat() in Python.html 143 bytes
20. Reordering Columns in a Pandas DataFrame in Python.mp4 10.0 MB
20. Reordering Columns in a Pandas DataFrame in Python.vtt 1.9 KB
21. EXERCISE - Reordering Columns in a Pandas DataFrame in Python.html 167 bytes
22. SOLUTION - Reordering Columns in a Pandas DataFrame in Python.html 478 bytes
23. Creating Checkpoints while Coding in Jupyter.mp4 17.3 MB
23. Creating Checkpoints while Coding in Jupyter.vtt 3.7 KB
24. EXERCISE - Creating Checkpoints while Coding in Jupyter.html 137 bytes
25. SOLUTION - Creating Checkpoints while Coding in Jupyter.html 118 bytes
26. Analyzing the Dates from the Initial Data Set.mp4 40.1 MB
26. Analyzing the Dates from the Initial Data Set.vtt 8.9 KB
27. Extracting the Month Value from the Date Column.mp4 33.9 MB
27. Extracting the Month Value from the Date Column.vtt 8.0 KB
28. Extracting the Day of the Week from the Date Column.mp4 19.1 MB
28. Extracting the Day of the Week from the Date Column.vtt 4.8 KB
29. EXERCISE - Removing the Date Column.html 1.2 KB
30. Analyzing Several Straightforward Columns for this Exercise.mp4 14.3 MB
30. Analyzing Several Straightforward Columns for this Exercise.vtt 4.6 KB
31. Working on Education, Children, and Pets.mp4 27.0 MB
31. Working on Education, Children, and Pets.vtt 6.0 KB
32. Final Remarks of this Section.mp4 13.5 MB
32. Final Remarks of this Section.vtt 2.7 KB
33. A Note on Exporting Your Data as a .csv File.html 883 bytes
01. Absenteeism-data.csv 32.0 KB
01. data-preprocessing-homework.pdf 134.5 KB
01. df-preprocessed.csv 29.1 KB
23. Absenteeism-Exercise-Preprocessing-df-reason-mod.ipynb 4.8 KB
29. Absenteeism-Exercise-Preprocessing-ChP-df-date-reason-mod.ipynb 7.3 KB
29. Absenteeism-Exercise-Preprocessing-LECTURES.ipynb 7.6 MB
29. Absenteeism-Exercise-Removing-the-Date-Column-SOLUTION.ipynb 8.3 KB
32. Absenteeism-Exercise-EXERCISES-and-SOLUTIONS.ipynb 4.1 KB
32. Absenteeism-Exercise-Preprocessing-df-preprocessed.ipynb 8.5 KB
01. Exploring the Problem with a Machine Learning Mindset.mp4 13.0 MB
01. Exploring the Problem with a Machine Learning Mindset.vtt 4.8 KB
02. Creating the Targets for the Logistic Regression.mp4 32.4 MB
02. Creating the Targets for the Logistic Regression.vtt 8.7 KB
03. Selecting the Inputs for the Logistic Regression.mp4 8.7 MB
03. Selecting the Inputs for the Logistic Regression.vtt 3.6 KB
04. Standardizing the Data.mp4 15.1 MB
04. Standardizing the Data.vtt 4.3 KB
05. Splitting the Data for Training and Testing.mp4 36.1 MB
05. Splitting the Data for Training and Testing.vtt 8.5 KB
06. Fitting the Model and Assessing its Accuracy.mp4 15.2 MB
06. Fitting the Model and Assessing its Accuracy.vtt 7.3 KB
07. Creating a Summary Table with the Coefficients and Intercept.mp4 27.0 MB
07. Creating a Summary Table with the Coefficients and Intercept.vtt 6.4 KB
08. Interpreting the Coefficients for Our Problem.mp4 41.1 MB
08. Interpreting the Coefficients for Our Problem.vtt 8.5 KB
09. Standardizing only the Numerical Variables (Creating a Custom Scaler).mp4 16.9 MB
09. Standardizing only the Numerical Variables (Creating a Custom Scaler).vtt 5.2 KB
10. Interpreting the Coefficients of the Logistic Regression.mp4 15.2 MB
10. Interpreting the Coefficients of the Logistic Regression.vtt 7.5 KB
11. Backward Elimination or How to Simplify Your Model.mp4 31.8 MB
11. Backward Elimination or How to Simplify Your Model.vtt 5.4 KB
12. Testing the Model We Created.mp4 31.6 MB
12. Testing the Model We Created.vtt 6.5 KB
13. Saving the Model and Preparing it for Deployment.mp4 25.5 MB
13. Saving the Model and Preparing it for Deployment.vtt 5.8 KB
14. ARTICLE - A Note on 'pickling'.html 2.1 KB
15. EXERCISE - Saving the Model (and Scaler).html 284 bytes
16. Preparing the Deployment of the Model through a Module.mp4 28.6 MB
16. Preparing the Deployment of the Model through a Module.vtt 5.9 KB
01. Absenteeism-preprocessed.csv 29.1 KB
01. Are You Sure You're All Set.html 519 bytes
02. Deploying the 'absenteeism_module' - Part I.mp4 19.7 MB
02. Deploying the 'absenteeism_module' - Part I.vtt 5.0 KB
03. Deploying the 'absenteeism_module' - Part II.mp4 45.1 MB
03. Deploying the 'absenteeism_module' - Part II.vtt 8.0 KB
04. Exporting the Obtained Data Set as a .csv.html 998 bytes
01. Absenteeism-Exercise-Integration.ipynb 62.4 KB
01. absenteeism-module.py 6.6 KB
01. Absenteeism-new-data.csv 1.9 KB
01. model 1.0 KB
01. scaler 1.9 KB
04. Absenteeism-Exercise-Deploying-the-absenteeism-module.ipynb 973 bytes
01. EXERCISE - Age vs Probability.html 385 bytes
02. Analyzing Age vs Probability in Tableau.mp4 38.7 MB
02. Analyzing Age vs Probability in Tableau.vtt 10.2 KB
03. EXERCISE - Reasons vs Probability.html 397 bytes
04. Analyzing Reasons vs Probability in Tableau.mp4 40.3 MB
04. Analyzing Reasons vs Probability in Tableau.vtt 9.7 KB
05. EXERCISE - Transportation Expense vs Probability.html 553 bytes
06. Analyzing Transportation Expense vs Probability in Tableau.mp4 16.5 MB
06. Analyzing Transportation Expense vs Probability in Tableau.vtt 7.6 KB
01. Absenteeism-predictions.csv 2.1 KB
02. Absenteeism-predictions.csv 2.1 KB
01. Using the .format() Method.mp4 25.7 MB
01. Using the .format() Method.vtt 12.7 KB
02. Iterating Over Range Objects.mp4 12.6 MB
02. Iterating Over Range Objects.vtt 6.4 KB
03. Introduction to Nested For Loops.mp4 12.2 MB
03. Introduction to Nested For Loops.vtt 8.5 KB
04. Triple Nested For Loops.mp4 33.0 MB
04. Triple Nested For Loops.vtt 8.5 KB
05. List Comprehensions.mp4 43.2 MB
05. List Comprehensions.vtt 12.8 KB
06. Anonymous (Lambda) Functions.mp4 22.8 MB
06. Anonymous (Lambda) Functions.vtt 10.5 KB
01. Additional-Python-Tools-Exercises.ipynb 11.4 KB
01. Additional-Python-Tools-Lectures.ipynb 13.5 KB
01. Additional-Python-Tools-Solutions.ipynb 25.5 KB
06. Additional-Python-Tools-Exercises.ipynb 11.4 KB
06. Additional-Python-Tools-Lectures.ipynb 13.5 KB
06. Additional-Python-Tools-Solutions.ipynb 25.5 KB
01. Introduction to pandas Series.mp4 25.0 MB
01. Introduction to pandas Series.vtt 10.8 KB
02. A Note on Completing the Upcoming Coding Exercises.html 3.0 KB
03. Working with Methods in Python - Part I.mp4 13.2 MB
03. Working with Methods in Python - Part I.vtt 7.2 KB
04. Working with Methods in Python - Part II.mp4 9.0 MB
04. Working with Methods in Python - Part II.vtt 3.9 KB
05. Parameters and Arguments in pandas.mp4 21.1 MB
05. Parameters and Arguments in pandas.vtt 5.8 KB
06. Using .unique() and .nunique().mp4 24.3 MB
06. Using .unique() and .nunique().vtt 5.8 KB
07. Using .sort_values().mp4 15.2 MB
07. Using .sort_values().vtt 5.6 KB
08. Introduction to pandas DataFrames - Part I.mp4 12.5 MB
08. Introduction to pandas DataFrames - Part I.vtt 7.3 KB
09. Introduction to pandas DataFrames - Part II.mp4 17.8 MB
09. Introduction to pandas DataFrames - Part II.vtt 8.0 KB
10. pandas DataFrames - Common Attributes.mp4 25.6 MB
10. pandas DataFrames - Common Attributes.vtt 6.6 KB
11. Data Selection in pandas DataFrames.mp4 37.3 MB
11. Data Selection in pandas DataFrames.vtt 10.5 KB
12. pandas DataFrames - Indexing with .iloc[].mp4 32.2 MB
12. pandas DataFrames - Indexing with .iloc[].vtt 8.3 KB
13. pandas DataFrames - Indexing with .loc[].mp4 20.7 MB
13. pandas DataFrames - Indexing with .loc[].vtt 5.6 KB
01. Lending-company.csv 112.4 KB
01. Location.csv 13.5 KB
01. pandas-Fundamentals-Exercises.ipynb 31.0 KB
01. pandas-Fundamentals-Lectures.ipynb 21.3 KB
01. pandas-Fundamentals-Solutions.ipynb 118.3 KB
01. Region.csv 10.2 KB
01. Sales-products.csv 152.3 KB
13. Lending-company.csv 112.4 KB
13. Location.csv 13.5 KB
13. pandas-Fundamentals-Exercises.ipynb 31.0 KB
13. pandas-Fundamentals-Lectures.ipynb 21.3 KB
13. pandas-Fundamentals-Solutions.ipynb 118.3 KB
13. Region.csv 10.2 KB
13. Sales-products.csv 152.3 KB
01. Bonus Lecture Next Steps.html 4.3 KB
01. 365-Data-Science-Data-Science-Interview-Questions-Guide.pdf 15.6 MB

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