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种子名称： [DesireCourse.Com] Udemy - The Data Science Course 2019 Complete Data Science Bootcamp
文件类型： 视频
文件数目： 354个文件
文件大小： 13.99 GB
收录时间： 2019-5-4 12:16
已经下载： 3次
资源热度： 128
最近下载： 2024-10-29 16:58

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[DesireCourse.Com] Udemy - The Data Science Course 2019 Complete Data Science Bootcamp.torrent

1. Part 1 Introduction/1. A Practical Example What You Will Learn in This Course.mp449.03MB

1. Part 1 Introduction/2. What Does the Course Cover.mp462.26MB

10. Combinatorics/1. Fundamentals of Combinatorics.mp416.22MB

10. Combinatorics/11. Solving Combinations.mp457.35MB

10. Combinatorics/13. Symmetry of Combinations.mp438.69MB

10. Combinatorics/15. Solving Combinations with Separate Sample Spaces.mp433.15MB

10. Combinatorics/17. Combinatorics in Real-Life The Lottery.mp439.39MB

10. Combinatorics/19. A Recap of Combinatorics.mp440.92MB

10. Combinatorics/20. A Practical Example of Combinatorics.mp4134.15MB

10. Combinatorics/3. Permutations and How to Use Them.mp441.47MB

10. Combinatorics/5. Simple Operations with Factorials.mp436.12MB

10. Combinatorics/7. Solving Variations with Repetition.mp434.01MB

10. Combinatorics/9. Solving Variations without Repetition.mp443.15MB

11. Bayesian Inference/1. Sets and Events.mp453.47MB

11. Bayesian Inference/11. Dependence and Independence of Sets.mp434.78MB

11. Bayesian Inference/13. The Conditional Probability Formula.mp445.86MB

11. Bayesian Inference/15. The Law of Total Probability.mp435.21MB

11. Bayesian Inference/16. The Additive Rule.mp425.74MB

11. Bayesian Inference/18. The Multiplication Law.mp442.91MB

11. Bayesian Inference/20. Bayes' Law.mp459.56MB

11. Bayesian Inference/3. Ways Sets Can Interact.mp445.39MB

11. Bayesian Inference/5. Intersection of Sets.mp426.97MB

11. Bayesian Inference/7. Union of Sets.mp457.2MB

11. Bayesian Inference/9. Mutually Exclusive Sets.mp425.39MB

12. Probability Distributions/1. Fundamentals of Probability Distributions.mp473.4MB

12. Probability Distributions/11. Discrete Distributions The Binomial Distribution.mp465.51MB

12. Probability Distributions/13. Discrete Distributions The Poisson Distribution.mp458.42MB

12. Probability Distributions/15. Characteristics of Continuous Distributions.mp479.78MB

12. Probability Distributions/17. Continuous Distributions The Normal Distribution.mp448.25MB

12. Probability Distributions/19. Continuous Distributions The Standard Normal Distribution.mp447.91MB

12. Probability Distributions/21. Continuous Distributions The Students' T Distribution.mp427.19MB

12. Probability Distributions/23. Continuous Distributions The Chi-Squared Distribution.mp426.35MB

12. Probability Distributions/25. Continuous Distributions The Exponential Distribution.mp440.23MB

12. Probability Distributions/27. Continuous Distributions The Logistic Distribution.mp447.05MB

12. Probability Distributions/3. Types of Probability Distributions.mp492.31MB

12. Probability Distributions/5. Characteristics of Discrete Distributions.mp422.71MB

12. Probability Distributions/7. Discrete Distributions The Uniform Distribution.mp424.4MB

12. Probability Distributions/9. Discrete Distributions The Bernoulli Distribution.mp434.14MB

13. Probability in Other Fields/1. Probability in Finance.mp499.06MB

13. Probability in Other Fields/2. Probability in Statistics.mp477.29MB

13. Probability in Other Fields/3. Probability in Data Science.mp463.5MB

14. Part 3 Statistics/1. Population and Sample.mp458.12MB

15. Statistics - Descriptive Statistics/1. Types of Data.mp472.52MB

15. Statistics - Descriptive Statistics/11. The Histogram.mp413.78MB

15. Statistics - Descriptive Statistics/14. Cross Tables and Scatter Plots.mp439.8MB

15. Statistics - Descriptive Statistics/17. Mean, median and mode.mp437.13MB

15. Statistics - Descriptive Statistics/19. Skewness.mp419.41MB

15. Statistics - Descriptive Statistics/22. Variance.mp450.96MB

15. Statistics - Descriptive Statistics/24. Standard Deviation and Coefficient of Variation.mp445.12MB

15. Statistics - Descriptive Statistics/27. Covariance.mp427.49MB

15. Statistics - Descriptive Statistics/3. Levels of Measurement.mp454.39MB

15. Statistics - Descriptive Statistics/30. Correlation Coefficient.mp429.39MB

15. Statistics - Descriptive Statistics/5. Categorical Variables - Visualization Techniques.mp438.47MB

15. Statistics - Descriptive Statistics/8. Numerical Variables - Frequency Distribution Table.mp425.85MB

16. Statistics - Practical Example Descriptive Statistics/1. Practical Example Descriptive Statistics.mp4160.46MB

17. Statistics - Inferential Statistics Fundamentals/1. Introduction.mp415.51MB

17. Statistics - Inferential Statistics Fundamentals/11. Standard error.mp422.77MB

17. Statistics - Inferential Statistics Fundamentals/13. Estimators and Estimates.mp447.83MB

17. Statistics - Inferential Statistics Fundamentals/2. What is a Distribution.mp461.6MB

17. Statistics - Inferential Statistics Fundamentals/4. The Normal Distribution.mp449.85MB

17. Statistics - Inferential Statistics Fundamentals/6. The Standard Normal Distribution.mp422.51MB

17. Statistics - Inferential Statistics Fundamentals/9. Central Limit Theorem.mp462.89MB

18. Statistics - Inferential Statistics Confidence Intervals/1. What are Confidence Intervals.mp449.98MB

18. Statistics - Inferential Statistics Confidence Intervals/10. Margin of Error.mp459.17MB

18. Statistics - Inferential Statistics Confidence Intervals/12. Confidence intervals. Two means. Dependent samples.mp470.47MB

18. Statistics - Inferential Statistics Confidence Intervals/14. Confidence intervals. Two means. Independent samples (Part 1).mp428.75MB

18. Statistics - Inferential Statistics Confidence Intervals/16. Confidence intervals. Two means. Independent samples (Part 2).mp426.82MB

18. Statistics - Inferential Statistics Confidence Intervals/18. Confidence intervals. Two means. Independent samples (Part 3).mp419.93MB

18. Statistics - Inferential Statistics Confidence Intervals/3. Confidence Intervals; Population Variance Known; z-score.mp478.21MB

18. Statistics - Inferential Statistics Confidence Intervals/5. Confidence Interval Clarifications.mp457.03MB

18. Statistics - Inferential Statistics Confidence Intervals/6. Student's T Distribution.mp435.44MB

18. Statistics - Inferential Statistics Confidence Intervals/8. Confidence Intervals; Population Variance Unknown; t-score.mp432.21MB

19. Statistics - Practical Example Inferential Statistics/1. Practical Example Inferential Statistics.mp4102.67MB

2. The Field of Data Science - The Various Data Science Disciplines/1. Data Science and Business Buzzwords Why are there so many.mp481.42MB

2. The Field of Data Science - The Various Data Science Disciplines/3. What is the difference between Analysis and Analytics.mp453.55MB

2. The Field of Data Science - The Various Data Science Disciplines/5. Business Analytics, Data Analytics, and Data Science An Introduction.mp464.52MB

2. The Field of Data Science - The Various Data Science Disciplines/7. Continuing with BI, ML, and AI.mp4108.99MB

2. The Field of Data Science - The Various Data Science Disciplines/9. A Breakdown of our Data Science Infographic.mp467.74MB

20. Statistics - Hypothesis Testing/1. Null vs Alternative Hypothesis.mp492.05MB

20. Statistics - Hypothesis Testing/10. p-value.mp455.88MB

20. Statistics - Hypothesis Testing/12. Test for the Mean. Population Variance Unknown.mp440.24MB

20. Statistics - Hypothesis Testing/14. Test for the Mean. Dependent Samples.mp450.38MB

20. Statistics - Hypothesis Testing/16. Test for the mean. Independent samples (Part 1).mp429.96MB

20. Statistics - Hypothesis Testing/18. Test for the mean. Independent samples (Part 2).mp436.4MB

20. Statistics - Hypothesis Testing/4. Rejection Region and Significance Level.mp4112.61MB

20. Statistics - Hypothesis Testing/6. Type I Error and Type II Error.mp443.94MB

20. Statistics - Hypothesis Testing/8. Test for the Mean. Population Variance Known.mp454.22MB

21. Statistics - Practical Example Hypothesis Testing/1. Practical Example Hypothesis Testing.mp469.48MB

22. Part 4 Introduction to Python/1. Introduction to Programming.mp458.55MB

22. Part 4 Introduction to Python/11. Python 2 vs Python 3.mp411.27MB

22. Part 4 Introduction to Python/3. Why Python.mp475.08MB

22. Part 4 Introduction to Python/5. Why Jupyter.mp444.32MB

22. Part 4 Introduction to Python/7. Installing Python and Jupyter.mp451.01MB

22. Part 4 Introduction to Python/8. Understanding Jupyter's Interface - the Notebook Dashboard.mp413.8MB

22. Part 4 Introduction to Python/9. Prerequisites for Coding in the Jupyter Notebooks.mp430.58MB

23. Python - Variables and Data Types/1. Variables.mp425.3MB

23. Python - Variables and Data Types/3. Numbers and Boolean Values in Python.mp417.07MB

23. Python - Variables and Data Types/5. Python Strings.mp430.76MB

24. Python - Basic Python Syntax/1. Using Arithmetic Operators in Python.mp418.93MB

24. Python - Basic Python Syntax/10. Indexing Elements.mp45.94MB

24. Python - Basic Python Syntax/12. Structuring with Indentation.mp46.82MB

24. Python - Basic Python Syntax/3. The Double Equality Sign.mp45.99MB

24. Python - Basic Python Syntax/5. How to Reassign Values.mp44MB

24. Python - Basic Python Syntax/7. Add Comments.mp45MB

24. Python - Basic Python Syntax/9. Understanding Line Continuation.mp42.35MB

25. Python - Other Python Operators/1. Comparison Operators.mp410.18MB

25. Python - Other Python Operators/3. Logical and Identity Operators.mp430.06MB

26. Python - Conditional Statements/1. The IF Statement.mp413.62MB

26. Python - Conditional Statements/3. The ELSE Statement.mp413.59MB

26. Python - Conditional Statements/4. The ELIF Statement.mp433.15MB

26. Python - Conditional Statements/5. A Note on Boolean Values.mp411.26MB

27. Python - Python Functions/1. Defining a Function in Python.mp47.75MB

27. Python - Python Functions/2. How to Create a Function with a Parameter.mp423.88MB

27. Python - Python Functions/3. Defining a Function in Python - Part II.mp414.79MB

27. Python - Python Functions/4. How to Use a Function within a Function.mp48.14MB

27. Python - Python Functions/5. Conditional Statements and Functions.mp415.69MB

27. Python - Python Functions/6. Functions Containing a Few Arguments.mp47.58MB

27. Python - Python Functions/7. Built-in Functions in Python.mp422.01MB

28. Python - Sequences/1. Lists.mp421.99MB

28. Python - Sequences/3. Using Methods.mp421.95MB

28. Python - Sequences/5. List Slicing.mp430.77MB

28. Python - Sequences/6. Tuples.mp416.67MB

28. Python - Sequences/7. Dictionaries.mp425.04MB

29. Python - Iterations/1. For Loops.mp411.8MB

29. Python - Iterations/3. While Loops and Incrementing.mp415.45MB

29. Python - Iterations/4. Lists with the range() Function.mp411.42MB

29. Python - Iterations/6. Conditional Statements and Loops.mp416.1MB

29. Python - Iterations/7. Conditional Statements, Functions, and Loops.mp49.48MB

29. Python - Iterations/8. How to Iterate over Dictionaries.mp416.98MB

3. The Field of Data Science - Connecting the Data Science Disciplines/1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4126.87MB

30. Python - Advanced Python Tools/1. Object Oriented Programming.mp433.59MB

30. Python - Advanced Python Tools/3. Modules and Packages.mp48.5MB

30. Python - Advanced Python Tools/5. What is the Standard Library.mp418.03MB

30. Python - Advanced Python Tools/7. Importing Modules in Python.mp419.93MB

31. Part 5 Advanced Statistical Methods in Python/1. Introduction to Regression Analysis.mp417.32MB

32. Advanced Statistical Methods - Linear regression with StatsModels/1. The Linear Regression Model.mp457.37MB

32. Advanced Statistical Methods - Linear regression with StatsModels/10. Using Seaborn for Graphs.mp412.24MB

32. Advanced Statistical Methods - Linear regression with StatsModels/11. How to Interpret the Regression Table.mp444.65MB

32. Advanced Statistical Methods - Linear regression with StatsModels/13. Decomposition of Variability.mp449.66MB

32. Advanced Statistical Methods - Linear regression with StatsModels/15. What is the OLS.mp428.31MB

32. Advanced Statistical Methods - Linear regression with StatsModels/17. R-Squared.mp441.03MB

32. Advanced Statistical Methods - Linear regression with StatsModels/3. Correlation vs Regression.mp414.74MB

32. Advanced Statistical Methods - Linear regression with StatsModels/5. Geometrical Representation of the Linear Regression Model.mp45.13MB

32. Advanced Statistical Methods - Linear regression with StatsModels/7. Python Packages Installation.mp440.59MB

32. Advanced Statistical Methods - Linear regression with StatsModels/8. First Regression in Python.mp444.56MB

33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/1. Multiple Linear Regression.mp421.52MB

33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/11. A2 No Endogeneity.mp435.68MB

33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/13. A3 Normality and Homoscedasticity.mp442.7MB

33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/14. A4 No Autocorrelation.mp431.52MB

33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/16. A5 No Multicollinearity.mp428.71MB

33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/18. Dealing with Categorical Data - Dummy Variables.mp455.67MB

33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/20. Making Predictions with the Linear Regression.mp424.69MB

33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/3. Adjusted R-Squared.mp454.84MB

33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/6. Test for Significance of the Model (F-Test).mp416.43MB

33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/7. OLS Assumptions.mp421.86MB

33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels/9. A1 Linearity.mp412.6MB

34. Advanced Statistical Methods - Linear Regression with sklearn/1. What is sklearn and How is it Different from Other Packages.mp427.26MB

34. Advanced Statistical Methods - Linear Regression with sklearn/10. Feature Selection (F-regression).mp429.52MB

34. Advanced Statistical Methods - Linear Regression with sklearn/12. Creating a Summary Table with p-values.mp412.31MB

34. Advanced Statistical Methods - Linear Regression with sklearn/14. Feature Scaling (Standardization).mp439.09MB

34. Advanced Statistical Methods - Linear Regression with sklearn/15. Feature Selection through Standardization of Weights.mp434.9MB

34. Advanced Statistical Methods - Linear Regression with sklearn/16. Predicting with the Standardized Coefficients.mp425.96MB

34. Advanced Statistical Methods - Linear Regression with sklearn/18. Underfitting and Overfitting.mp416.95MB

34. Advanced Statistical Methods - Linear Regression with sklearn/19. Train - Test Split Explained.mp449.17MB

34. Advanced Statistical Methods - Linear Regression with sklearn/2. How are Going to Approach this Section.mp419.41MB

34. Advanced Statistical Methods - Linear Regression with sklearn/3. Simple Linear Regression with sklearn.mp434.77MB

34. Advanced Statistical Methods - Linear Regression with sklearn/4. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp432.01MB

34. Advanced Statistical Methods - Linear Regression with sklearn/7. Multiple Linear Regression with sklearn.mp420.08MB

34. Advanced Statistical Methods - Linear Regression with sklearn/8. Calculating the Adjusted R-Squared in sklearn.mp430.89MB

35. Advanced Statistical Methods - Practical Example Linear Regression/1. Practical Example Linear Regression (Part 1).mp497.08MB

35. Advanced Statistical Methods - Practical Example Linear Regression/2. Practical Example Linear Regression (Part 2).mp446MB

35. Advanced Statistical Methods - Practical Example Linear Regression/4. Practical Example Linear Regression (Part 3).mp423.7MB

35. Advanced Statistical Methods - Practical Example Linear Regression/6. Practical Example Linear Regression (Part 4).mp456.05MB

35. Advanced Statistical Methods - Practical Example Linear Regression/8. Practical Example Linear Regression (Part 5).mp457.89MB

36. Advanced Statistical Methods - Logistic Regression/1. Introduction to Logistic Regression.mp427.06MB

36. Advanced Statistical Methods - Logistic Regression/10. Binary Predictors in a Logistic Regression.mp438.44MB

36. Advanced Statistical Methods - Logistic Regression/12. Calculating the Accuracy of the Model.mp432.85MB

36. Advanced Statistical Methods - Logistic Regression/14. Underfitting and Overfitting.mp422.3MB

36. Advanced Statistical Methods - Logistic Regression/15. Testing the Model.mp432.28MB

36. Advanced Statistical Methods - Logistic Regression/2. A Simple Example in Python.mp434.7MB

36. Advanced Statistical Methods - Logistic Regression/3. Logistic vs Logit Function.mp486.49MB

36. Advanced Statistical Methods - Logistic Regression/4. Building a Logistic Regression.mp417.1MB

36. Advanced Statistical Methods - Logistic Regression/6. An Invaluable Coding Tip.mp423.06MB

36. Advanced Statistical Methods - Logistic Regression/7. Understanding Logistic Regression Tables.mp430.55MB

36. Advanced Statistical Methods - Logistic Regression/9. What do the Odds Actually Mean.mp432.28MB

37. Advanced Statistical Methods - Cluster Analysis/1. Introduction to Cluster Analysis.mp453.43MB

37. Advanced Statistical Methods - Cluster Analysis/2. Some Examples of Clusters.mp471.54MB

37. Advanced Statistical Methods - Cluster Analysis/3. Difference between Classification and Clustering.mp436.16MB

37. Advanced Statistical Methods - Cluster Analysis/4. Math Prerequisites.mp414.56MB

38. Advanced Statistical Methods - K-Means Clustering/1. K-Means Clustering.mp427.29MB

38. Advanced Statistical Methods - K-Means Clustering/10. Relationship between Clustering and Regression.mp49.93MB

38. Advanced Statistical Methods - K-Means Clustering/11. Market Segmentation with Cluster Analysis (Part 1).mp443.01MB

38. Advanced Statistical Methods - K-Means Clustering/12. Market Segmentation with Cluster Analysis (Part 2).mp456.11MB

38. Advanced Statistical Methods - K-Means Clustering/13. How is Clustering Useful.mp474.46MB

38. Advanced Statistical Methods - K-Means Clustering/2. A Simple Example of Clustering.mp451.83MB

38. Advanced Statistical Methods - K-Means Clustering/4. Clustering Categorical Data.mp421.24MB

38. Advanced Statistical Methods - K-Means Clustering/6. How to Choose the Number of Clusters.mp444.14MB

38. Advanced Statistical Methods - K-Means Clustering/8. Pros and Cons of K-Means Clustering.mp437.7MB

38. Advanced Statistical Methods - K-Means Clustering/9. To Standardize or not to Standardize.mp430.11MB

39. Advanced Statistical Methods - Other Types of Clustering/1. Types of Clustering.mp444.57MB

39. Advanced Statistical Methods - Other Types of Clustering/2. Dendrogram.mp429.06MB

39. Advanced Statistical Methods - Other Types of Clustering/3. Heatmaps.mp429.62MB

4. The Field of Data Science - The Benefits of Each Discipline/1. The Reason behind these Disciplines.mp481.18MB

40. Part 6 Mathematics/1. What is a matrix.mp433.6MB

40. Part 6 Mathematics/10. Addition and Subtraction of Matrices.mp432.62MB

40. Part 6 Mathematics/12. Errors when Adding Matrices.mp411.18MB

40. Part 6 Mathematics/13. Transpose of a Matrix.mp438.08MB

40. Part 6 Mathematics/14. Dot Product.mp424MB

40. Part 6 Mathematics/15. Dot Product of Matrices.mp449.44MB

40. Part 6 Mathematics/16. Why is Linear Algebra Useful.mp4144.34MB

40. Part 6 Mathematics/3. Scalars and Vectors.mp433.85MB

40. Part 6 Mathematics/5. Linear Algebra and Geometry.mp449.79MB

40. Part 6 Mathematics/7. Arrays in Python - A Convenient Way To Represent Matrices.mp426.68MB

40. Part 6 Mathematics/8. What is a Tensor.mp422.53MB

41. Part 7 Deep Learning/1. What to Expect from this Part.mp431.11MB

42. Deep Learning - Introduction to Neural Networks/1. Introduction to Neural Networks.mp442.93MB

42. Deep Learning - Introduction to Neural Networks/11. The Linear model with Multiple Inputs and Multiple Outputs.mp438.32MB

42. Deep Learning - Introduction to Neural Networks/13. Graphical Representation of Simple Neural Networks.mp422.65MB

42. Deep Learning - Introduction to Neural Networks/15. What is the Objective Function.mp417.92MB

42. Deep Learning - Introduction to Neural Networks/17. Common Objective Functions L2-norm Loss.mp423.27MB

42. Deep Learning - Introduction to Neural Networks/19. Common Objective Functions Cross-Entropy Loss.mp437.24MB

42. Deep Learning - Introduction to Neural Networks/21. Optimization Algorithm 1-Parameter Gradient Descent.mp455.63MB

42. Deep Learning - Introduction to Neural Networks/23. Optimization Algorithm n-Parameter Gradient Descent.mp439.42MB

42. Deep Learning - Introduction to Neural Networks/3. Training the Model.mp428.71MB

42. Deep Learning - Introduction to Neural Networks/5. Types of Machine Learning.mp445.1MB

42. Deep Learning - Introduction to Neural Networks/7. The Linear Model (Linear Algebraic Version).mp428.45MB

42. Deep Learning - Introduction to Neural Networks/9. The Linear Model with Multiple Inputs.mp425.12MB

43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1. Basic NN Example (Part 1).mp420.6MB

43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2. Basic NN Example (Part 2).mp434.95MB

43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/3. Basic NN Example (Part 3).mp424.41MB

43. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4. Basic NN Example (Part 4).mp461.13MB

44. Deep Learning - TensorFlow Introduction/1. How to Install TensorFlow.mp414.56MB

44. Deep Learning - TensorFlow Introduction/3. TensorFlow Outline and Logic.mp447.69MB

44. Deep Learning - TensorFlow Introduction/4. Actual Introduction to TensorFlow.mp417.41MB

44. Deep Learning - TensorFlow Introduction/5. Types of File Formats, supporting Tensors.mp420.34MB

44. Deep Learning - TensorFlow Introduction/6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp438.5MB

44. Deep Learning - TensorFlow Introduction/7. Basic NN Example with TF Loss Function and Gradient Descent.mp432.51MB

44. Deep Learning - TensorFlow Introduction/8. Basic NN Example with TF Model Output.mp437.4MB

45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/1. What is a Layer.mp412.51MB

45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/2. What is a Deep Net.mp429.54MB

45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/3. Digging into a Deep Net.mp459.36MB

45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/4. Non-Linearities and their Purpose.mp427.69MB

45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/5. Activation Functions.mp425.09MB

45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/6. Activation Functions Softmax Activation.mp425.92MB

45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/7. Backpropagation.mp434.95MB

45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/8. Backpropagation picture.mp419.51MB

46. Deep Learning - Overfitting/1. What is Overfitting.mp431.09MB

46. Deep Learning - Overfitting/2. Underfitting and Overfitting for Classification.mp425.08MB

46. Deep Learning - Overfitting/3. What is Validation.mp432.71MB

46. Deep Learning - Overfitting/4. Training, Validation, and Test Datasets.mp425.19MB

46. Deep Learning - Overfitting/5. N-Fold Cross Validation.mp420.71MB

46. Deep Learning - Overfitting/6. Early Stopping or When to Stop Training.mp424.17MB

47. Deep Learning - Initialization/1. What is Initialization.mp421.76MB

47. Deep Learning - Initialization/2. Types of Simple Initializations.mp414.32MB

47. Deep Learning - Initialization/3. State-of-the-Art Method - (Xavier) Glorot Initialization.mp417.14MB

48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/1. Stochastic Gradient Descent.mp428.69MB

48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/2. Problems with Gradient Descent.mp411.02MB

48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/3. Momentum.mp416.44MB

48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp429.09MB

48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/5. Learning Rate Schedules Visualized.mp49.11MB

48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp426.35MB

48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/7. Adam (Adaptive Moment Estimation).mp422.35MB

49. Deep Learning - Preprocessing/1. Preprocessing Introduction.mp427.78MB

49. Deep Learning - Preprocessing/2. Types of Basic Preprocessing.mp411.85MB

49. Deep Learning - Preprocessing/3. Standardization.mp450.98MB

49. Deep Learning - Preprocessing/4. Preprocessing Categorical Data.mp418.61MB

49. Deep Learning - Preprocessing/5. Binary and One-Hot Encoding.mp428.94MB

5. The Field of Data Science - Popular Data Science Techniques/1. Techniques for Working with Traditional Data.mp4138.31MB

5. The Field of Data Science - Popular Data Science Techniques/10. Techniques for Working with Traditional Methods.mp4123.51MB

5. The Field of Data Science - Popular Data Science Techniques/12. Real Life Examples of Traditional Methods.mp442.78MB

5. The Field of Data Science - Popular Data Science Techniques/13. Machine Learning (ML) Techniques.mp499.33MB

5. The Field of Data Science - Popular Data Science Techniques/15. Types of Machine Learning.mp4125.14MB

5. The Field of Data Science - Popular Data Science Techniques/17. Real Life Examples of Machine Learning (ML).mp436.82MB

5. The Field of Data Science - Popular Data Science Techniques/3. Real Life Examples of Traditional Data.mp429.93MB

5. The Field of Data Science - Popular Data Science Techniques/4. Techniques for Working with Big Data.mp475.5MB

5. The Field of Data Science - Popular Data Science Techniques/6. Real Life Examples of Big Data.mp422.04MB

5. The Field of Data Science - Popular Data Science Techniques/7. Business Intelligence (BI) Techniques.mp489.94MB

5. The Field of Data Science - Popular Data Science Techniques/9. Real Life Examples of Business Intelligence (BI).mp429.54MB

50. Deep Learning - Classifying on the MNIST Dataset/1. MNIST What is the MNIST Dataset.mp417.82MB

50. Deep Learning - Classifying on the MNIST Dataset/2. MNIST How to Tackle the MNIST.mp422.59MB

50. Deep Learning - Classifying on the MNIST Dataset/3. MNIST Relevant Packages.mp418.91MB

50. Deep Learning - Classifying on the MNIST Dataset/4. MNIST Model Outline.mp456.39MB

50. Deep Learning - Classifying on the MNIST Dataset/5. MNIST Loss and Optimization Algorithm.mp425.87MB

50. Deep Learning - Classifying on the MNIST Dataset/6. Calculating the Accuracy of the Model.mp443.9MB

50. Deep Learning - Classifying on the MNIST Dataset/7. MNIST Batching and Early Stopping.mp412.85MB

50. Deep Learning - Classifying on the MNIST Dataset/8. MNIST Learning.mp446.69MB

50. Deep Learning - Classifying on the MNIST Dataset/9. MNIST Results and Testing.mp462.78MB

51. Deep Learning - Business Case Example/1. Business Case Getting acquainted with the dataset.mp487.65MB

51. Deep Learning - Business Case Example/10. Business Case Testing the Model.mp411.2MB

51. Deep Learning - Business Case Example/11. Business Case A Comment on the Homework.mp436.39MB

51. Deep Learning - Business Case Example/2. Business Case Outlining the Solution.mp412.22MB

51. Deep Learning - Business Case Example/3. The Importance of Working with a Balanced Dataset.mp439.41MB

51. Deep Learning - Business Case Example/4. Business Case Preprocessing.mp4103.42MB

51. Deep Learning - Business Case Example/6. Creating a Data Provider.mp476.35MB

51. Deep Learning - Business Case Example/7. Business Case Model Outline.mp453.12MB

51. Deep Learning - Business Case Example/8. Business Case Optimization.mp441.52MB

51. Deep Learning - Business Case Example/9. Business Case Interpretation.mp425.74MB

52. Deep Learning - Conclusion/1. Summary on What You've Learned.mp439.75MB

52. Deep Learning - Conclusion/2. What's Further out there in terms of Machine Learning.mp420.12MB

52. Deep Learning - Conclusion/3. An overview of CNNs.mp458.8MB

52. Deep Learning - Conclusion/5. An Overview of RNNs.mp425.26MB

52. Deep Learning - Conclusion/6. An Overview of non-NN Approaches.mp444.77MB

53. Software Integration/1. What are Data, Servers, Clients, Requests, and Responses.mp469.04MB

53. Software Integration/3. What are Data Connectivity, APIs, and Endpoints.mp4104.09MB

53. Software Integration/5. Taking a Closer Look at APIs.mp4115.6MB

53. Software Integration/7. Communication between Software Products through Text Files.mp460.35MB

53. Software Integration/9. Software Integration - Explained.mp472.65MB

54. Case Study - What's Next in the Course/1. Game Plan for this Python, SQL, and Tableau Business Exercise.mp452.3MB

54. Case Study - What's Next in the Course/2. The Business Task.mp439.16MB

54. Case Study - What's Next in the Course/3. Introducing the Data Set.mp440.87MB

55. Case Study - Preprocessing the 'Absenteeism_data'/10. Analyzing the Reasons for Absence.mp440.58MB

55. Case Study - Preprocessing the 'Absenteeism_data'/11. Obtaining Dummies from a Single Feature.mp481.11MB

55. Case Study - Preprocessing the 'Absenteeism_data'/15. More on Dummy Variables A Statistical Perspective.mp413.74MB

55. Case Study - Preprocessing the 'Absenteeism_data'/16. Classifying the Various Reasons for Absence.mp474.6MB

55. Case Study - Preprocessing the 'Absenteeism_data'/17. Using .concat() in Python.mp438.74MB

55. Case Study - Preprocessing the 'Absenteeism_data'/2. Importing the Absenteeism Data in Python.mp423.16MB

55. Case Study - Preprocessing the 'Absenteeism_data'/20. Reordering Columns in a Pandas DataFrame in Python.mp414.02MB

55. Case Study - Preprocessing the 'Absenteeism_data'/23. Creating Checkpoints while Coding in Jupyter.mp425.67MB

55. Case Study - Preprocessing the 'Absenteeism_data'/26. Analyzing the Dates from the Initial Data Set.mp457.28MB

55. Case Study - Preprocessing the 'Absenteeism_data'/27. Extracting the Month Value from the Date Column.mp447.8MB

55. Case Study - Preprocessing the 'Absenteeism_data'/28. Extracting the Day of the Week from the Date Column.mp427.96MB

55. Case Study - Preprocessing the 'Absenteeism_data'/3. Checking the Content of the Data Set.mp461.9MB

55. Case Study - Preprocessing the 'Absenteeism_data'/30. Analyzing Several Straightforward Columns for this Exercise.mp429.52MB

55. Case Study - Preprocessing the 'Absenteeism_data'/31. Working on Education, Children, and Pets.mp439.59MB

55. Case Study - Preprocessing the 'Absenteeism_data'/32. Final Remarks of this Section.mp421.63MB

55. Case Study - Preprocessing the 'Absenteeism_data'/4. Introduction to Terms with Multiple Meanings.mp427.85MB

55. Case Study - Preprocessing the 'Absenteeism_data'/6. Using a Statistical Approach towards the Solution to the Exercise.mp420.19MB

55. Case Study - Preprocessing the 'Absenteeism_data'/7. Dropping a Column from a DataFrame in Python.mp461.77MB

56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/1. Exploring the Problem with a Machine Learning Mindset.mp427.54MB

56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/10. Interpreting the Coefficients of the Logistic Regression.mp440.41MB

56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/11. Backward Elimination or How to Simplify Your Model.mp439.56MB

56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/12. Testing the Model We Created.mp449.07MB

56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/13. Saving the Model and Preparing it for Deployment.mp437.45MB

56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/16. Preparing the Deployment of the Model through a Module.mp444.48MB

56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/2. Creating the Targets for the Logistic Regression.mp445.79MB

56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/3. Selecting the Inputs for the Logistic Regression.mp416.75MB

56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/4. Standardizing the Data.mp420.6MB

56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/5. Splitting the Data for Training and Testing.mp452.77MB

56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/6. Fitting the Model and Assessing its Accuracy.mp441.62MB

56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/7. Creating a Summary Table with the Coefficients and Intercept.mp438.88MB

56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/8. Interpreting the Coefficients for Our Problem.mp452.38MB

56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'/9. Standardizing only the Numerical Variables (Creating a Custom Scaler).mp441.19MB

57. Case Study - Loading the 'absenteeism_module'/2. Deploying the 'absenteeism_module' - Part I.mp425.49MB

57. Case Study - Loading the 'absenteeism_module'/3. Deploying the 'absenteeism_module' - Part II.mp454.25MB

58. Case Study - Analyzing the Predicted Outputs in Tableau/2. Analyzing Age vs Probability in Tableau.mp456.56MB

58. Case Study - Analyzing the Predicted Outputs in Tableau/4. Analyzing Reasons vs Probability in Tableau.mp459.34MB

58. Case Study - Analyzing the Predicted Outputs in Tableau/6. Analyzing Transportation Expense vs Probability in Tableau.mp440.63MB

6. The Field of Data Science - Popular Data Science Tools/1. Necessary Programming Languages and Software Used in Data Science.mp4103.51MB

7. The Field of Data Science - Careers in Data Science/1. Finding the Job - What to Expect and What to Look for.mp454.38MB

8. The Field of Data Science - Debunking Common Misconceptions/1. Debunking Common Misconceptions.mp472.85MB

9. Part 2 Probability/1. The Basic Probability Formula.mp485.92MB

9. Part 2 Probability/3. Computing Expected Values.mp475.69MB

9. Part 2 Probability/5. Frequency.mp461.73MB

9. Part 2 Probability/7. Events and Their Complements.mp459.15MB