01 - Part 1_ Introduction/001 A Practical Example_ What You Will Learn in This Course.mp413.08MB

01 - Part 1_ Introduction/002 What Does the Course Cover.mp449.69MB

02 - The Field of Data Science - The Various Data Science Disciplines/001 Data Science and Business Buzzwords_ Why are there so Many_.mp454.72MB

02 - The Field of Data Science - The Various Data Science Disciplines/002 What is the difference between Analysis and Analytics.mp48.01MB

02 - The Field of Data Science - The Various Data Science Disciplines/003 Business Analytics, Data Analytics, and Data Science_ An Introduction.mp449.96MB

02 - The Field of Data Science - The Various Data Science Disciplines/004 Continuing with BI, ML, and AI.mp435.94MB

02 - The Field of Data Science - The Various Data Science Disciplines/005 A Breakdown of our Data Science Infographic.mp433.95MB

03 - The Field of Data Science - Connecting the Data Science Disciplines/001 Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp421.73MB

04 - The Field of Data Science - The Benefits of Each Discipline/001 The Reason Behind These Disciplines.mp412.41MB

05 - The Field of Data Science - Popular Data Science Techniques/001 Techniques for Working with Traditional Data.mp4105.52MB

05 - The Field of Data Science - Popular Data Science Techniques/002 Real Life Examples of Traditional Data.mp413.92MB

05 - The Field of Data Science - Popular Data Science Techniques/003 Techniques for Working with Big Data.mp460.48MB

05 - The Field of Data Science - Popular Data Science Techniques/004 Real Life Examples of Big Data.mp44.21MB

05 - The Field of Data Science - Popular Data Science Techniques/005 Business Intelligence (BI) Techniques.mp451.34MB

05 - The Field of Data Science - Popular Data Science Techniques/006 Real Life Examples of Business Intelligence (BI).mp419.35MB

05 - The Field of Data Science - Popular Data Science Techniques/007 Techniques for Working with Traditional Methods.mp474.75MB

05 - The Field of Data Science - Popular Data Science Techniques/008 Real Life Examples of Traditional Methods.mp421.17MB

05 - The Field of Data Science - Popular Data Science Techniques/009 Machine Learning (ML) Techniques.mp447.78MB

05 - The Field of Data Science - Popular Data Science Techniques/010 Types of Machine Learning.mp461.78MB

05 - The Field of Data Science - Popular Data Science Techniques/011 Real Life Examples of Machine Learning (ML).mp422.44MB

06 - The Field of Data Science - Popular Data Science Tools/001 Necessary Programming Languages and Software Used in Data Science.mp419.54MB

07 - The Field of Data Science - Careers in Data Science/001 Finding the Job - What to Expect and What to Look for.mp49.48MB

08 - The Field of Data Science - Debunking Common Misconceptions/001 Debunking Common Misconceptions.mp416.43MB

09 - Part 2_ Probability/001 The Basic Probability Formula.mp429.13MB

09 - Part 2_ Probability/002 Computing Expected Values.mp429.24MB

09 - Part 2_ Probability/003 Frequency.mp436.39MB

09 - Part 2_ Probability/004 Events and Their Complements.mp411.4MB

10 - Probability - Combinatorics/001 Fundamentals of Combinatorics.mp43.21MB

10 - Probability - Combinatorics/002 Permutations and How to Use Them.mp413.97MB

10 - Probability - Combinatorics/003 Simple Operations with Factorials.mp413.98MB

10 - Probability - Combinatorics/004 Solving Variations with Repetition.mp413.75MB

10 - Probability - Combinatorics/005 Solving Variations without Repetition.mp414.76MB

10 - Probability - Combinatorics/006 Solving Combinations.mp418.99MB

10 - Probability - Combinatorics/007 Symmetry of Combinations.mp413.51MB

10 - Probability - Combinatorics/008 Solving Combinations with Separate Sample Spaces.mp412.87MB

10 - Probability - Combinatorics/009 Combinatorics in Real-Life_ The Lottery.mp416.16MB

10 - Probability - Combinatorics/010 A Recap of Combinatorics.mp412MB

10 - Probability - Combinatorics/011 A Practical Example of Combinatorics.mp442.24MB

11 - Probability - Bayesian Inference/001 Sets and Events.mp417.44MB

11 - Probability - Bayesian Inference/002 Ways Sets Can Interact.mp419.02MB

11 - Probability - Bayesian Inference/003 Intersection of Sets.mp48.78MB

11 - Probability - Bayesian Inference/004 Union of Sets.mp419.47MB

11 - Probability - Bayesian Inference/005 Mutually Exclusive Sets.mp45.25MB

11 - Probability - Bayesian Inference/006 Dependence and Independence of Sets.mp411.98MB

11 - Probability - Bayesian Inference/007 The Conditional Probability Formula.mp416.33MB

11 - Probability - Bayesian Inference/008 The Law of Total Probability.mp411.39MB

11 - Probability - Bayesian Inference/009 The Additive Rule.mp410.89MB

11 - Probability - Bayesian Inference/010 The Multiplication Law.mp419.8MB

11 - Probability - Bayesian Inference/011 Bayes' Law.mp420.94MB

11 - Probability - Bayesian Inference/012 A Practical Example of Bayesian Inference.mp4125.49MB

12 - Probability - Distributions/001 Fundamentals of Probability Distributions.mp419.28MB

12 - Probability - Distributions/002 Types of Probability Distributions.mp428.69MB

12 - Probability - Distributions/003 Characteristics of Discrete Distributions.mp49.25MB

12 - Probability - Distributions/004 Discrete Distributions_ The Uniform Distribution.mp410.08MB

12 - Probability - Distributions/005 Discrete Distributions_ The Bernoulli Distribution.mp414.76MB

12 - Probability - Distributions/006 Discrete Distributions_ The Binomial Distribution.mp424.94MB

12 - Probability - Distributions/007 Discrete Distributions_ The Poisson Distribution.mp414.62MB

12 - Probability - Distributions/008 Characteristics of Continuous Distributions.mp428.87MB

12 - Probability - Distributions/009 Continuous Distributions_ The Normal Distribution.mp419.67MB

12 - Probability - Distributions/010 Continuous Distributions_ The Standard Normal Distribution.mp420.72MB

12 - Probability - Distributions/011 Continuous Distributions_ The Students' T Distribution.mp45.44MB

12 - Probability - Distributions/012 Continuous Distributions_ The Chi-Squared Distribution.mp410.95MB

12 - Probability - Distributions/013 Continuous Distributions_ The Exponential Distribution.mp415.76MB

12 - Probability - Distributions/014 Continuous Distributions_ The Logistic Distribution.mp415.95MB

12 - Probability - Distributions/015 A Practical Example of Probability Distributions.mp4138.31MB

13 - Probability - Probability in Other Fields/001 Probability in Finance.mp439.66MB

13 - Probability - Probability in Other Fields/002 Probability in Statistics.mp414.26MB

13 - Probability - Probability in Other Fields/003 Probability in Data Science.mp423.94MB

14 - Part 3_ Statistics/001 Population and Sample.mp410.89MB

15 - Statistics - Descriptive Statistics/001 Types of Data.mp442.47MB

15 - Statistics - Descriptive Statistics/002 Levels of Measurement.mp431.44MB

15 - Statistics - Descriptive Statistics/003 Categorical Variables - Visualization Techniques.mp436.65MB

15 - Statistics - Descriptive Statistics/005 Numerical Variables - Frequency Distribution Table.mp412.8MB

15 - Statistics - Descriptive Statistics/007 The Histogram.mp43.85MB

15 - Statistics - Descriptive Statistics/009 Cross Tables and Scatter Plots.mp419.7MB

15 - Statistics - Descriptive Statistics/011 Mean, median and mode.mp417.53MB

15 - Statistics - Descriptive Statistics/013 Skewness.mp49.92MB

15 - Statistics - Descriptive Statistics/015 Variance.mp420.21MB

15 - Statistics - Descriptive Statistics/017 Standard Deviation and Coefficient of Variation.mp420.14MB

15 - Statistics - Descriptive Statistics/019 Covariance.mp418.41MB

15 - Statistics - Descriptive Statistics/021 Correlation Coefficient.mp419.38MB

16 - Statistics - Practical Example_ Descriptive Statistics/001 Practical Example_ Descriptive Statistics.mp437.17MB

17 - Statistics - Inferential Statistics Fundamentals/001 Introduction.mp42.93MB

17 - Statistics - Inferential Statistics Fundamentals/002 What is a Distribution.mp416.9MB

17 - Statistics - Inferential Statistics Fundamentals/003 The Normal Distribution.mp416.16MB

17 - Statistics - Inferential Statistics Fundamentals/004 The Standard Normal Distribution.mp48.62MB

17 - Statistics - Inferential Statistics Fundamentals/006 Central Limit Theorem.mp422.86MB

17 - Statistics - Inferential Statistics Fundamentals/007 Standard error.mp413.33MB

17 - Statistics - Inferential Statistics Fundamentals/008 Estimators and Estimates.mp416.13MB

18 - Statistics - Inferential Statistics_ Confidence Intervals/001 What are Confidence Intervals_.mp428.38MB

18 - Statistics - Inferential Statistics_ Confidence Intervals/002 Confidence Intervals; Population Variance Known; Z-score.mp452.21MB

18 - Statistics - Inferential Statistics_ Confidence Intervals/004 Confidence Interval Clarifications.mp418.56MB

18 - Statistics - Inferential Statistics_ Confidence Intervals/005 Student's T Distribution.mp413.33MB

18 - Statistics - Inferential Statistics_ Confidence Intervals/006 Confidence Intervals; Population Variance Unknown; T-score.mp411.58MB

18 - Statistics - Inferential Statistics_ Confidence Intervals/008 Margin of Error.mp422.66MB

18 - Statistics - Inferential Statistics_ Confidence Intervals/009 Confidence intervals. Two means. Dependent samples.mp445.04MB

18 - Statistics - Inferential Statistics_ Confidence Intervals/011 Confidence intervals. Two means. Independent Samples (Part 1).mp412MB

18 - Statistics - Inferential Statistics_ Confidence Intervals/013 Confidence intervals. Two means. Independent Samples (Part 2).mp413.05MB

18 - Statistics - Inferential Statistics_ Confidence Intervals/015 Confidence intervals. Two means. Independent Samples (Part 3).mp44.17MB

19 - Statistics - Practical Example_ Inferential Statistics/001 Practical Example_ Inferential Statistics.mp422.1MB

20 - Statistics - Hypothesis Testing/001 Null vs Alternative Hypothesis.mp480.83MB

20 - Statistics - Hypothesis Testing/003 Rejection Region and Significance Level.mp438.2MB

20 - Statistics - Hypothesis Testing/004 Type I Error and Type II Error.mp418.17MB

20 - Statistics - Hypothesis Testing/005 Test for the Mean. Population Variance Known.mp436.96MB

20 - Statistics - Hypothesis Testing/007 p-value.mp433.08MB

20 - Statistics - Hypothesis Testing/008 Test for the Mean. Population Variance Unknown.mp419.72MB

20 - Statistics - Hypothesis Testing/010 Test for the Mean. Dependent Samples.mp432.8MB

20 - Statistics - Hypothesis Testing/012 Test for the mean. Independent Samples (Part 1).mp47.57MB

20 - Statistics - Hypothesis Testing/014 Test for the mean. Independent Samples (Part 2).mp424.47MB

21 - Statistics - Practical Example_ Hypothesis Testing/001 Practical Example_ Hypothesis Testing.mp416.3MB

22 - Part 4_ Introduction to Python/001 Introduction to Programming.mp414.33MB

22 - Part 4_ Introduction to Python/002 Why Python_.mp411.77MB

22 - Part 4_ Introduction to Python/003 Why Jupyter_.mp47.96MB

22 - Part 4_ Introduction to Python/004 Installing Python and Jupyter.mp432.86MB

22 - Part 4_ Introduction to Python/005 Understanding Jupyter's Interface - the Notebook Dashboard.mp44.39MB

22 - Part 4_ Introduction to Python/006 Prerequisites for Coding in the Jupyter Notebooks.mp415.38MB

23 - Python - Variables and Data Types/001 Variables.mp48.93MB

23 - Python - Variables and Data Types/002 Numbers and Boolean Values in Python.mp44.61MB

23 - Python - Variables and Data Types/003 Python Strings.mp419.74MB

24 - Python - Basic Python Syntax/001 Using Arithmetic Operators in Python.mp47.28MB

24 - Python - Basic Python Syntax/002 The Double Equality Sign.mp42.72MB

24 - Python - Basic Python Syntax/003 How to Reassign Values.mp41.86MB

24 - Python - Basic Python Syntax/004 Add Comments.mp42.41MB

24 - Python - Basic Python Syntax/005 Understanding Line Continuation.mp41014.51KB

24 - Python - Basic Python Syntax/006 Indexing Elements.mp42.36MB

24 - Python - Basic Python Syntax/007 Structuring with Indentation.mp42.8MB

25 - Python - Other Python Operators/001 Comparison Operators.mp43.12MB

25 - Python - Other Python Operators/002 Logical and Identity Operators.mp419MB

26 - Python - Conditional Statements/001 The IF Statement.mp45.33MB

26 - Python - Conditional Statements/002 The ELSE Statement.mp45.25MB

26 - Python - Conditional Statements/003 The ELIF Statement.mp414.25MB

26 - Python - Conditional Statements/004 A Note on Boolean Values.mp43.26MB

27 - Python - Python Functions/001 Defining a Function in Python.mp43.23MB

27 - Python - Python Functions/002 How to Create a Function with a Parameter.mp48.29MB

27 - Python - Python Functions/003 Defining a Function in Python - Part II.mp46.45MB

27 - Python - Python Functions/004 How to Use a Function within a Function.mp43.25MB

27 - Python - Python Functions/005 Conditional Statements and Functions.mp46.04MB

27 - Python - Python Functions/006 Functions Containing a Few Arguments.mp42.24MB

27 - Python - Python Functions/007 Built-in Functions in Python.mp48.5MB

28 - Python - Sequences/001 Lists.mp420.5MB

28 - Python - Sequences/002 Using Methods.mp423.42MB

28 - Python - Sequences/003 List Slicing.mp419.17MB

28 - Python - Sequences/004 Tuples.mp49.5MB

28 - Python - Sequences/005 Dictionaries.mp424.91MB

29 - Python - Iterations/001 For Loops.mp423.58MB

29 - Python - Iterations/002 While Loops and Incrementing.mp420.2MB

29 - Python - Iterations/003 Lists with the range() Function.mp414.5MB

29 - Python - Iterations/004 Conditional Statements and Loops.mp421.94MB

29 - Python - Iterations/005 Conditional Statements, Functions, and Loops.mp42.91MB

29 - Python - Iterations/006 How to Iterate over Dictionaries.mp416.46MB

30 - Python - Advanced Python Tools/001 Object Oriented Programming.mp48.42MB

30 - Python - Advanced Python Tools/002 Modules and Packages.mp41.71MB

30 - Python - Advanced Python Tools/003 What is the Standard Library_.mp44.87MB

30 - Python - Advanced Python Tools/004 Importing Modules in Python.mp48.53MB

31 - Part 5_ Advanced Statistical Methods in Python/001 Introduction to Regression Analysis.mp42.92MB

32 - Advanced Statistical Methods - Linear Regression with StatsModels/001 The Linear Regression Model.mp413.16MB

32 - Advanced Statistical Methods - Linear Regression with StatsModels/002 Correlation vs Regression.mp43.75MB

32 - Advanced Statistical Methods - Linear Regression with StatsModels/003 Geometrical Representation of the Linear Regression Model.mp41.75MB

32 - Advanced Statistical Methods - Linear Regression with StatsModels/004 Python Packages Installation.mp423.7MB

32 - Advanced Statistical Methods - Linear Regression with StatsModels/005 First Regression in Python.mp429.63MB

32 - Advanced Statistical Methods - Linear Regression with StatsModels/007 Using Seaborn for Graphs.mp47.37MB

32 - Advanced Statistical Methods - Linear Regression with StatsModels/008 How to Interpret the Regression Table.mp428.72MB

32 - Advanced Statistical Methods - Linear Regression with StatsModels/009 Decomposition of Variability.mp48.62MB

32 - Advanced Statistical Methods - Linear Regression with StatsModels/010 What is the OLS_.mp422.44MB

32 - Advanced Statistical Methods - Linear Regression with StatsModels/011 R-Squared.mp410.79MB

33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/001 Multiple Linear Regression.mp45.54MB

33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/002 Adjusted R-Squared.mp434.22MB

33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/004 Test for Significance of the Model (F-Test).mp45.9MB

33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/005 OLS Assumptions.mp45.12MB

33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/006 A1_ Linearity.mp42.66MB

33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/007 A2_ No Endogeneity.mp48.99MB

33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/008 A3_ Normality and Homoscedasticity.mp427.39MB

33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/009 A4_ No Autocorrelation.mp47.67MB

33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/010 A5_ No Multicollinearity.mp47.36MB

33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/011 Dealing with Categorical Data - Dummy Variables.mp435.09MB

33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/013 Making Predictions with the Linear Regression.mp416.36MB

34 - Advanced Statistical Methods - Linear Regression with sklearn/001 What is sklearn and How is it Different from Other Packages.mp46.24MB

34 - Advanced Statistical Methods - Linear Regression with sklearn/002 How are we Going to Approach this Section_.mp44.03MB

34 - Advanced Statistical Methods - Linear Regression with sklearn/003 Simple Linear Regression with sklearn.mp431.65MB

34 - Advanced Statistical Methods - Linear Regression with sklearn/004 Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp428.88MB

34 - Advanced Statistical Methods - Linear Regression with sklearn/007 Multiple Linear Regression with sklearn.mp49.81MB

34 - Advanced Statistical Methods - Linear Regression with sklearn/008 Calculating the Adjusted R-Squared in sklearn.mp416.92MB

34 - Advanced Statistical Methods - Linear Regression with sklearn/010 Feature Selection (F-regression).mp415.68MB

34 - Advanced Statistical Methods - Linear Regression with sklearn/012 Creating a Summary Table with P-values.mp46.45MB

34 - Advanced Statistical Methods - Linear Regression with sklearn/014 Feature Scaling (Standardization).mp420.37MB

34 - Advanced Statistical Methods - Linear Regression with sklearn/015 Feature Selection through Standardization of Weights.mp427.16MB

34 - Advanced Statistical Methods - Linear Regression with sklearn/016 Predicting with the Standardized Coefficients.mp418.34MB

34 - Advanced Statistical Methods - Linear Regression with sklearn/018 Underfitting and Overfitting.mp45.69MB

34 - Advanced Statistical Methods - Linear Regression with sklearn/019 Train - Test Split Explained.mp435.58MB

35 - Advanced Statistical Methods - Practical Example_ Linear Regression/001 Practical Example_ Linear Regression (Part 1).mp484.84MB

35 - Advanced Statistical Methods - Practical Example_ Linear Regression/002 Practical Example_ Linear Regression (Part 2).mp431.9MB

35 - Advanced Statistical Methods - Practical Example_ Linear Regression/004 Practical Example_ Linear Regression (Part 3).mp46.91MB

35 - Advanced Statistical Methods - Practical Example_ Linear Regression/006 Practical Example_ Linear Regression (Part 4).mp429.84MB

35 - Advanced Statistical Methods - Practical Example_ Linear Regression/008 Practical Example_ Linear Regression (Part 5).mp450.42MB

36 - Advanced Statistical Methods - Logistic Regression/001 Introduction to Logistic Regression.mp44.41MB

36 - Advanced Statistical Methods - Logistic Regression/002 A Simple Example in Python.mp421.91MB

36 - Advanced Statistical Methods - Logistic Regression/003 Logistic vs Logit Function.mp443.96MB

36 - Advanced Statistical Methods - Logistic Regression/004 Building a Logistic Regression.mp48.61MB

36 - Advanced Statistical Methods - Logistic Regression/006 An Invaluable Coding Tip.mp416.77MB

36 - Advanced Statistical Methods - Logistic Regression/007 Understanding Logistic Regression Tables.mp412.89MB

36 - Advanced Statistical Methods - Logistic Regression/009 What do the Odds Actually Mean.mp411.38MB

36 - Advanced Statistical Methods - Logistic Regression/010 Binary Predictors in a Logistic Regression.mp418.47MB

36 - Advanced Statistical Methods - Logistic Regression/012 Calculating the Accuracy of the Model.mp420.28MB

36 - Advanced Statistical Methods - Logistic Regression/014 Underfitting and Overfitting.mp47.25MB

36 - Advanced Statistical Methods - Logistic Regression/015 Testing the Model.mp421.6MB

37 - Advanced Statistical Methods - Cluster Analysis/001 Introduction to Cluster Analysis.mp410.66MB

37 - Advanced Statistical Methods - Cluster Analysis/002 Some Examples of Clusters.mp435.12MB

37 - Advanced Statistical Methods - Cluster Analysis/003 Difference between Classification and Clustering.mp49.53MB

37 - Advanced Statistical Methods - Cluster Analysis/004 Math Prerequisites.mp44.47MB

38 - Advanced Statistical Methods - K-Means Clustering/001 K-Means Clustering.mp410.53MB

38 - Advanced Statistical Methods - K-Means Clustering/002 A Simple Example of Clustering.mp426.08MB

38 - Advanced Statistical Methods - K-Means Clustering/004 Clustering Categorical Data.mp410.35MB

38 - Advanced Statistical Methods - K-Means Clustering/006 How to Choose the Number of Clusters.mp419.79MB

38 - Advanced Statistical Methods - K-Means Clustering/008 Pros and Cons of K-Means Clustering.mp410.93MB

38 - Advanced Statistical Methods - K-Means Clustering/009 To Standardize or not to Standardize.mp410.5MB

38 - Advanced Statistical Methods - K-Means Clustering/010 Relationship between Clustering and Regression.mp42.42MB

38 - Advanced Statistical Methods - K-Means Clustering/011 Market Segmentation with Cluster Analysis (Part 1).mp421.16MB

38 - Advanced Statistical Methods - K-Means Clustering/012 Market Segmentation with Cluster Analysis (Part 2).mp434.08MB

38 - Advanced Statistical Methods - K-Means Clustering/013 How is Clustering Useful_.mp436.49MB

39 - Advanced Statistical Methods - Other Types of Clustering/001 Types of Clustering.mp47.57MB

39 - Advanced Statistical Methods - Other Types of Clustering/002 Dendrogram.mp417.34MB

39 - Advanced Statistical Methods - Other Types of Clustering/003 Heatmaps.mp425.71MB

40 - Part 6_ Mathematics/001 What is a Matrix_.mp411.7MB

40 - Part 6_ Mathematics/002 Scalars and Vectors.mp48.39MB

40 - Part 6_ Mathematics/003 Linear Algebra and Geometry.mp413.56MB

40 - Part 6_ Mathematics/004 Arrays in Python - A Convenient Way To Represent Matrices.mp419.01MB

40 - Part 6_ Mathematics/005 What is a Tensor_.mp411.61MB

40 - Part 6_ Mathematics/006 Addition and Subtraction of Matrices.mp422.08MB

40 - Part 6_ Mathematics/007 Errors when Adding Matrices.mp43.34MB

40 - Part 6_ Mathematics/008 Transpose of a Matrix.mp420.49MB

40 - Part 6_ Mathematics/009 Dot Product.mp411.36MB

40 - Part 6_ Mathematics/010 Dot Product of Matrices.mp426.42MB

40 - Part 6_ Mathematics/011 Why is Linear Algebra Useful_.mp486.18MB

41 - Part 7_ Deep Learning/001 What to Expect from this Part_.mp47.56MB

42 - Deep Learning - Introduction to Neural Networks/001 Introduction to Neural Networks.mp410.37MB

42 - Deep Learning - Introduction to Neural Networks/002 Training the Model.mp47.57MB

42 - Deep Learning - Introduction to Neural Networks/003 Types of Machine Learning.mp49.81MB

42 - Deep Learning - Introduction to Neural Networks/004 The Linear Model (Linear Algebraic Version).mp47.87MB

42 - Deep Learning - Introduction to Neural Networks/005 The Linear Model with Multiple Inputs.mp47.77MB

42 - Deep Learning - Introduction to Neural Networks/006 The Linear model with Multiple Inputs and Multiple Outputs.mp416.23MB

42 - Deep Learning - Introduction to Neural Networks/007 Graphical Representation of Simple Neural Networks.mp46.35MB

42 - Deep Learning - Introduction to Neural Networks/008 What is the Objective Function_.mp46.03MB

42 - Deep Learning - Introduction to Neural Networks/009 Common Objective Functions_ L2-norm Loss.mp44.47MB

42 - Deep Learning - Introduction to Neural Networks/010 Common Objective Functions_ Cross-Entropy Loss.mp49.68MB

42 - Deep Learning - Introduction to Neural Networks/011 Optimization Algorithm_ 1-Parameter Gradient Descent.mp422.7MB

42 - Deep Learning - Introduction to Neural Networks/012 Optimization Algorithm_ n-Parameter Gradient Descent.mp416.35MB

43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/001 Basic NN Example (Part 1).mp45.14MB

43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/002 Basic NN Example (Part 2).mp415.23MB

43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/003 Basic NN Example (Part 3).mp415.68MB

43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/004 Basic NN Example (Part 4).mp430.06MB

44 - Deep Learning - TensorFlow 2.0_ Introduction/001 How to Install TensorFlow 2.0.mp427.34MB

44 - Deep Learning - TensorFlow 2.0_ Introduction/002 TensorFlow Outline and Comparison with Other Libraries.mp414.94MB

44 - Deep Learning - TensorFlow 2.0_ Introduction/003 TensorFlow 1 vs TensorFlow 2.mp414.95MB

44 - Deep Learning - TensorFlow 2.0_ Introduction/004 A Note on TensorFlow 2 Syntax.mp42.34MB

44 - Deep Learning - TensorFlow 2.0_ Introduction/005 Types of File Formats Supporting TensorFlow.mp47.25MB

44 - Deep Learning - TensorFlow 2.0_ Introduction/006 Outlining the Model with TensorFlow 2.mp426.99MB

44 - Deep Learning - TensorFlow 2.0_ Introduction/007 Interpreting the Result and Extracting the Weights and Bias.mp413.67MB

44 - Deep Learning - TensorFlow 2.0_ Introduction/008 Customizing a TensorFlow 2 Model.mp416.78MB

45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/001 What is a Layer_.mp43.47MB

45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/002 What is a Deep Net_.mp411.06MB

45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/003 Digging into a Deep Net.mp419.14MB

45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/004 Non-Linearities and their Purpose.mp49.74MB

45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/005 Activation Functions.mp48.53MB

45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/006 Activation Functions_ Softmax Activation.mp48.42MB

45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/007 Backpropagation.mp419.49MB

45 - Deep Learning - Digging Deeper into NNs_ Introducing Deep Neural Networks/008 Backpropagation Picture.mp47.68MB

46 - Deep Learning - Overfitting/001 What is Overfitting_.mp410.5MB

46 - Deep Learning - Overfitting/002 Underfitting and Overfitting for Classification.mp413.53MB

46 - Deep Learning - Overfitting/003 What is Validation_.mp48.14MB

46 - Deep Learning - Overfitting/004 Training, Validation, and Test Datasets.mp47.74MB

46 - Deep Learning - Overfitting/005 N-Fold Cross Validation.mp45.14MB

46 - Deep Learning - Overfitting/006 Early Stopping or When to Stop Training.mp48.5MB

47 - Deep Learning - Initialization/001 What is Initialization_.mp417.42MB

47 - Deep Learning - Initialization/002 Types of Simple Initializations.mp45.73MB

47 - Deep Learning - Initialization/003 State-of-the-Art Method - (Xavier) Glorot Initialization.mp44.18MB

48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/001 Stochastic Gradient Descent.mp47.62MB

48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/002 Problems with Gradient Descent.mp43.51MB

48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/003 Momentum.mp45.01MB

48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/004 Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp412.03MB

48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/005 Learning Rate Schedules Visualized.mp42.34MB

48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/006 Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp48.24MB

48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/007 Adam (Adaptive Moment Estimation).mp46.88MB

49 - Deep Learning - Preprocessing/001 Preprocessing Introduction.mp48.98MB

49 - Deep Learning - Preprocessing/002 Types of Basic Preprocessing.mp42.4MB

49 - Deep Learning - Preprocessing/003 Standardization.mp411.95MB

49 - Deep Learning - Preprocessing/004 Preprocessing Categorical Data.mp45.34MB

49 - Deep Learning - Preprocessing/005 Binary and One-Hot Encoding.mp48.36MB

50 - Deep Learning - Classifying on the MNIST Dataset/001 MNIST_ The Dataset.mp44.06MB

50 - Deep Learning - Classifying on the MNIST Dataset/002 MNIST_ How to Tackle the MNIST.mp47.66MB

50 - Deep Learning - Classifying on the MNIST Dataset/003 MNIST_ Importing the Relevant Packages and Loading the Data.mp412.24MB

50 - Deep Learning - Classifying on the MNIST Dataset/004 MNIST_ Preprocess the Data - Create a Validation Set and Scale It.mp422.93MB

50 - Deep Learning - Classifying on the MNIST Dataset/006 MNIST_ Preprocess the Data - Shuffle and Batch.mp432.71MB

50 - Deep Learning - Classifying on the MNIST Dataset/008 MNIST_ Outline the Model.mp422.09MB

50 - Deep Learning - Classifying on the MNIST Dataset/009 MNIST_ Select the Loss and the Optimizer.mp410.65MB

50 - Deep Learning - Classifying on the MNIST Dataset/010 MNIST_ Learning.mp431.03MB

50 - Deep Learning - Classifying on the MNIST Dataset/012 MNIST_ Testing the Model.mp422.64MB

51 - Deep Learning - Business Case Example/001 Business Case_ Exploring the Dataset and Identifying Predictors.mp451.38MB

51 - Deep Learning - Business Case Example/002 Business Case_ Outlining the Solution.mp42.21MB

51 - Deep Learning - Business Case Example/003 Business Case_ Balancing the Dataset.mp426.19MB

51 - Deep Learning - Business Case Example/004 Business Case_ Preprocessing the Data.mp473.82MB

51 - Deep Learning - Business Case Example/006 Business Case_ Load the Preprocessed Data.mp413.8MB

51 - Deep Learning - Business Case Example/008 Business Case_ Learning and Interpreting the Result.mp427.77MB

51 - Deep Learning - Business Case Example/009 Business Case_ Setting an Early Stopping Mechanism.mp443.81MB

51 - Deep Learning - Business Case Example/011 Business Case_ Testing the Model.mp48.19MB

52 - Deep Learning - Conclusion/001 Summary on What You've Learned.mp49.66MB

52 - Deep Learning - Conclusion/002 What's Further out there in terms of Machine Learning.mp43.71MB

52 - Deep Learning - Conclusion/004 An overview of CNNs.mp430.47MB

52 - Deep Learning - Conclusion/005 An Overview of RNNs.mp46.75MB

52 - Deep Learning - Conclusion/006 An Overview of non-NN Approaches.mp415.65MB

53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/002 How to Install TensorFlow 1.mp43.71MB

53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/004 TensorFlow Intro.mp416.56MB

53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/005 Actual Introduction to TensorFlow.mp46.17MB

53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/006 Types of File Formats, supporting Tensors.mp48.9MB

53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/007 Basic NN Example with TF_ Inputs, Outputs, Targets, Weights, Biases.mp428MB

53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/008 Basic NN Example with TF_ Loss Function and Gradient Descent.mp415.72MB

53 - Appendix_ Deep Learning - TensorFlow 1_ Introduction/009 Basic NN Example with TF_ Model Output.mp417.09MB

54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/001 MNIST_ What is the MNIST Dataset_.mp44.23MB

54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/002 MNIST_ How to Tackle the MNIST.mp47.68MB

54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/003 MNIST_ Relevant Packages.mp47.88MB

54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/004 MNIST_ Model Outline.mp434.69MB

54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/005 MNIST_ Loss and Optimization Algorithm.mp411.56MB

54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/006 Calculating the Accuracy of the Model.mp416.64MB

54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/007 MNIST_ Batching and Early Stopping.mp48.7MB

54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/008 MNIST_ Learning.mp431.88MB

54 - Appendix_ Deep Learning - TensorFlow 1_ Classifying on the MNIST Dataset/009 MNIST_ Results and Testing.mp438.19MB

55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/001 Business Case_ Getting Acquainted with the Dataset.mp460.26MB

55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/002 Business Case_ Outlining the Solution.mp42.89MB

55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/003 The Importance of Working with a Balanced Dataset.mp421.6MB

55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/004 Business Case_ Preprocessing.mp474.39MB

55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/006 Creating a Data Provider.mp456.23MB

55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/007 Business Case_ Model Outline.mp442.48MB

55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/008 Business Case_ Optimization.mp426.95MB

55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/009 Business Case_ Interpretation.mp418.59MB

55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/010 Business Case_ Testing the Model.mp44.39MB

55 - Appendix_ Deep Learning - TensorFlow 1_ Business Case/011 Business Case_ A Comment on the Homework.mp419.64MB

56 - Software Integration/001 What are Data, Servers, Clients, Requests, and Responses.mp419.17MB

56 - Software Integration/002 What are Data Connectivity, APIs, and Endpoints_.mp458.83MB

56 - Software Integration/003 Taking a Closer Look at APIs.mp465.29MB

56 - Software Integration/004 Communication between Software Products through Text Files.mp49.28MB

56 - Software Integration/005 Software Integration - Explained.mp441.99MB

57 - Case Study - What's Next in the Course_/001 Game Plan for this Python, SQL, and Tableau Business Exercise.mp415.8MB

57 - Case Study - What's Next in the Course_/002 The Business Task.mp46.8MB

57 - Case Study - What's Next in the Course_/003 Introducing the Data Set.mp415.29MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/002 Importing the Absenteeism Data in Python.mp418.03MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/003 Checking the Content of the Data Set.mp454.27MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/004 Introduction to Terms with Multiple Meanings.mp418.04MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/006 Using a Statistical Approach towards the Solution to the Exercise.mp49.9MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/007 Dropping a Column from a DataFrame in Python.mp441.3MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/010 Analyzing the Reasons for Absence.mp427.63MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/011 Obtaining Dummies from a Single Feature.mp463.77MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/015 More on Dummy Variables_ A Statistical Perspective.mp43.18MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/016 Classifying the Various Reasons for Absence.mp451.32MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/017 Using .concat() in Python.mp419.77MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/020 Reordering Columns in a Pandas DataFrame in Python.mp47.18MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/023 Creating Checkpoints while Coding in Jupyter.mp417.34MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/026 Analyzing the Dates from the Initial Data Set.mp440.13MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/027 Extracting the Month Value from the _Date_ Column.mp438.91MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/028 Extracting the Day of the Week from the _Date_ Column.mp49.12MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/030 Analyzing Several _Straightforward_ Columns for this Exercise.mp412.23MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/031 Working on _Education_, _Children_, and _Pets_.mp419.69MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/032 Final Remarks of this Section.mp417.04MB

59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/001 Exploring the Problem with a Machine Learning Mindset.mp411.08MB

59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/002 Creating the Targets for the Logistic Regression.mp432.5MB

59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/003 Selecting the Inputs for the Logistic Regression.mp44.64MB

59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/004 Standardizing the Data.mp415.14MB

59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/005 Splitting the Data for Training and Testing.mp436.12MB

59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/006 Fitting the Model and Assessing its Accuracy.mp435.29MB

59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/007 Creating a Summary Table with the Coefficients and Intercept.mp426.98MB

59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/008 Interpreting the Coefficients for Our Problem.mp434.41MB

59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/009 Standardizing only the Numerical Variables (Creating a Custom Scaler).mp428.02MB

59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/010 Interpreting the Coefficients of the Logistic Regression.mp415.22MB

59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/011 Backward Elimination or How to Simplify Your Model.mp431.96MB

59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/012 Testing the Model We Created.mp431.63MB

59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/013 Saving the Model and Preparing it for Deployment.mp425.52MB

59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/016 Preparing the Deployment of the Model through a Module.mp428.57MB

60 - Case Study - Loading the 'absenteeism_module'/002 Deploying the 'absenteeism_module' - Part I.mp48.38MB

60 - Case Study - Loading the 'absenteeism_module'/003 Deploying the 'absenteeism_module' - Part II.mp425.99MB

61 - Case Study - Analyzing the Predicted Outputs in Tableau/002 Analyzing Age vs Probability in Tableau.mp438.69MB

61 - Case Study - Analyzing the Predicted Outputs in Tableau/004 Analyzing Reasons vs Probability in Tableau.mp440.24MB

61 - Case Study - Analyzing the Predicted Outputs in Tableau/006 Analyzing Transportation Expense vs Probability in Tableau.mp410.87MB

62 - Appendix - Additional Python Tools/001 Using the .format() Method.mp421.67MB

62 - Appendix - Additional Python Tools/002 Iterating Over Range Objects.mp47.85MB

62 - Appendix - Additional Python Tools/003 Introduction to Nested For Loops.mp412.26MB

62 - Appendix - Additional Python Tools/004 Triple Nested For Loops.mp419.4MB

62 - Appendix - Additional Python Tools/005 List Comprehensions.mp443.23MB

62 - Appendix - Additional Python Tools/006 Anonymous (Lambda) Functions.mp433.71MB

63 - Appendix - pandas Fundamentals/001 Introduction to pandas Series.mp422.22MB

63 - Appendix - pandas Fundamentals/002 Working with Methods in Python - Part I.mp416.8MB

63 - Appendix - pandas Fundamentals/003 Working with Methods in Python - Part II.mp45.77MB

63 - Appendix - pandas Fundamentals/004 Parameters and Arguments in pandas.mp415.45MB

63 - Appendix - pandas Fundamentals/005 Using .unique() and .nunique().mp426.33MB

63 - Appendix - pandas Fundamentals/006 Using .sort_values().mp413.2MB

63 - Appendix - pandas Fundamentals/007 Introduction to pandas DataFrames - Part I.mp410.6MB

63 - Appendix - pandas Fundamentals/008 Introduction to pandas DataFrames - Part II.mp417.83MB

63 - Appendix - pandas Fundamentals/009 pandas DataFrames - Common Attributes.mp429.8MB

63 - Appendix - pandas Fundamentals/010 Data Selection in pandas DataFrames.mp437.28MB

63 - Appendix - pandas Fundamentals/011 pandas DataFrames - Indexing with .iloc[].mp423.54MB

63 - Appendix - pandas Fundamentals/012 pandas DataFrames - Indexing with .loc[].mp420.72MB