01 - Part 1 Introduction/001 A Practical Example What You Will Learn in This Course.mp443.94MB

01 - Part 1 Introduction/002 What Does the Course Cover.mp451.36MB

02 - The Field of Data Science - The Various Data Science Disciplines/001 Data Science and Business Buzzwords Why are there so Many.mp457.35MB

02 - The Field of Data Science - The Various Data Science Disciplines/002 What is the difference between Analysis and Analytics.mp411.16MB

02 - The Field of Data Science - The Various Data Science Disciplines/003 Business Analytics, Data Analytics, and Data Science An Introduction.mp452.62MB

02 - The Field of Data Science - The Various Data Science Disciplines/004 Continuing with BI, ML, and AI.mp436.95MB

02 - The Field of Data Science - The Various Data Science Disciplines/005 A Breakdown of our Data Science Infographic.mp445.3MB

04 - The Field of Data Science - The Benefits of Each Discipline/001 The Reason Behind These Disciplines.mp446.7MB

05 - The Field of Data Science - Popular Data Science Techniques/001 Techniques for Working with Traditional Data.mp4107.39MB

05 - The Field of Data Science - Popular Data Science Techniques/002 Real Life Examples of Traditional Data.mp418.37MB

05 - The Field of Data Science - Popular Data Science Techniques/003 Techniques for Working with Big Data.mp462.11MB

05 - The Field of Data Science - Popular Data Science Techniques/004 Real Life Examples of Big Data.mp413.07MB

05 - The Field of Data Science - Popular Data Science Techniques/005 Business Intelligence (BI) Techniques.mp452.88MB

05 - The Field of Data Science - Popular Data Science Techniques/006 Real Life Examples of Business Intelligence (BI).mp424.68MB

05 - The Field of Data Science - Popular Data Science Techniques/007 Techniques for Working with Traditional Methods.mp476.1MB

05 - The Field of Data Science - Popular Data Science Techniques/008 Real Life Examples of Traditional Methods.mp427.41MB

05 - The Field of Data Science - Popular Data Science Techniques/009 Machine Learning (ML) Techniques.mp449.41MB

05 - The Field of Data Science - Popular Data Science Techniques/010 Types of Machine Learning.mp480.57MB

05 - The Field of Data Science - Popular Data Science Techniques/011 Real Life Examples of Machine Learning (ML).mp427.7MB

07 - The Field of Data Science - Careers in Data Science/001 Finding the Job - What to Expect and What to Look for.mp413.81MB

08 - The Field of Data Science - Debunking Common Misconceptions/001 Debunking Common Misconceptions.mp458.81MB

09 - Part 2 Probability/001 The Basic Probability Formula.mp429.39MB

09 - Part 2 Probability/002 Computing Expected Values.mp445.67MB

09 - Part 2 Probability/003 Frequency.mp437.36MB

09 - Part 2 Probability/004 Events and Their Complements.mp425.83MB

10 - Probability - Combinatorics/001 Fundamentals of Combinatorics.mp45.95MB

10 - Probability - Combinatorics/002 Permutations and How to Use Them.mp417.52MB

10 - Probability - Combinatorics/003 Simple Operations with Factorials.mp416.68MB

10 - Probability - Combinatorics/004 Solving Variations with Repetition.mp413.95MB

10 - Probability - Combinatorics/005 Solving Variations without Repetition.mp418.25MB

10 - Probability - Combinatorics/006 Solving Combinations.mp423.67MB

10 - Probability - Combinatorics/007 Symmetry of Combinations.mp413.75MB

10 - Probability - Combinatorics/008 Solving Combinations with Separate Sample Spaces.mp410.65MB

10 - Probability - Combinatorics/009 Combinatorics in Real-Life The Lottery.mp416.39MB

10 - Probability - Combinatorics/010 A Recap of Combinatorics.mp415.02MB

10 - Probability - Combinatorics/011 A Practical Example of Combinatorics.mp442.8MB

11 - Probability - Bayesian Inference/001 Sets and Events.mp417.65MB

11 - Probability - Bayesian Inference/002 Ways Sets Can Interact.mp419.3MB

11 - Probability - Bayesian Inference/003 Intersection of Sets.mp411.01MB

11 - Probability - Bayesian Inference/004 Union of Sets.mp424.2MB

11 - Probability - Bayesian Inference/005 Mutually Exclusive Sets.mp410.59MB

11 - Probability - Bayesian Inference/006 Dependence and Independence of Sets.mp414.92MB

11 - Probability - Bayesian Inference/007 The Conditional Probability Formula.mp416.59MB

11 - Probability - Bayesian Inference/008 The Law of Total Probability.mp411.59MB

11 - Probability - Bayesian Inference/009 The Additive Rule.mp411.11MB

11 - Probability - Bayesian Inference/010 The Multiplication Law.mp420.19MB

11 - Probability - Bayesian Inference/011 Bayes' Law.mp421.34MB

11 - Probability - Bayesian Inference/012 A Practical Example of Bayesian Inference.mp4139.14MB

12 - Probability - Distributions/001 Fundamentals of Probability Distributions.mp419.43MB

12 - Probability - Distributions/002 Types of Probability Distributions.mp435.58MB

12 - Probability - Distributions/003 Characteristics of Discrete Distributions.mp49.42MB

12 - Probability - Distributions/004 Discrete Distributions The Uniform Distribution.mp410.31MB

12 - Probability - Distributions/005 Discrete Distributions The Bernoulli Distribution.mp415.12MB

12 - Probability - Distributions/006 Discrete Distributions The Binomial Distribution.mp430.61MB

12 - Probability - Distributions/007 Discrete Distributions The Poisson Distribution.mp423.93MB

12 - Probability - Distributions/008 Characteristics of Continuous Distributions.mp436.13MB

12 - Probability - Distributions/009 Continuous Distributions The Normal Distribution.mp420.01MB

12 - Probability - Distributions/010 Continuous Distributions The Standard Normal Distribution.mp438.36MB

12 - Probability - Distributions/011 Continuous Distributions The Students' T Distribution.mp49.24MB

12 - Probability - Distributions/012 Continuous Distributions The Chi-Squared Distribution.mp420.97MB

12 - Probability - Distributions/013 Continuous Distributions The Exponential Distribution.mp416MB

12 - Probability - Distributions/014 Continuous Distributions The Logistic Distribution.mp416.17MB

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

13 - Probability - Probability in Other Fields/001 Probability in Finance.mp440.34MB

13 - Probability - Probability in Other Fields/002 Probability in Statistics.mp431.6MB

13 - Probability - Probability in Other Fields/003 Probability in Data Science.mp456.89MB

14 - Part 3 Statistics/001 Population and Sample.mp435.1MB

15 - Statistics - Descriptive Statistics/001 Types of Data.mp443.17MB

15 - Statistics - Descriptive Statistics/002 Levels of Measurement.mp432.18MB

15 - Statistics - Descriptive Statistics/003 Categorical Variables - Visualization Techniques.mp436.64MB

15 - Statistics - Descriptive Statistics/005 Numerical Variables - Frequency Distribution Table.mp417.69MB

15 - Statistics - Descriptive Statistics/007 The Histogram.mp49.57MB

15 - Statistics - Descriptive Statistics/009 Cross Tables and Scatter Plots.mp419.71MB

15 - Statistics - Descriptive Statistics/011 Mean, median and mode.mp424.49MB

15 - Statistics - Descriptive Statistics/013 Skewness.mp413.31MB

15 - Statistics - Descriptive Statistics/015 Variance.mp423.54MB

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

15 - Statistics - Descriptive Statistics/019 Covariance.mp418.38MB

15 - Statistics - Descriptive Statistics/021 Correlation Coefficient.mp419.34MB

16 - Statistics - Practical Example Descriptive Statistics/001 Practical Example Descriptive Statistics.mp4150.18MB

17 - Statistics - Inferential Statistics Fundamentals/001 Introduction.mp43.06MB

17 - Statistics - Inferential Statistics Fundamentals/002 What is a Distribution.mp417.2MB

17 - Statistics - Inferential Statistics Fundamentals/003 The Normal Distribution.mp427.49MB

17 - Statistics - Inferential Statistics Fundamentals/004 The Standard Normal Distribution.mp48.61MB

17 - Statistics - Inferential Statistics Fundamentals/006 Central Limit Theorem.mp423.22MB

17 - Statistics - Inferential Statistics Fundamentals/007 Standard error.mp413.52MB

17 - Statistics - Inferential Statistics Fundamentals/008 Estimators and Estimates.mp427.7MB

18 - Statistics - Inferential Statistics Confidence Intervals/001 What are Confidence Intervals.mp428.62MB

18 - Statistics - Inferential Statistics Confidence Intervals/002 Confidence Intervals; Population Variance Known; Z-score.mp452.16MB

18 - Statistics - Inferential Statistics Confidence Intervals/004 Confidence Interval Clarifications.mp418.94MB

18 - Statistics - Inferential Statistics Confidence Intervals/005 Student's T Distribution.mp413.68MB

18 - Statistics - Inferential Statistics Confidence Intervals/006 Confidence Intervals; Population Variance Unknown; T-score.mp413.69MB

18 - Statistics - Inferential Statistics Confidence Intervals/008 Margin of Error.mp423.1MB

18 - Statistics - Inferential Statistics Confidence Intervals/009 Confidence intervals. Two means. Dependent samples.mp444.97MB

18 - Statistics - Inferential Statistics Confidence Intervals/011 Confidence intervals. Two means. Independent Samples (Part 1).mp411.99MB

18 - Statistics - Inferential Statistics Confidence Intervals/013 Confidence intervals. Two means. Independent Samples (Part 2).mp414.62MB

18 - Statistics - Inferential Statistics Confidence Intervals/015 Confidence intervals. Two means. Independent Samples (Part 3).mp46.89MB

19 - Statistics - Practical Example Inferential Statistics/001 Practical Example Inferential Statistics.mp468.98MB

20 - Statistics - Hypothesis Testing/001 Null vs Alternative Hypothesis.mp483.58MB

20 - Statistics - Hypothesis Testing/003 Rejection Region and Significance Level.mp438.69MB

20 - Statistics - Hypothesis Testing/004 Type I Error and Type II Error.mp418.61MB

20 - Statistics - Hypothesis Testing/005 Test for the Mean. Population Variance Known.mp436.95MB

20 - Statistics - Hypothesis Testing/007 p-value.mp433.78MB

20 - Statistics - Hypothesis Testing/008 Test for the Mean. Population Variance Unknown.mp419.71MB

20 - Statistics - Hypothesis Testing/010 Test for the Mean. Dependent Samples.mp432.79MB

20 - Statistics - Hypothesis Testing/012 Test for the mean. Independent Samples (Part 1).mp415.41MB

20 - Statistics - Hypothesis Testing/014 Test for the mean. Independent Samples (Part 2).mp424.45MB

21 - Statistics - Practical Example Hypothesis Testing/001 Practical Example Hypothesis Testing.mp445.82MB

22 - Part 4 Introduction to Python/001 Introduction to Programming.mp414.76MB

22 - Part 4 Introduction to Python/002 Why Python.mp412.04MB

22 - Part 4 Introduction to Python/003 Why Jupyter.mp48.13MB

22 - Part 4 Introduction to Python/004 Installing Python and Jupyter.mp432.82MB

22 - Part 4 Introduction to Python/005 Understanding Jupyter's Interface - the Notebook Dashboard.mp46.07MB

22 - Part 4 Introduction to Python/006 Prerequisites for Coding in the Jupyter Notebooks.mp418.96MB

23 - Python - Variables and Data Types/001 Variables.mp48.95MB

23 - Python - Variables and Data Types/002 Numbers and Boolean Values in Python.mp413.7MB

23 - Python - Variables and Data Types/003 Python Strings.mp415.67MB

24 - Python - Basic Python Syntax/001 Using Arithmetic Operators in Python.mp48.63MB

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.mp41.2MB

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

24 - Python - Basic Python Syntax/007 Structuring with Indentation.mp42.79MB

25 - Python - Other Python Operators/001 Comparison Operators.mp44.82MB

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

26 - Python - Conditional Statements/001 The IF Statement.mp46.07MB

26 - Python - Conditional Statements/002 The ELSE Statement.mp46.04MB

26 - Python - Conditional Statements/003 The ELIF Statement.mp414.23MB

26 - Python - Conditional Statements/004 A Note on Boolean Values.mp44.24MB

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.mp48.33MB

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.mp43.23MB

27 - Python - Python Functions/005 Conditional Statements and Functions.mp45.98MB

27 - Python - Python Functions/006 Functions Containing a Few Arguments.mp42.83MB

27 - Python - Python Functions/007 Built-in Functions in Python.mp410.19MB

28 - Python - Sequences/001 Lists.mp423.03MB

28 - Python - Sequences/002 Using Methods.mp430.37MB

28 - Python - Sequences/003 List Slicing.mp419.18MB

28 - Python - Sequences/004 Tuples.mp418.19MB

28 - Python - Sequences/005 Dictionaries.mp432.44MB

29 - Python - Iterations/001 For Loops.mp423.6MB

29 - Python - Iterations/002 While Loops and Incrementing.mp420.18MB

29 - Python - Iterations/003 Lists with the range() Function.mp411.94MB

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

29 - Python - Iterations/005 Conditional Statements, Functions, and Loops.mp44.27MB

29 - Python - Iterations/006 How to Iterate over Dictionaries.mp416.44MB

30 - Python - Advanced Python Tools/001 Object Oriented Programming.mp48.65MB

30 - Python - Advanced Python Tools/002 Modules and Packages.mp42.08MB

30 - Python - Advanced Python Tools/003 What is the Standard Library.mp45.05MB

30 - Python - Advanced Python Tools/004 Importing Modules in Python.mp48.55MB

31 - Part 5 Advanced Statistical Methods in Python/001 Introduction to Regression Analysis.mp43.59MB

32 - Advanced Statistical Methods - Linear Regression with StatsModels/001 The Linear Regression Model.mp413.48MB

32 - Advanced Statistical Methods - Linear Regression with StatsModels/002 Correlation vs Regression.mp43.84MB

32 - Advanced Statistical Methods - Linear Regression with StatsModels/004 Python Packages Installation.mp423.67MB

32 - Advanced Statistical Methods - Linear Regression with StatsModels/005 First Regression in Python.mp429.62MB

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.mp428.73MB

32 - Advanced Statistical Methods - Linear Regression with StatsModels/009 Decomposition of Variability.mp48.79MB

32 - Advanced Statistical Methods - Linear Regression with StatsModels/010 What is the OLS.mp422.48MB

32 - Advanced Statistical Methods - Linear Regression with StatsModels/011 R-Squared.mp411.2MB

33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/001 Multiple Linear Regression.mp45.68MB

33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/002 Adjusted R-Squared.mp434.19MB

33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/004 Test for Significance of the Model (F-Test).mp47.17MB

33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/005 OLS Assumptions.mp45.26MB

33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/006 A1 Linearity.mp43.57MB

33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/007 A2 No Endogeneity.mp49.24MB

33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/008 A3 Normality and Homoscedasticity.mp427.38MB

33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/009 A4 No Autocorrelation.mp47.89MB

33 - Advanced Statistical Methods - Multiple Linear Regression with StatsModels/010 A5 No Multicollinearity.mp47.6MB

34 - Advanced Statistical Methods - Linear Regression with sklearn/002 How are we Going to Approach this Section.mp45.29MB

34 - Advanced Statistical Methods - Linear Regression with sklearn/003 Simple Linear Regression with sklearn.mp431.62MB

34 - Advanced Statistical Methods - Linear Regression with sklearn/004 Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp428.89MB

34 - Advanced Statistical Methods - Linear Regression with sklearn/007 Multiple Linear Regression with sklearn.mp411.01MB

34 - Advanced Statistical Methods - Linear Regression with sklearn/008 Calculating the Adjusted R-Squared in sklearn.mp421.75MB

34 - Advanced Statistical Methods - Linear Regression with sklearn/010 Feature Selection (F-regression).mp420.47MB

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).mp420.36MB

34 - Advanced Statistical Methods - Linear Regression with sklearn/015 Feature Selection through Standardization of Weights.mp424.46MB

34 - Advanced Statistical Methods - Linear Regression with sklearn/016 Predicting with the Standardized Coefficients.mp420.42MB

34 - Advanced Statistical Methods - Linear Regression with sklearn/018 Underfitting and Overfitting.mp45.83MB

34 - Advanced Statistical Methods - Linear Regression with sklearn/019 Train - Test Split Explained.mp435.57MB

35 - Advanced Statistical Methods - Practical Example Linear Regression/001 Practical Example Linear Regression (Part 1).mp484.69MB

35 - Advanced Statistical Methods - Practical Example Linear Regression/002 Practical Example Linear Regression (Part 2).mp431.88MB

35 - Advanced Statistical Methods - Practical Example Linear Regression/004 Practical Example Linear Regression (Part 3).mp416.65MB

35 - Advanced Statistical Methods - Practical Example Linear Regression/006 Practical Example Linear Regression (Part 4).mp439.4MB

35 - Advanced Statistical Methods - Practical Example Linear Regression/008 Practical Example Linear Regression (Part 5).mp450.4MB

36 - Advanced Statistical Methods - Logistic Regression/001 Introduction to Logistic Regression.mp45.87MB

36 - Advanced Statistical Methods - Logistic Regression/002 A Simple Example in Python.mp421.89MB

36 - Advanced Statistical Methods - Logistic Regression/003 Logistic vs Logit Function.mp423.76MB

36 - Advanced Statistical Methods - Logistic Regression/004 Building a Logistic Regression.mp48.59MB

36 - Advanced Statistical Methods - Logistic Regression/006 An Invaluable Coding Tip.mp418.76MB

36 - Advanced Statistical Methods - Logistic Regression/007 Understanding Logistic Regression Tables.mp414.58MB

36 - Advanced Statistical Methods - Logistic Regression/009 What do the Odds Actually Mean.mp411.39MB

36 - Advanced Statistical Methods - Logistic Regression/010 Binary Predictors in a Logistic Regression.mp424.83MB

36 - Advanced Statistical Methods - Logistic Regression/012 Calculating the Accuracy of the Model.mp420.27MB

36 - Advanced Statistical Methods - Logistic Regression/014 Underfitting and Overfitting.mp47.49MB

36 - Advanced Statistical Methods - Logistic Regression/015 Testing the Model.mp421.59MB

37 - Advanced Statistical Methods - Cluster Analysis/001 Introduction to Cluster Analysis.mp414.46MB

37 - Advanced Statistical Methods - Cluster Analysis/002 Some Examples of Clusters.mp435.87MB

37 - Advanced Statistical Methods - Cluster Analysis/003 Difference between Classification and Clustering.mp49.67MB

37 - Advanced Statistical Methods - Cluster Analysis/004 Math Prerequisites.mp45.27MB

38 - Advanced Statistical Methods - K-Means Clustering/001 K-Means Clustering.mp410.82MB

38 - Advanced Statistical Methods - K-Means Clustering/002 A Simple Example of Clustering.mp434.21MB

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.mp426.87MB

38 - Advanced Statistical Methods - K-Means Clustering/008 Pros and Cons of K-Means Clustering.mp411.12MB

38 - Advanced Statistical Methods - K-Means Clustering/009 To Standardize or not to Standardize.mp410.92MB

38 - Advanced Statistical Methods - K-Means Clustering/010 Relationship between Clustering and Regression.mp43.51MB

38 - Advanced Statistical Methods - K-Means Clustering/011 Market Segmentation with Cluster Analysis (Part 1).mp428.09MB

38 - Advanced Statistical Methods - K-Means Clustering/012 Market Segmentation with Cluster Analysis (Part 2).mp434.09MB

38 - Advanced Statistical Methods - K-Means Clustering/013 How is Clustering Useful.mp437.48MB

39 - Advanced Statistical Methods - Other Types of Clustering/001 Types of Clustering.mp49.01MB

39 - Advanced Statistical Methods - Other Types of Clustering/002 Dendrogram.mp418.3MB

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

40 - Part 6 Mathematics/001 What is a Matrix.mp411.94MB

40 - Part 6 Mathematics/002 Scalars and Vectors.mp48.53MB

40 - Part 6 Mathematics/003 Linear Algebra and Geometry.mp413.73MB

40 - Part 6 Mathematics/004 Arrays in Python - A Convenient Way To Represent Matrices.mp418.98MB

40 - Part 6 Mathematics/005 What is a Tensor.mp415.53MB

40 - Part 6 Mathematics/006 Addition and Subtraction of Matrices.mp422.1MB

40 - Part 6 Mathematics/007 Errors when Adding Matrices.mp45.78MB

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

40 - Part 6 Mathematics/009 Dot Product.mp412.84MB

40 - Part 6 Mathematics/010 Dot Product of Matrices.mp434.32MB

40 - Part 6 Mathematics/011 Why is Linear Algebra Useful.mp488.42MB

41 - Part 7 Deep Learning/001 What to Expect from this Part.mp411.73MB

42 - Deep Learning - Introduction to Neural Networks/001 Introduction to Neural Networks.mp410.49MB

42 - Deep Learning - Introduction to Neural Networks/002 Training the Model.mp47.71MB

42 - Deep Learning - Introduction to Neural Networks/003 Types of Machine Learning.mp413.05MB

42 - Deep Learning - Introduction to Neural Networks/004 The Linear Model (Linear Algebraic Version).mp47.98MB

42 - Deep Learning - Introduction to Neural Networks/005 The Linear Model with Multiple Inputs.mp47.91MB

42 - Deep Learning - Introduction to Neural Networks/006 The Linear model with Multiple Inputs and Multiple Outputs.mp416.66MB

42 - Deep Learning - Introduction to Neural Networks/007 Graphical Representation of Simple Neural Networks.mp47.78MB

42 - Deep Learning - Introduction to Neural Networks/008 What is the Objective Function.mp46.18MB

42 - Deep Learning - Introduction to Neural Networks/009 Common Objective Functions L2-norm Loss.mp45.47MB

42 - Deep Learning - Introduction to Neural Networks/010 Common Objective Functions Cross-Entropy Loss.mp49.84MB

42 - Deep Learning - Introduction to Neural Networks/011 Optimization Algorithm 1-Parameter Gradient Descent.mp423.59MB

42 - Deep Learning - Introduction to Neural Networks/012 Optimization Algorithm n-Parameter Gradient Descent.mp416.83MB

43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/001 Basic NN Example (Part 1).mp49.34MB

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).mp415.65MB

43 - Deep Learning - How to Build a Neural Network from Scratch with NumPy/004 Basic NN Example (Part 4).mp439.99MB

44 - Deep Learning - TensorFlow 2.0 Introduction/001 How to Install TensorFlow 2.0.mp427.32MB

44 - Deep Learning - TensorFlow 2.0 Introduction/002 TensorFlow Outline and Comparison with Other Libraries.mp415.28MB

44 - Deep Learning - TensorFlow 2.0 Introduction/003 TensorFlow 1 vs TensorFlow 2.mp415.32MB

44 - Deep Learning - TensorFlow 2.0 Introduction/004 A Note on TensorFlow 2 Syntax.mp44.63MB

44 - Deep Learning - TensorFlow 2.0 Introduction/005 Types of File Formats Supporting TensorFlow.mp48.86MB

44 - Deep Learning - TensorFlow 2.0 Introduction/006 Outlining the Model with TensorFlow 2.mp426.94MB

44 - Deep Learning - TensorFlow 2.0 Introduction/007 Interpreting the Result and Extracting the Weights and Bias.mp425.96MB

44 - Deep Learning - TensorFlow 2.0 Introduction/008 Customizing a TensorFlow 2 Model.mp416.71MB

45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/001 What is a Layer.mp45.17MB

45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/002 What is a Deep Net.mp49.12MB

45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/003 Digging into a Deep Net.mp423.7MB

45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/004 Non-Linearities and their Purpose.mp422.57MB

45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/005 Activation Functions.mp48.85MB

45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/006 Activation Functions Softmax Activation.mp48.74MB

45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/007 Backpropagation.mp420.35MB

45 - Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/008 Backpropagation Picture.mp48.06MB

46 - Deep Learning - Overfitting/001 What is Overfitting.mp410.81MB

46 - Deep Learning - Overfitting/002 Underfitting and Overfitting for Classification.mp414MB

46 - Deep Learning - Overfitting/003 What is Validation.mp48.38MB

46 - Deep Learning - Overfitting/004 Training, Validation, and Test Datasets.mp49.4MB

46 - Deep Learning - Overfitting/005 N-Fold Cross Validation.mp46.24MB

46 - Deep Learning - Overfitting/006 Early Stopping or When to Stop Training.mp410.29MB

47 - Deep Learning - Initialization/001 What is Initialization.mp412.85MB

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

47 - Deep Learning - Initialization/003 State-of-the-Art Method - (Xavier) Glorot Initialization.mp45.46MB

48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/001 Stochastic Gradient Descent.mp49.67MB

48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/002 Problems with Gradient Descent.mp43.65MB

48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/003 Momentum.mp45.18MB

48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/005 Learning Rate Schedules Visualized.mp48MB

48 - Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/007 Adam (Adaptive Moment Estimation).mp47.14MB

49 - Deep Learning - Preprocessing/001 Preprocessing Introduction.mp49.23MB

49 - Deep Learning - Preprocessing/002 Types of Basic Preprocessing.mp43.24MB

49 - Deep Learning - Preprocessing/003 Standardization.mp412.07MB

49 - Deep Learning - Preprocessing/004 Preprocessing Categorical Data.mp45.44MB

49 - Deep Learning - Preprocessing/005 Binary and One-Hot Encoding.mp423.94MB

50 - Deep Learning - Classifying on the MNIST Dataset/001 MNIST The Dataset.mp44.53MB

50 - Deep Learning - Classifying on the MNIST Dataset/002 MNIST How to Tackle the MNIST.mp47.95MB

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.mp422.9MB

50 - Deep Learning - Classifying on the MNIST Dataset/006 MNIST Preprocess the Data - Shuffle and Batch.mp432.69MB

50 - Deep Learning - Classifying on the MNIST Dataset/008 MNIST Outline the Model.mp422.08MB

50 - Deep Learning - Classifying on the MNIST Dataset/009 MNIST Select the Loss and the Optimizer.mp410.63MB

50 - Deep Learning - Classifying on the MNIST Dataset/010 MNIST Learning.mp430.99MB

50 - Deep Learning - Classifying on the MNIST Dataset/012 MNIST Testing the Model.mp422.61MB

51 - Deep Learning - Business Case Example/001 Business Case Exploring the Dataset and Identifying Predictors.mp451.28MB

51 - Deep Learning - Business Case Example/002 Business Case Outlining the Solution.mp43.05MB

51 - Deep Learning - Business Case Example/003 Business Case Balancing the Dataset.mp427.31MB

51 - Deep Learning - Business Case Example/004 Business Case Preprocessing the Data.mp473.86MB

51 - Deep Learning - Business Case Example/006 Business Case Load the Preprocessed Data.mp413.83MB

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.mp443.75MB

51 - Deep Learning - Business Case Example/011 Business Case Testing the Model.mp48.18MB

52 - Deep Learning - Conclusion/001 Summary on What You've Learned.mp49.86MB

52 - Deep Learning - Conclusion/002 What's Further out there in terms of Machine Learning.mp44.79MB

52 - Deep Learning - Conclusion/004 An overview of CNNs.mp413.38MB

52 - Deep Learning - Conclusion/005 An Overview of RNNs.mp46.97MB

52 - Deep Learning - Conclusion/006 An Overview of non-NN Approaches.mp416.08MB

53 - Appendix Deep Learning - TensorFlow 1 Introduction/002 How to Install TensorFlow 1.mp45MB

53 - Appendix Deep Learning - TensorFlow 1 Introduction/004 TensorFlow Intro.mp416.88MB

53 - Appendix Deep Learning - TensorFlow 1 Introduction/005 Actual Introduction to TensorFlow.mp49.15MB

53 - Appendix Deep Learning - TensorFlow 1 Introduction/006 Types of File Formats, supporting Tensors.mp48.89MB

53 - Appendix Deep Learning - TensorFlow 1 Introduction/007 Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp417.7MB

53 - Appendix Deep Learning - TensorFlow 1 Introduction/008 Basic NN Example with TF Loss Function and Gradient Descent.mp415.7MB

53 - Appendix Deep Learning - TensorFlow 1 Introduction/009 Basic NN Example with TF Model Output.mp419.14MB

54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/001 MNIST What is the MNIST Dataset.mp44.8MB

54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/002 MNIST How to Tackle the MNIST.mp46.49MB

54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/003 MNIST Relevant Packages.mp411.26MB

54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/004 MNIST Model Outline.mp434.65MB

54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/005 MNIST Loss and Optimization Algorithm.mp415.77MB

54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/006 Calculating the Accuracy of the Model.mp424.45MB

54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/007 MNIST Batching and Early Stopping.mp49.48MB

54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/008 MNIST Learning.mp431.83MB

54 - Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/009 MNIST Results and Testing.mp445.47MB

55 - Appendix Deep Learning - TensorFlow 1 Business Case/001 Business Case Getting Acquainted with the Dataset.mp460.25MB

55 - Appendix Deep Learning - TensorFlow 1 Business Case/002 Business Case Outlining the Solution.mp44.16MB

55 - Appendix Deep Learning - TensorFlow 1 Business Case/003 The Importance of Working with a Balanced Dataset.mp427.25MB

55 - Appendix Deep Learning - TensorFlow 1 Business Case/004 Business Case Preprocessing.mp463.7MB

55 - Appendix Deep Learning - TensorFlow 1 Business Case/006 Creating a Data Provider.mp456.31MB

55 - Appendix Deep Learning - TensorFlow 1 Business Case/007 Business Case Model Outline.mp442.53MB

55 - Appendix Deep Learning - TensorFlow 1 Business Case/008 Business Case Optimization.mp426.93MB

55 - Appendix Deep Learning - TensorFlow 1 Business Case/009 Business Case Interpretation.mp418.6MB

55 - Appendix Deep Learning - TensorFlow 1 Business Case/010 Business Case Testing the Model.mp44.34MB

55 - Appendix Deep Learning - TensorFlow 1 Business Case/011 Business Case A Comment on the Homework.mp420.58MB

56 - Software Integration/001 What are Data, Servers, Clients, Requests, and Responses.mp419.51MB

56 - Software Integration/002 What are Data Connectivity, APIs, and Endpoints.mp460.23MB

56 - Software Integration/003 Taking a Closer Look at APIs.mp467.06MB

56 - Software Integration/004 Communication between Software Products through Text Files.mp421.87MB

56 - Software Integration/005 Software Integration - Explained.mp442.94MB

57 - Case Study - What's Next in the Course/001 Game Plan for this Python, SQL, and Tableau Business Exercise.mp419.67MB

57 - Case Study - What's Next in the Course/002 The Business Task.mp48.35MB

57 - Case Study - What's Next in the Course/003 Introducing the Data Set.mp424.24MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/002 Importing the Absenteeism Data in Python.mp419.53MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/003 Checking the Content of the Data Set.mp454.05MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/004 Introduction to Terms with Multiple Meanings.mp418MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/006 Using a Statistical Approach towards the Solution to the Exercise.mp413.75MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/007 Dropping a Column from a DataFrame in Python.mp441.25MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/010 Analyzing the Reasons for Absence.mp427.61MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/011 Obtaining Dummies from a Single Feature.mp469.76MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/015 More on Dummy Variables A Statistical Perspective.mp45.82MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/016 Classifying the Various Reasons for Absence.mp459.2MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/017 Using .concat() in Python.mp427.33MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/020 Reordering Columns in a Pandas DataFrame in Python.mp49.99MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/023 Creating Checkpoints while Coding in Jupyter.mp417.33MB

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.mp433.93MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/028 Extracting the Day of the Week from the Date Column.mp419.11MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/030 Analyzing Several Straightforward Columns for this Exercise.mp420.09MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/031 Working on Education, Children, and Pets.mp416.92MB

58 - Case Study - Preprocessing the 'Absenteeism_data'/032 Final Remarks of this Section.mp419.74MB

59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/004 Standardizing the Data.mp415.15MB

59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/005 Splitting the Data for Training and Testing.mp441.89MB

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'/008 Interpreting the Coefficients for Our Problem.mp424.77MB

59 - Case Study - Applying Machine Learning to Create the 'absenteeism_module'/012 Testing the Model We Created.mp431.6MB

60 - Case Study - Loading the 'absenteeism_module'/002 Deploying the 'absenteeism_module' - Part I.mp419.64MB

60 - Case Study - Loading the 'absenteeism_module'/003 Deploying the 'absenteeism_module' - Part II.mp445.15MB

61 - Case Study - Analyzing the Predicted Outputs in Tableau/002 Analyzing Age vs Probability in Tableau.mp438.66MB

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.mp419.78MB

62 - Appendix - Additional Python Tools/001 Using the .format() Method.mp425.69MB

62 - Appendix - Additional Python Tools/002 Iterating Over Range Objects.mp412.62MB

62 - Appendix - Additional Python Tools/003 Introduction to Nested For Loops.mp412.13MB

62 - Appendix - Additional Python Tools/004 Triple Nested For Loops.mp433MB

62 - Appendix - Additional Python Tools/005 List Comprehensions.mp443.2MB

62 - Appendix - Additional Python Tools/006 Anonymous (Lambda) Functions.mp430.34MB

63 - Appendix - pandas Fundamentals/001 Introduction to pandas Series.mp422.21MB

63 - Appendix - pandas Fundamentals/002 Working with Methods in Python - Part I.mp419.55MB

63 - Appendix - pandas Fundamentals/003 Working with Methods in Python - Part II.mp49MB

63 - Appendix - pandas Fundamentals/004 Parameters and Arguments in pandas.mp421.14MB

63 - Appendix - pandas Fundamentals/005 Using .unique() and .nunique().mp424.29MB

63 - Appendix - pandas Fundamentals/006 Using .sort_values().mp421.04MB

63 - Appendix - pandas Fundamentals/007 Introduction to pandas DataFrames - Part I.mp410.61MB

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

63 - Appendix - pandas Fundamentals/009 pandas DataFrames - Common Attributes.mp429.79MB

63 - Appendix - pandas Fundamentals/010 Data Selection in pandas DataFrames.mp437.26MB

63 - Appendix - pandas Fundamentals/011 pandas DataFrames - Indexing with .iloc[].mp432.22MB

63 - Appendix - pandas Fundamentals/012 pandas DataFrames - Indexing with .loc[].mp420.69MB

64 - Appendix - Working with Text Files in Python/001 An Introduction to Working with Files in Python.mp412.03MB

64 - Appendix - Working with Text Files in Python/002 File vs File Object, Reading vs Parsing Data.mp49.42MB

64 - Appendix - Working with Text Files in Python/003 Structured, Semi-Structured and Unstructured Data.mp411.07MB

64 - Appendix - Working with Text Files in Python/004 Text Files and Data Connectivity.mp410.81MB

64 - Appendix - Working with Text Files in Python/005 Importing Data in Python - Principles.mp416.72MB

64 - Appendix - Working with Text Files in Python/006 Plain Text Files, Flat Files and More.mp413.16MB

64 - Appendix - Working with Text Files in Python/007 Text Files of Fixed Width.mp44.83MB

64 - Appendix - Working with Text Files in Python/008 Common Naming Conventions.mp48.21MB

64 - Appendix - Working with Text Files in Python/009 Importing Text Files - open().mp428.19MB

64 - Appendix - Working with Text Files in Python/010 Importing Text Files - with open().mp426.26MB

64 - Appendix - Working with Text Files in Python/011 Importing .csv Files - Part I.mp449.87MB

64 - Appendix - Working with Text Files in Python/012 Importing .csv Files - Part II.mp410.92MB

64 - Appendix - Working with Text Files in Python/013 Importing .csv Files - Part III.mp475.01MB

64 - Appendix - Working with Text Files in Python/014 Importing Data with index_col.mp411.63MB

64 - Appendix - Working with Text Files in Python/015 Importing Data with .loadtxt() and .genfromtxt().mp456.33MB

64 - Appendix - Working with Text Files in Python/016 Importing Data - Partial Cleaning While Importing Data.mp443.91MB

64 - Appendix - Working with Text Files in Python/018 Importing Data from .json Files.mp481.95MB

64 - Appendix - Working with Text Files in Python/019 An Introduction to Working with Excel Files in Python.mp442.98MB

64 - Appendix - Working with Text Files in Python/020 Working with Excel (.xlsx) Data.mp414.41MB

64 - Appendix - Working with Text Files in Python/021 Importing Data in Python - an Important Exercise.mp443.01MB

64 - Appendix - Working with Text Files in Python/022 Importing Data with the .squeeze() Method.mp422.42MB

64 - Appendix - Working with Text Files in Python/023 Importing Files in Jupyter.mp419.57MB

64 - Appendix - Working with Text Files in Python/024 Saving Your Data with pandas.mp421.06MB

64 - Appendix - Working with Text Files in Python/025 Saving Your Data with NumPy - Part I - .npy.mp418.91MB

64 - Appendix - Working with Text Files in Python/026 Saving Your Data with NumPy - Part II - .npz.mp423.26MB

64 - Appendix - Working with Text Files in Python/027 Saving Your Data with NumPy - Part III - .csv.mp420.83MB

64 - Appendix - Working with Text Files in Python/029 Working with Text Files in Python - Conclusion.mp42.11MB