1. Part 1 Introduction

  1. A Practical Example What You Will Learn in This Course.vtt

  2. What Does the Course Cover.vtt

  3.1 Download All Resources.html

 10. Combinatorics

  1. Fundamentals of Combinatorics.vtt

  10. Solving Variations without Repetition.html

  11. Solving Combinations.vtt

  12. Solving Combinations.html

  13. Symmetry of Combinations.vtt

  14. Symmetry of Combinations.html

  15. Solving Combinations with Separate Sample Spaces.vtt

  17. Combinatorics in Real-Life The Lottery.mp4

  18. Combinatorics in Real-Life The Lottery.html

  19. A Recap of Combinatorics.vtt

  20. A Practical Example of Combinatorics.mp4

  3. Permutations and How to Use Them.mp4

  4. Permutations and How to Use Them.html

  5. Simple Operations with Factorials.vtt

  7. Solving Variations with Repetition.mp4

  8. Solving Variations with Repetition.html

  9. Solving Variations without Repetition.vtt

 11. Bayesian Inference

  1. Sets and Events.vtt

  10. Mutually Exclusive Sets.html

  11. Dependence and Independence of Sets.vtt

  13. The Conditional Probability Formula.mp4

  14. The Conditional Probability Formula.html

  15. The Law of Total Probability.vtt

  16. The Additive Rule.vtt

  18. The Multiplication Law.mp4

  19. The Multiplication Law.html

  20. Bayes' Law.mp4

  21. Bayes' Law.html

  3. Ways Sets Can Interact.vtt

  5. Intersection of Sets.mp4

  6. Intersection of Sets.html

  7. Union of Sets.vtt

  9. Mutually Exclusive Sets.mp4

 12. Probability Distributions

  1. Fundamentals of Probability Distributions.vtt

  10. Discrete Distributions The Bernoulli Distribution.html

  11. Discrete Distributions The Binomial Distribution.vtt

  13. Discrete Distributions The Poisson Distribution.mp4

  14. Discrete Distributions The Poisson Distribution.html

  15. Characteristics of Continuous Distributions.vtt

  16. Characteristics of Continuous Distributions.html

  17. Continuous Distributions The Normal Distribution.vtt

  18. Continuous Distributions The Normal Distribution.html

  19. Continuous Distributions The Standard Normal Distribution.vtt

  20. Continuous Distributions The Standard Normal Distribution.html

  21. Continuous Distributions The Students' T Distribution.vtt

  23. Continuous Distributions The Chi-Squared Distribution.mp4

  24. Continuous Distributions The Chi-Squared Distribution.html

  25. Continuous Distributions The Exponential Distribution.vtt

  27. Continuous Distributions The Logistic Distribution.mp4

  28. Continuous Distributions The Logistic Distribution.html

  3. Types of Probability Distributions.vtt

  5. Characteristics of Discrete Distributions.mp4

  6. Characteristics of Discrete Distributions.html

  7. Discrete Distributions The Uniform Distribution.vtt

  9. Discrete Distributions The Bernoulli Distribution.mp4

 13. Probability in Other Fields

  1. Probability in Finance.vtt

  2. Probability in Statistics.vtt

  3. Probability in Data Science.vtt

 14. Part 3 Statistics

  1. Population and Sample.vtt

  1.2 Course notes_descriptive_statistics.pdf.pdf

 15. Statistics - Descriptive Statistics

  1. Types of Data.vtt

  10. Numerical Variables Exercise.html

  10.2 2.4. Numerical variables. Frequency distribution table_exercise.xlsx.xlsx

  11. The Histogram.vtt

  12. The Histogram.html

  13.1 2.5.The-Histogram-exercise-solution.xlsx.xlsx

  13.3 2.5.The-Histogram-exercise.xlsx.xlsx

  14. Cross Tables and Scatter Plots.vtt

  15. Cross Tables and Scatter Plots.html

  16.1 2.6. Cross table and scatter plot_exercise.xlsx.xlsx

  17. Mean, median and mode.mp4

  17.1 2.7. Mean, median and mode_lesson.xlsx.xlsx

  18.1 2.7. Mean, median and mode_exercise.xlsx.xlsx

  19. Skewness.mp4

  19.1 2.8. Skewness_lesson.xlsx.xlsx

  20. Skewness.html

  21.1 2.8. Skewness_exercise.xlsx.xlsx

  22. Variance.mp4

  22.1 2.9. Variance_lesson.xlsx.xlsx

  23.1 2.9. Variance_exercise.xlsx.xlsx

  24. Standard Deviation and Coefficient of Variation.mp4

  24.1 2.10. Standard deviation and coefficient of variation_lesson.xlsx.xlsx

  26. Standard Deviation and Coefficient of Variation Exercise.html

  26.2 2.10. Standard deviation and coefficient of variation_exercise.xlsx.xlsx

  27. Covariance.vtt

  28. Covariance.html

  29.1 2.11. Covariance_exercise.xlsx.xlsx

  3. Levels of Measurement.mp4

  30. Correlation Coefficient.mp4

  31. Correlation.html

  32.1 2.12. Correlation_exercise.xlsx.xlsx

  4. Levels of Measurement.html

  5. Categorical Variables - Visualization Techniques.vtt

  6. Categorical Variables - Visualization Techniques.html

  7.1 2.3. Categorical variables. Visualization techniques_exercise.xlsx.xlsx

  7.3 Statistics - PDF with Excel Solutions that don't visualize properly.pdf.pdf

  8. Numerical Variables - Frequency Distribution Table.vtt

  9. Numerical Variables - Frequency Distribution Table.html

 16. Statistics - Practical Example Descriptive Statistics

  1. Practical Example Descriptive Statistics.vtt

  2. Practical Example Descriptive Statistics Exercise.html

  2.2 2.13.Practical-example.Descriptive-statistics-exercise-solution.xlsx.xlsx

 17. Statistics - Inferential Statistics Fundamentals

  1. Introduction.vtt

  10. Central Limit Theorem.html

  11. Standard error.vtt

  13. Estimators and Estimates.mp4

  14. Estimators and Estimates.html

  2. What is a Distribution.vtt

  2.2 Course notes_inferential statistics.pdf.pdf

  4. The Normal Distribution.mp4

  5. The Normal Distribution.html

  6. The Standard Normal Distribution.vtt

  7. The Standard Normal Distribution.html

  8.1 3.4.Standard-normal-distribution-exercise-solution.xlsx.xlsx

  9. Central Limit Theorem.mp4

 18. Statistics - Inferential Statistics Confidence Intervals

  1. What are Confidence Intervals.vtt

  10. Margin of Error.vtt

  12. Confidence intervals. Two means. Dependent samples.mp4

  12.1 3.13. Confidence intervals. Two means. Dependent samples_lesson.xlsx.xlsx

  13.1 3.13. Confidence intervals. Two means. Dependent samples_exercise.xlsx.xlsx

  14. Confidence intervals. Two means. Independent samples (Part 1).mp4

  14.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_lesson.xlsx.xlsx

  15.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise_solution.xlsx.xlsx

  16. Confidence intervals. Two means. Independent samples (Part 2).mp4

  16.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_lesson.xlsx.xlsx

  17.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise.xlsx.xlsx

  18. Confidence intervals. Two means. Independent samples (Part 3).mp4

  2. What are Confidence Intervals.html

  3. Confidence Intervals; Population Variance Known; z-score.vtt

  3.2 3.9.The-z-table.xlsx.xlsx

  4.1 3.9.The-z-table.xlsx.xlsx

  4.3 3.9. Population variance known, z-score_exercise_solution.xlsx.xlsx

  5. Confidence Interval Clarifications.vtt

  6. Student's T Distribution.vtt

  8. Confidence Intervals; Population Variance Unknown; t-score.mp4

  8.1 3.11. Population variance unknown, t-score_lesson.xlsx.xlsx

  9. Confidence Intervals; Population Variance Unknown; t-score; Exercise.html

  9.2 3.11. Population variance unknown, t-score_exercise_solution.xlsx.xlsx

 19. Statistics - Practical Example Inferential Statistics

  1. Practical Example Inferential Statistics.vtt

  2. Practical Example Inferential Statistics Exercise.html

  2.2 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx.xlsx

 2. The Field of Data Science - The Various Data Science Disciplines

  1. Data Science and Business Buzzwords Why are there so many.vtt

  2. Data Science and Business Buzzwords Why are there so many.html

  3. What is the difference between Analysis and Analytics.vtt

  5. Business Analytics, Data Analytics, and Data Science An Introduction.mp4

  5.1 365_DataScience_Diagram.pdf.pdf

  7. Continuing with BI, ML, and AI.mp4

  7.1 365_DataScience.png.png

  8. Continuing with BI, ML, and AI.html

  9. A Breakdown of our Data Science Infographic.vtt

 20. Statistics - Hypothesis Testing

  1. Null vs Alternative Hypothesis.vtt

  10. p-value.mp4

  10.1 Online p-value calculator.pdf.pdf

  12. Test for the Mean. Population Variance Unknown.mp4

  12.1 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx.xlsx

  13.1 4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx.xlsx

  14. Test for the Mean. Dependent Samples.mp4

  14.1 4.7. Test for the mean. Dependent samples_lesson.xlsx.xlsx

  15.1 4.7. Test for the mean. Dependent samples_exercise_solution.xlsx.xlsx

  16. Test for the mean. Independent samples (Part 1).mp4

  16.1 4.8. Test for the mean. Independent samples (Part 1)_lesson.xlsx.xlsx

  17.1 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise-solution.xlsx.xlsx

  18. Test for the mean. Independent samples (Part 2).mp4

  18.1 4.9. Test for the mean. Independent samples (Part 2)_lesson.xlsx.xlsx

  2. Further Reading on Null and Alternative Hypothesis.html

  20.1 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2-solution.xlsx.xlsx

  3. Null vs Alternative Hypothesis.html

  4. Rejection Region and Significance Level.vtt

  5. Rejection Region and Significance Level.html

  6. Type I Error and Type II Error.vtt

  8. Test for the Mean. Population Variance Known.mp4

  8.1 4.4. Test for the mean. Population variance known_lesson.xlsx.xlsx

  9.1 4.4. Test for the mean. Population variance known_exercise_solution.xlsx.xlsx

 21. Statistics - Practical Example Hypothesis Testing

  1. Practical Example Hypothesis Testing.vtt

  2. Practical Example Hypothesis Testing Exercise.html

  2.2 4.10.Hypothesis-testing-section-practical-example-exercise-solution.xlsx.xlsx

 22. Part 4 Introduction to Python

  1. Introduction to Programming.vtt

  11. Python 2 vs Python 3.mp4

  11.1 Python Introduction - Course Notes.pdf.pdf

  3. Why Python.mp4

  4. Why Python.html

  5. Why Jupyter.vtt

  7. Installing Python and Jupyter.mp4

  8. Understanding Jupyter's Interface - the Notebook Dashboard.mp4

  9. Prerequisites for Coding in the Jupyter Notebooks.mp4

 23. Python - Variables and Data Types

  1. Variables.vtt

  1.2 Python Introduction - Course Notes.pdf.pdf

  3. Numbers and Boolean Values in Python.mp4

  3.1 Numbers and Boolean Values - Resources.html

  5. Python Strings.mp4

  5.1 Strings - Resources.html

 24. Python - Basic Python Syntax

  1. Using Arithmetic Operators in Python.vtt

  10. Indexing Elements.mp4

  10.1 Indexing Elements - Resources.html

  12. Structuring with Indentation.mp4

  12.1 Structure Your Code with Indentation - Resources.html

  2. Using Arithmetic Operators in Python.html

  3. The Double Equality Sign.vtt

  4. The Double Equality Sign.html

  5. How to Reassign Values.vtt

  6. How to Reassign Values.html

  7. Add Comments.vtt

  8. Add Comments.html

  9. Understanding Line Continuation.vtt

 25. Python - Other Python Operators

  1. Comparison Operators.vtt

  2. Comparison Operators.html

  3. Logical and Identity Operators.vtt

  4. Logical and Identity Operators.html

 26. Python - Conditional Statements

  1. The IF Statement.vtt

  2. The IF Statement.html

  3. The ELSE Statement.vtt

  4. The ELIF Statement.mp4

  4.1 Else if, for Brief - Elif - Resources.html

  5. A Note on Boolean Values.vtt

  6. A Note on Boolean Values.html

 27. Python - Python Functions

  1. Defining a Function in Python.vtt

  2. How to Create a Function with a Parameter.mp4

  2.1 Creating a Function with a Parameter - Resources.html

  3. Defining a Function in Python - Part II.vtt

  4. How to Use a Function within a Function.mp4

  4.1 Using a Function in Another Function - Resources.html

  5. Conditional Statements and Functions.vtt

  6. Functions Containing a Few Arguments.mp4

  6.1 Creating Functions Containing a Few Arguments - Resources.html

  7. Built-in Functions in Python.vtt

  8. Python Functions.html

 28. Python - Sequences

  1. Lists.vtt

  2. Lists.html

  3. Using Methods.vtt

  4. Using Methods.html

  5. List Slicing.vtt

  6. Tuples.mp4

  6.1 Tuples - Resources.html

  7. Dictionaries.vtt

  8. Dictionaries.html

 29. Python - Iterations

  1. For Loops.vtt

  2. For Loops.html

  3. While Loops and Incrementing.vtt

  4. Lists with the range() Function.mp4

  4.1 Create Lists with the range() Function - Resources.html

  6. Conditional Statements and Loops.mp4

  6.1 Use Conditional Statements and Loops Together - Resources.html

  7. Conditional Statements, Functions, and Loops.vtt

  8. How to Iterate over Dictionaries.mp4

  8.1 Iterating over Dictionaries - Resources.html

 3. The Field of Data Science - Connecting the Data Science Disciplines

  1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.vtt

 30. Python - Advanced Python Tools

  1. Object Oriented Programming.vtt

  3. Modules and Packages.mp4

  4. Modules and Packages.html

  5. What is the Standard Library.vtt

  7. Importing Modules in Python.mp4

  8. Importing Modules in Python.html

 31. Part 5 Advanced Statistical Methods in Python

  1. Introduction to Regression Analysis.vtt

 32. Advanced Statistical Methods - Linear regression with StatsModels

  1. The Linear Regression Model.vtt

  10. Using Seaborn for Graphs.vtt

  11. How to Interpret the Regression Table.vtt

  13. Decomposition of Variability.mp4

  14. Decomposition of Variability.html

  15. What is the OLS.vtt

  17. R-Squared.mp4

  18. R-Squared.html

  3. Correlation vs Regression.mp4

  4. Correlation vs Regression.html

  5. Geometrical Representation of the Linear Regression Model.vtt

  7. Python Packages Installation.mp4

  8. First Regression in Python.mp4

  8.1 First regression in Python.html

  9.1 First regression in Python - Exercise.html

 33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels

  1. Multiple Linear Regression.vtt

  11. A2 No Endogeneity.mp4

  12. A2 No Endogeneity.html

  13. A3 Normality and Homoscedasticity.vtt

  14. A4 No Autocorrelation.vtt

  16. A5 No Multicollinearity.mp4

  17. A5 No Multicollinearity.html

  18. Dealing with Categorical Data - Dummy Variables.vtt

  19. Dealing with Categorical Data - Dummy Variables.html

  2. Multiple Linear Regression.html

  20. Making Predictions with the Linear Regression.vtt

  3. Adjusted R-Squared.mp4

  3.1 Adjusted R-squared.html

  5. Multiple Linear Regression Exercise.html

  6. Test for Significance of the Model (F-Test).mp4

  7. OLS Assumptions.mp4

  8. OLS Assumptions.html

  9. A1 Linearity.vtt

 34. Advanced Statistical Methods - Linear Regression with sklearn

  1. What is sklearn and How is it Different from Other Packages.vtt

  10. Feature Selection (F-regression).vtt

  11. A Note on Calculation of P-values with sklearn.html

  12. Creating a Summary Table with p-values.mp4

  12.1 Summary table with p-values.html

  13.1 Multiple linear regression - Exercise.html

  14. Feature Scaling (Standardization).vtt

  15. Feature Selection through Standardization of Weights.mp4

  15.1 Feature scaling standardization.html

  16. Predicting with the Standardized Coefficients.vtt

  17. Feature Scaling (Standardization) - Exercise.html

  18. Underfitting and Overfitting.mp4

  19. Train - Test Split Explained.mp4

  19.1 Train - Test split explained.html

  2. How are Going to Approach this Section.vtt

  3. Simple Linear Regression with sklearn.vtt

  4. Simple Linear Regression with sklearn - A StatsModels-like Summary Table.vtt

  6. Simple Linear Regression with sklearn - Exercise.html

  7. Multiple Linear Regression with sklearn.mp4

  8. Calculating the Adjusted R-Squared in sklearn.mp4

  9. Calculating the Adjusted R-Squared in sklearn - Exercise.html

 35. Advanced Statistical Methods - Practical Example Linear Regression

  1. Practical Example Linear Regression (Part 1).vtt

  2. Practical Example Linear Regression (Part 2).mp4

  2.1 sklearn - Linear Regression - Practical Example (Part 2).html

  4. Practical Example Linear Regression (Part 3).mp4

  4.1 sklearn - Linear Regression - Practical Example (Part 3).html

  5.1 Dummies and VIF - Exercise and Solution.html

  6. Practical Example Linear Regression (Part 4).vtt

  7. Dummy Variables - Exercise.html

  8. Practical Example Linear Regression (Part 5).vtt

  9. Linear Regression - Exercise.html

 36. Advanced Statistical Methods - Logistic Regression

  1. Introduction to Logistic Regression.vtt

  10. Binary Predictors in a Logistic Regression.vtt

  11. Binary Predictors in a Logistic Regression - Exercise.html

  11.2 Binary predictors - exercise.html

  12. Calculating the Accuracy of the Model.vtt

  13. Calculating the Accuracy of the Model.html

  13.2 Bank_data.csv.csv

  14. Underfitting and Overfitting.vtt

  15. Testing the Model.vtt

  16. Testing the Model - Exercise.html

  16.2 Bank_data.csv.csv

  2. A Simple Example in Python.mp4

  2.1 A simple example in Python.html

  3. Logistic vs Logit Function.vtt

  4. Building a Logistic Regression.vtt

  5. Building a Logistic Regression - Exercise.html

  5.2 Example_bank_data.csv.csv

  6. An Invaluable Coding Tip.vtt

  7. Understanding Logistic Regression Tables.vtt

  8.1 Understanding logistic regression.html

  9. What do the Odds Actually Mean.mp4

 37. Advanced Statistical Methods - Cluster Analysis

  1. Introduction to Cluster Analysis.vtt

  2. Some Examples of Clusters.vtt

  3. Difference between Classification and Clustering.vtt

  4. Math Prerequisites.vtt

 38. Advanced Statistical Methods - K-Means Clustering

  1. K-Means Clustering.vtt

  10. Relationship between Clustering and Regression.vtt

  11. Market Segmentation with Cluster Analysis (Part 1).vtt

  12. Market Segmentation with Cluster Analysis (Part 2).mp4

  12.1 Market segmentation.html

  13. How is Clustering Useful.vtt

  14.1 Exercise - part 1.html

  15. EXERCISE Species Segmentation with Cluster Analysis (Part 2).html

  15.2 iris_dataset.csv.csv

  2. A Simple Example of Clustering.mp4

  2.1 Example of clustering.html

  3.1 A simple example of clustering.html

  4. Clustering Categorical Data.mp4

  4.1 Clustering categorical data.html

  5.1 Categorical.csv.csv

  6. How to Choose the Number of Clusters.mp4

  6.1 How to choose the number of clusters.html

  7.1 How to choose the number of clusters.html

  8. Pros and Cons of K-Means Clustering.mp4

  9. To Standardize or not to Standardize.mp4

 39. Advanced Statistical Methods - Other Types of Clustering

  1. Types of Clustering.vtt

  2. Dendrogram.vtt

  3. Heatmaps.vtt

 4. The Field of Data Science - The Benefits of Each Discipline

  1. The Reason behind these Disciplines.vtt

 40. Part 6 Mathematics

  1. What is a matrix.vtt

  10. Addition and Subtraction of Matrices.vtt

  11. Addition and Subtraction of Matrices.html

  12. Errors when Adding Matrices.vtt

  13. Transpose of a Matrix.mp4

  13.1 Transpose of a Matrix Python Notebook.html

  14. Dot Product.vtt

  15. Dot Product of Matrices.mp4

  15.1 Dot Product of Matrices Python Notebook.html

  16. Why is Linear Algebra Useful.vtt

  3. Scalars and Vectors.mp4

  4. Scalars and Vectors.html

  5. Linear Algebra and Geometry.vtt

  7. Arrays in Python - A Convenient Way To Represent Matrices.mp4

  7.1 Arrays in Python Notebook.html

  8. What is a Tensor.vtt

  9. What is a Tensor.html

 41. Part 7 Deep Learning

  1. What to Expect from this Part.vtt

 42. Deep Learning - Introduction to Neural Networks

  1. Introduction to Neural Networks.vtt

  10. The Linear Model with Multiple Inputs.html

  11. The Linear model with Multiple Inputs and Multiple Outputs.vtt

  13. Graphical Representation of Simple Neural Networks.mp4

  14. Graphical Representation of Simple Neural Networks.html

  15. What is the Objective Function.vtt

  17. Common Objective Functions L2-norm Loss.mp4

  18. Common Objective Functions L2-norm Loss.html

  19. Common Objective Functions Cross-Entropy Loss.vtt

  20. Common Objective Functions Cross-Entropy Loss.html

  21. Optimization Algorithm 1-Parameter Gradient Descent.vtt

  22. Optimization Algorithm 1-Parameter Gradient Descent.html

  23. Optimization Algorithm n-Parameter Gradient Descent.vtt

  3. Training the Model.mp4

  3.1 Course Notes - Section 2.pdf.pdf

  5. Types of Machine Learning.mp4

  6. Types of Machine Learning.html

  7. The Linear Model (Linear Algebraic Version).vtt

  9. The Linear Model with Multiple Inputs.mp4

 43. Deep Learning - How to Build a Neural Network from Scratch with NumPy

  1. Basic NN Example (Part 1).vtt

  1.2 Bais NN Example Part 1.html

  2. Basic NN Example (Part 2).vtt

  3. Basic NN Example (Part 3).mp4

  3.1 Basic NN Example (Part 3).html

  4. Basic NN Example (Part 4).vtt

  5. Basic NN Example Exercises.html

  5.10 Basic NN Example Exercise 4 Solution.html

  5.3 Basic NN Example Exercise 3a Solution.html

  5.5 Basic NN Example Exercise 3b Solution.html

  5.7 Basic NN Example Exercise 3c Solution.html

  5.9 Basic NN Example Exercise 6 Solution.html

 44. Deep Learning - TensorFlow Introduction

  1. How to Install TensorFlow.vtt

  2. A Note on Installing Packages in Anaconda.html

  3. TensorFlow Outline and Logic.vtt

  4. Actual Introduction to TensorFlow.vtt

  5. Types of File Formats, supporting Tensors.mp4

  5.1 Basic NN Example with TensorFlow (Part 1).html

  6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.vtt

  7. Basic NN Example with TF Loss Function and Gradient Descent.mp4

  7.1 Basic NN Example with TensorFlow (Part 3).html

  8. Basic NN Example with TF Model Output.vtt

  9. Basic NN Example with TF Exercises.html

  9.2 Basic NN Example with TensorFlow Exercise 2.1 Solution.html

  9.4 Basic NN Example with TensorFlow Exercise 1 Solution.html

  9.6 Basic NN Example with TensorFlow (All Exercises).html

  9.8 Basic NN Example with TensorFlow Exercise 3 Solution.html

 45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks

  1. What is a Layer.vtt

  2. What is a Deep Net.mp4

  2.1 Course Notes - Section 6.pdf.pdf

  3. Digging into a Deep Net.vtt

  4. Non-Linearities and their Purpose.vtt

  5. Activation Functions.vtt

  6. Activation Functions Softmax Activation.vtt

  7. Backpropagation.vtt

  8. Backpropagation picture.vtt

  9.1 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf.pdf

 46. Deep Learning - Overfitting

  1. What is Overfitting.vtt

  2. Underfitting and Overfitting for Classification.vtt

  3. What is Validation.vtt

  4. Training, Validation, and Test Datasets.vtt

  5. N-Fold Cross Validation.vtt

  6. Early Stopping or When to Stop Training.vtt

 47. Deep Learning - Initialization

  1. What is Initialization.vtt

  2. Types of Simple Initializations.vtt

  3. State-of-the-Art Method - (Xavier) Glorot Initialization.vtt

 48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules

  1. Stochastic Gradient Descent.vtt

  2. Problems with Gradient Descent.vtt

  3. Momentum.vtt

  4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.vtt

  5. Learning Rate Schedules Visualized.vtt

  6. Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).vtt

  7. Adam (Adaptive Moment Estimation).vtt

 49. Deep Learning - Preprocessing

  1. Preprocessing Introduction.vtt

  2. Types of Basic Preprocessing.vtt

  3. Standardization.vtt

  4. Preprocessing Categorical Data.vtt

  5. Binary and One-Hot Encoding.vtt

 5. The Field of Data Science - Popular Data Science Techniques

  1. Techniques for Working with Traditional Data.vtt

  10. Techniques for Working with Traditional Methods.vtt

  12. Real Life Examples of Traditional Methods.mp4

  13. Machine Learning (ML) Techniques.mp4

  14. Machine Learning (ML) Techniques.html

  15. Types of Machine Learning.vtt

  17. Real Life Examples of Machine Learning (ML).mp4

  18. Real Life Examples of Machine Learning (ML).html

  3. Real Life Examples of Traditional Data.mp4

  4. Techniques for Working with Big Data.mp4

  5. Techniques for Working with Big Data.html

  6. Real Life Examples of Big Data.vtt

  7. Business Intelligence (BI) Techniques.vtt

  9. Real Life Examples of Business Intelligence (BI).mp4

 50. Deep Learning - Classifying on the MNIST Dataset

  1. MNIST What is the MNIST Dataset.vtt

  10.1 TensorFlow MNIST All Exercises.html

  11.1 TensorFlow MNIST '1. Width' Solution.html

  11.11 TensorFlow MNIST '7. Batch size (Part 2)' Solution.html

  11.3 TensorFlow MNIST '6. Batch size (Part 1)' Solution.html

  11.5 TensorFlow MNIST 'Time' Solution.html

  11.7 TensorFlow MNIST '9. Learning Rate (Part 2)' Solution.html

  11.9 TensorFlow MNIST '2. Depth' Solution.html

  2. MNIST How to Tackle the MNIST.vtt

  3. MNIST Relevant Packages.vtt

  4. MNIST Model Outline.mp4

  4.1 TensorFlow MNIST Part 2 with Comments.html

  5. MNIST Loss and Optimization Algorithm.vtt

  6. Calculating the Accuracy of the Model.mp4

  6.1 TensorFlow MNIST Part 4 with Comments.html

  7. MNIST Batching and Early Stopping.vtt

  8. MNIST Learning.mp4

  8.1 TensorFlow MNIST Part 6 with Comments.html

  9. MNIST Results and Testing.vtt

 51. Deep Learning - Business Case Example

  1. Business Case Getting acquainted with the dataset.vtt

  10. Business Case Testing the Model.mp4

  11. Business Case A Comment on the Homework.mp4

  11.1 TensorFlow Business Case Homework.html

  12.1 TensorFlow Business Case Homework.html

  2. Business Case Outlining the Solution.vtt

  3. The Importance of Working with a Balanced Dataset.vtt

  4. Business Case Preprocessing.vtt

  5. Business Case Preprocessing Exercise.html

  6. Creating a Data Provider.mp4

  6.1 Creating a Data Provider (Class).html

  7. Business Case Model Outline.vtt

  8. Business Case Optimization.mp4

  8.1 TensorFlow Business Case Optimization.html

  9. Business Case Interpretation.vtt

 52. Deep Learning - Conclusion

  1. Summary on What You've Learned.vtt

  2. What's Further out there in terms of Machine Learning.vtt

  3. An overview of CNNs.vtt

  5. An Overview of RNNs.mp4

  6. An Overview of non-NN Approaches.mp4

  7. Download All Resources.html

 53. Software Integration

  1. What are Data, Servers, Clients, Requests, and Responses.vtt

  2. What are Data, Servers, Clients, Requests, and Responses.html

  3. What are Data Connectivity, APIs, and Endpoints.vtt

  5. Taking a Closer Look at APIs.mp4

  6. Taking a Closer Look at APIs.html

  7. Communication between Software Products through Text Files.vtt

  9. Software Integration - Explained.mp4

 54. Case Study - What's Next in the Course

  1. Game Plan for this Python, SQL, and Tableau Business Exercise.vtt

  2. The Business Task.vtt

  3. Introducing the Data Set.vtt

 55. Case Study - Preprocessing the 'Absenteeism_data'

  1.1 Absenteeism_data.csv.csv

  1.3 data_preprocessing_homework.pdf.pdf

  10. Analyzing the Reasons for Absence.vtt

  11. Obtaining Dummies from a Single Feature.vtt

  13. SOLUTION - Obtaining Dummies from a Single Feature.html

  15. More on Dummy Variables A Statistical Perspective.mp4

  16. Classifying the Various Reasons for Absence.mp4

  17. Using .concat() in Python.mp4

  18. EXERCISE - Using .concat() in Python.html

  2. Importing the Absenteeism Data in Python.mp4

  20. Reordering Columns in a Pandas DataFrame in Python.mp4

  21. EXERCISE - Reordering Columns in a Pandas DataFrame in Python.html

  23. Creating Checkpoints while Coding in Jupyter.mp4

  23.1 Creating Checkpoints.html

  25. SOLUTION - Creating Checkpoints while Coding in Jupyter.html

  26. Analyzing the Dates from the Initial Data Set.vtt

  27. Extracting the Month Value from the Date Column.vtt

  28. Extracting the Day of the Week from the Date Column.vtt

  29.1 Removing the “Date” Column.html

  3. Checking the Content of the Data Set.mp4

  30. Analyzing Several Straightforward Columns for this Exercise.mp4

  31. Working on Education, Children, and Pets.mp4

  32. Final Remarks of this Section.mp4

  32.1 Exercises and solutions.html

  4. Introduction to Terms with Multiple Meanings.mp4

  5. What's Regression Analysis - a Quick Refresher.html

  6. Using a Statistical Approach towards the Solution to the Exercise.vtt

  7. Dropping a Column from a DataFrame in Python.vtt

  9. SOLUTION - Dropping a Column from a DataFrame in Python.html

 56. Case Study - Applying Machine Learning to Create the 'absenteeism_module'

  1. Exploring the Problem with a Machine Learning Mindset.vtt

  10. Interpreting the Coefficients of the Logistic Regression.mp4

  11. Backward Elimination or How to Simplify Your Model.mp4

  11.1 Logistic Regression prior to Backward Elimination.html

  12. Testing the Model We Created.vtt

  13. Saving the Model and Preparing it for Deployment.vtt

  15. EXERCISE - Saving the Model (and Scaler).html

  15.2 Logistic Regression.html

  16. Preparing the Deployment of the Model through a Module.vtt

  2. Creating the Targets for the Logistic Regression.vtt

  3. Selecting the Inputs for the Logistic Regression.vtt

  4. Standardizing the Data.vtt

  5. Splitting the Data for Training and Testing.vtt

  6. Fitting the Model and Assessing its Accuracy.vtt

  7. Creating a Summary Table with the Coefficients and Intercept.vtt

  8. Interpreting the Coefficients for Our Problem.vtt

  9. Standardizing only the Numerical Variables (Creating a Custom Scaler).vtt

 57. Case Study - Loading the 'absenteeism_module'

  1.1 5 Files Needed to Deploy the Model.html

  2. Deploying the 'absenteeism_module' - Part I.vtt

  3. Deploying the 'absenteeism_module' - Part II.vtt

  4.1 Deploying the ‘absenteeism_module.html

 58. Case Study - Analyzing the Predicted Outputs in Tableau

  2. Analyzing Age vs Probability in Tableau.mp4

  3. EXERCISE - Reasons vs Probability.html

  4. Analyzing Reasons vs Probability in Tableau.vtt

  6. Analyzing Transportation Expense vs Probability in Tableau.mp4

 6. The Field of Data Science - Popular Data Science Tools

  1. Necessary Programming Languages and Software Used in Data Science.vtt

 7. The Field of Data Science - Careers in Data Science

  1. Finding the Job - What to Expect and What to Look for.vtt

 8. The Field of Data Science - Debunking Common Misconceptions

  1. Debunking Common Misconceptions.vtt

 9. Part 2 Probability

  1. The Basic Probability Formula.vtt

  2. The Basic Probability Formula.html

  3. Computing Expected Values.vtt

  5. Frequency.mp4

  6. Frequency.html

  7. Events and Their Complements.vtt