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Workshop Onboarding
 Welcome to The Data Science Workshop
 Installation and Setup
 Credits

1. Introduction to Data Science in Python
 Overview
 Application of Data Science
 Overview of Python
 Exercise 1.01: Creating a Dictionary That Will Contain Machine Learning Algorithms
 Exercise 1.01: Creating a Dictionary That Will Contain Machine Learning Algorithms
 Python for Data Science
 Exercise 1.02: Loading Data of Different Formats into a pandas DataFrame
 Exercise 1.02: Loading Data of Different Formats into a pandas DataFrame
 ScikitLearn
 Exercise 1.03: Predicting Breast Cancer from a Dataset Using sklearn
 Exercise 1.03: Predicting Breast Cancer from a Dataset Using sklearn
 Activity 1.01: Train a Spam Detector Algorithm
 Summary

2. Regression
 Overview
 Simple Linear Regression
 Exercise 2.01: Loading and Preparing the Data for Analysis
 Exercise 2.01: Loading and Preparing the Data for Analysis
 Exercise 2.02: Graphical Investigation of Linear Relationships Using Python
 Exercise 2.02: Graphical Investigation of Linear Relationships Using Python
 Exercise 2.03: Examining a Possible LogLinear Relationship Using Python
 Exercise 2.03: Examining a Possible LogLinear Relationship Using Python
 Exercise 2.04: Fitting a Simple Linear Regression Model Using the Statsmodels formula API
 Exercise 2.04: Fitting a Simple Linear Regression Model Using the Statsmodels formula API
 Analyzing the Model Summary
 Activity 2.01: Fitting a LogLinear Model Using the Statsmodels formula API
 Exercise 2.05: Fitting a Multiple Linear Regression Model Using the Statsmodels formula API
 Exercise 2.05: Fitting a Multiple Linear Regression Model Using the Statsmodels formula API
 Assumptions of Regression Analysis
 Activity 2.02: Fitting a Multiple LogLinear Regression Model
 Explaining the Results of Regression Analysis
 Summary

3. Binary Classification
 Overview
 Understanding the Business Context
 Exercise 3.01: Loading and Exploring the Data from the Dataset
 Exercise 3.01: Loading and Exploring the Data from the Dataset
 Testing Business Hypotheses Using Exploratory Data Analysis
 Exercise 3.02: Business Hypothesis Testing for Age versus Propensity for a Term Loan
 Exercise 3.02: Business Hypothesis Testing for Age versus Propensity for a Term Loan
 Intuitions from the Exploratory Analysis
 Activity 3.01: Business Hypothesis Testing to Find Employment Status versus Propensity for Term Deposits
 Feature Engineering
 Exercise 3.03: Feature Engineering – Exploration of Individual Features
 Exercise 3.03: Feature Engineering – Exploration of Individual Features
 Exercise 3.04: Feature Engineering – Creating New Features from Existing Ones
 Exercise 3.04: Feature Engineering – Creating New Features from Existing Ones
 DataDriven Feature Engineering
 Exercise 3.05: Finding the Correlation in Data to Generate a Correlation Plot Using Bank Data
 Exercise 3.05: Finding the Correlation in Data to Generate a Correlation Plot Using Bank Data
 Skewness of Data
 Exercise 3.06: A Logistic Regression Model for Predicting the Propensity of Term Deposit Purchases in a Bank
 Exercise 3.06: A Logistic Regression Model for Predicting the Propensity of Term Deposit Purchases in a Bank
 Activity 3.02: Model Iteration 2 – Logistic Regression Model with Feature Engineered Variables
 Next Steps
 Summary

4. Multiclass Classification with RandomForest
 Overview
 Training a Random Forest Classifier
 Evaluating the Model's Performance
 Exercise 4.01: Building a Model for Classifying Animal Type and Assessing Its Performance
 Exercise 4.01: Building a Model for Classifying Animal Type and Assessing Its Performance
 Number of Trees Estimator
 Exercise 4.02: Tuning n_estimators to Reduce Overfitting
 Exercise 4.02: Tuning n_estimators to Reduce Overfitting
 Maximum Depth
 Exercise 4.03: Tuning max_depth to Reduce Overfitting
 Exercise 4.03: Tuning max_depth to Reduce Overfitting
 Minimum Sample in Leaf
 Exercise 4.04: Tuning min_samples_leaf
 Exercise 4.04: Tuning min_samples_leaf
 Maximum Features
 Exercise 4.05: Tuning max_features
 Exercise 4.05: Tuning max_features
 Activity 4.01: Train a Random Forest Classifier on the ISOLET Dataset
 Summary
 Survey 1

5. Performing Your First Cluster Analysis
 Overview
 Exercise 5.01: Performing Your First Clustering Analysis on the ATO Dataset
 Exercise 5.01: Performing Your First Clustering Analysis on the ATO Dataset
 Interpreting kmeans Results
 Exercise 5.02: Clustering Australian Postcodes by Business Income and Expenses
 Exercise 5.02: Clustering Australian Postcodes by Business Income and Expenses
 Choosing the Number of Clusters
 Exercise 5.03: Finding the Optimal Number of Clusters
 Exercise 5.03: Finding the Optimal Number of Clusters
 Initializing Clusters
 Exercise 5.04: Using Different Initialization Parameters to Achieve a Suitable Outcome
 Exercise 5.04: Using Different Initialization Parameters to Achieve a Suitable Outcome
 Calculating the Distance to the Centroid
 Standardizing Data
 Exercise 5.05: Finding the Closest Centroids in Our Dataset
 Exercise 5.05: Finding the Closest Centroids in Our Dataset
 Exercise 5.06: Standardizing the Data from Our Dataset
 Exercise 5.06: Standardizing the Data from Our Dataset
 Activity 5.01: Perform Customer Segmentation Analysis in a Bank Using kmeans
 Summary

6. How to Assess Performance
 Overview
 Exercise 6.01: Importing and Splitting Data
 Exercise 6.01: Importing and Splitting Data
 Assessing Model Performance for Regression Models
 Exercise 6.02: Computing the R2 Score of a Linear Regression Model
 Exercise 6.02: Computing the R2 Score of a Linear Regression Model
 Exercise 6.03: Computing the MAE of a Model
 Exercise 6.03: Computing the MAE of a Model
 Exercise 6.04: Computing the Mean Absolute Error of a Second Model
 Exercise 6.04: Computing the Mean Absolute Error of a Second Model
 Exercise 6.05: Creating a Classification Model for Computing Evaluation Metrics
 Exercise 6.05: Creating a Classification Model for Computing Evaluation Metrics
 Exercise 6.06: Generating a Confusion Matrix for the Classification Model
 Exercise 6.06: Generating a Confusion Matrix for the Classification Model
 More on the Confusion Matrix
 Exercise 6.07: Computing Precision for the Classification Model
 Exercise 6.07: Computing Precision for the Classification Model
 Exercise 6.08: Computing Recall for the Classification Model
 Exercise 6.08: Computing Recall for the Classification Model
 Exercise 6.09: Computing the F1 Score for the Classification Model
 Exercise 6.09: Computing the F1 Score for the Classification Model
 Exercise 6.10: Computing Model Accuracy for the Classification Model
 Exercise 6.10: Computing Model Accuracy for the Classification Model
 Exercise 6.11: Computing the Log Loss for the Classification Model
 Exercise 6.11: Computing the Log Loss for the Classification Model
 Exercise 6.12: Computing and Plotting ROC Curve for a Binary Classification Problem
 Exercise 6.12: Computing and Plotting ROC Curve for a Binary Classification Problem
 Exercise 6.13: Computing the ROC AUC for the Caesarian Dataset
 Exercise 6.13: Computing the ROC AUC for the Caesarian Dataset
 Exercise 6.14: Saving and Loading a Model
 Exercise 6.14: Saving and Loading a Model
 Activity 6.01: Train Three Different Models and Use Evaluation Metrics to Pick the Best Performing Model
 Summary

7. The Generalization of Machine Learning Models
 Overview
 Overfitting
 Exercise 7.01: Importing and Splitting Data
 Exercise 7.01: Importing and Splitting Data
 Exercise 7.02: Setting a Random State When Splitting Data
 Exercise 7.02: Setting a Random State When Splitting Data
 Exercise 7.03: Creating a FiveFold CrossValidation Dataset
 Exercise 7.03: Creating a FiveFold CrossValidation Dataset
 Exercise 7.04: Creating a FiveFold CrossValidation Dataset Using a Loop for Calls
 Exercise 7.04: Creating a FiveFold CrossValidation Dataset Using a Loop for Calls
 Exercise 7.05: Getting the Scores from FiveFold CrossValidation
 Exercise 7.05: Getting the Scores from FiveFold CrossValidation
 Understanding Estimators That Implement CV
 Exercise 7.06: Training a Logistic Regression Model Using CrossValidation
 Exercise 7.06: Training a Logistic Regression Model Using CrossValidation
 Hyperparameter Tuning with GridSearchCV
 Exercise 7.07: Using Grid Search with CrossValidation to Find the Best Parameters for a Model
 Exercise 7.07: Using Grid Search with CrossValidation to Find the Best Parameters for a Model
 Exercise 7.08: Using Randomized Search for Hyperparameter Tuning
 Exercise 7.08: Using Randomized Search for Hyperparameter Tuning
 Exercise 7.09: Fixing Model Overfitting Using Lasso Regression
 Exercise 7.09: Fixing Model Overfitting Using Lasso Regression
 Exercise 7.10: Fixing Model Overfitting Using Ridge Regression
 Exercise 7.10: Fixing Model Overfitting Using Ridge Regression
 Activity 7.01: Find an Optimal Model for Predicting the Critical Temperatures of Superconductors
 Summary

8. Hyperparameter Tuning
 Overview
 What Are Hyperparameters?
 Exercise 8.01: Manual Hyperparameter Tuning for a kNN Classifier
 Exercise 8.01: Manual Hyperparameter Tuning for a kNN Classifier
 Advantages and Disadvantages of a Manual Search
 GridSearchCV
 Exercise 8.02: Grid Search Hyperparameter Tuning for a Support Vector Machine Classifier
 Exercise 8.02: Grid Search Hyperparameter Tuning for a Support Vector Machine Classifier
 Advantages and Disadvantages of Grid Search
 Random Search
 Exercise 8.03 Random Search Hyperparameter Tuning for a Random Forest Classifier
 Exercise 8.03 Random Search Hyperparameter Tuning for a Random Forest Classifier
 Advantages and Disadvantages of a Random Search
 Activity 8.01: Is the Mushroom Poisonous?
 Summary

9. Interpreting a Machine Learning Model
 Overview
 Exercise 9.01: Extracting the Linear Regression Coefficient
 Exercise 9.01: Extracting the Linear Regression Coefficient
 RandomForest Variable Importance
 Exercise 9.02: Extracting RandomForest Feature Importance
 Exercise 9.02: Extracting RandomForest Feature Importance
 Variable Importance via Permutation
 Exercise 9.03: Extracting Feature Importance via Permutation
 Exercise 9.03: Extracting Feature Importance via Permutation
 Partial Dependence Plots
 Exercise 9.04: Plotting Partial Dependence
 Exercise 9.04: Plotting Partial Dependence
 Local Interpretation with LIME
 Exercise 9.05: Local Interpretation with LIME
 Exercise 9.05: Local Interpretation with LIME
 Activity 9.01: Train and Analyze a Network Intrusion Detection Model
 Summary
 Survey 2

10. Analyzing a Dataset
 Overview
 Exploring Your Data
 Exercise 10.01: Exploring the Ames Housing Dataset with Descriptive Statistics
 Exercise 10.01: Exploring the Ames Housing Dataset with Descriptive Statistics
 Exercise 10.02: Analyzing the Categorical Variables from the Ames Housing Dataset
 Exercise 10.02: Analyzing the Categorical Variables from the Ames Housing Dataset
 Summarizing Numerical Variables
 Exercise 10.03: Analyzing Numerical Variables from the Ames Housing Dataset
 Exercise 10.03: Analyzing Numerical Variables from the Ames Housing Dataset
 Visualizing Your Data
 Exercise 10.04: Visualizing the Ames Housing Dataset with Altair
 Exercise 10.04: Visualizing the Ames Housing Dataset with Altair
 Activity 10.01: Analyzing Churn Data Using Visual Data Analysis Techniques
 Summary

11. Data Preparation
 Overview
 Handling Row Duplication
 Exercise 11.01: Handling Duplicates in a Breast Cancer Dataset
 Exercise 11.01: Handling Duplicates in a Breast Cancer Dataset
 Exercise 11.02: Converting Data Types for the Ames Housing Dataset
 Exercise 11.02: Converting Data Types for the Ames Housing Dataset
 Exercise 11.03: Fixing Incorrect Values in the State Column
 Exercise 11.03: Fixing Incorrect Values in the State Column
 Handling Missing Values
 Exercise 11.04: Fixing Missing Values for the Horse Colic Dataset
 Exercise 11.04: Fixing Missing Values for the Horse Colic Dataset
 Activity 11.01: Preparing the Speed Dating Dataset
 Summary

12. Feature Engineering
 Overview
 Merging Datasets
 Exercise 12.01: Merging the ATO Dataset with the Postcode Data
 Exercise 12.01: Merging the ATO Dataset with the Postcode Data
 Exercise 12.02: Binning the YearBuilt variable from the AMES Housing dataset
 Exercise 12.02: Binning the YearBuilt variable from the AMES Housing dataset
 Exercise 12.03: Date Manipulation on Financial Services Consumer Complaints
 Exercise 12.03: Date Manipulation on Financial Services Consumer Complaints
 Exercise 12.04: Feature Engineering Using Data Aggregation on the AMES Housing Dataset
 Exercise 12.04: Feature Engineering Using Data Aggregation on the AMES Housing Dataset
 Activity 12.01: Feature Engineering on a Financial Dataset
 Summary

13. Imbalanced Datasets
 Overview
 Exercise 13.01: Benchmarking the Logistic Regression Model on the Dataset
 Exercise 13.01: Benchmarking the Logistic Regression Model on the Dataset
 Challenges of Imbalanced Datasets
 Exercise 13.02: Implementing Random Undersampling and Classification on Our Banking Dataset to Find the Optimal Result
 Exercise 13.02: Implementing Random Undersampling and Classification on Our Banking Dataset to Find the Optimal Result
 Exercise 13.03: Implementing SMOTE on Our Banking Dataset to Find the Optimal Result
 Exercise 13.03: Implementing SMOTE on Our Banking Dataset to Find the Optimal Result
 Exercise 13.04: Implementing MSMOTE on Our Banking Dataset to Find the Optimal Result
 Exercise 13.04: Implementing MSMOTE on Our Banking Dataset to Find the Optimal Result
 Applying Balancing Techniques on a Telecom Dataset
 Activity 13.01: Finding the Best Balancing Technique by Fitting a Classifier on the Telecom Churn Dataset
 Summary

14. Dimensionality Reduction
 Overview
 Exercise 14.01: Loading and Cleaning the Dataset
 Exercise 14.01: Loading and Cleaning the Dataset
 Creating a HighDimensional Dataset
 Activity 14.01: Fitting a Logistic Regression Model on a HighDimensional Dataset
 Exercise 14.02: Dimensionality Reduction Using Backward Feature Elimination
 Exercise 14.02: Dimensionality Reduction Using Backward Feature Elimination
 Exercise 14.03: Dimensionality Reduction Using Forward Feature Selection
 Exercise 14.03: Dimensionality Reduction Using Forward Feature Selection
 Principal Component Analysis (PCA)
 Exercise 14.04: Dimensionality Reduction Using PCA
 Exercise 14.04: Dimensionality Reduction Using PCA
 Exercise 14.05: Dimensionality Reduction Using Independent Component Analysis
 Exercise 14.05: Dimensionality Reduction Using Independent Component Analysis
 Exercise 14.06: Dimensionality Reduction Using Factor Analysis
 Exercise 14.06: Dimensionality Reduction Using Factor Analysis
 Comparing Different Dimensionality Reduction Techniques
 Activity 14.02: Comparison of Dimensionality Reduction Techniques on the Enhanced Ads Dataset
 Summary

15. Ensemble Learning
 Overview
 Ensemble Learning
 Exercise 15.01: Loading, Exploring, and Cleaning the Data
 Exercise 15.01: Loading, Exploring, and Cleaning the Data
 Activity 15.01: Fitting a Logistic Regression Model on Credit Card Data
 Simple Methods for Ensemble Learning
 Exercise 15.02: Ensemble Model Using the Averaging Technique
 Exercise 15.02: Ensemble Model Using the Averaging Technique
 Exercise 15.03: Ensemble Model Using the Weighted Averaging Technique
 Exercise 15.03: Ensemble Model Using the Weighted Averaging Technique
 Iteration 2 with Different Weights
 Exercise 15.04: Ensemble Model Using Max Voting
 Exercise 15.04: Ensemble Model Using Max Voting
 Advanced Techniques for Ensemble Learning
 Exercise 15.05: Ensemble Learning Using Bagging
 Exercise 15.05: Ensemble Learning Using Bagging
 Exercise 15.06: Ensemble Learning Using Boosting
 Exercise 15.06: Ensemble Learning Using Boosting
 Exercise 15.07: Ensemble Learning Using Stacking
 Exercise 15.07: Ensemble Learning Using Stacking
 Activity 15.02: Comparison of Advanced Ensemble Techniques
 Summary
 Survey 3

16. Machine Learning Pipelines
 Overview
 Exercise 16.01: Preparing the Dataset to Implement Pipelines
 Exercise 16.01: Preparing the Dataset to Implement Pipelines
 Exercise 16.02: Applying Pipelines for Feature Extraction to the Dataset
 Exercise 16.02: Applying Pipelines for Feature Extraction to the Dataset
 Exercise 16.03: Adding Dimensionality Reduction to the Feature Extraction Pipeline
 Exercise 16.03: Adding Dimensionality Reduction to the Feature Extraction Pipeline
 Exercise 16.04: Modeling and Predictions Using ML Pipelines
 Exercise 16.04: Modeling and Predictions Using ML Pipelines
 Exercise 16.05: SpotChecking Models Using ML Pipelines
 Exercise 16.05: SpotChecking Models Using ML Pipelines
 Exercise 16.06: Grid Search and CrossValidation with ML Pipelines
 Exercise 16.06: Grid Search and CrossValidation with ML Pipelines
 Applying Pipelines to a Dataset
 Activity 16.01: Complete ML Workflow in a Pipeline
 Summary

17. Automated Feature Engineering
 Overview
 Feature Engineering
 Exercise 17.01: Defining Entities and Establishing Relationships
 Exercise 17.01: Defining Entities and Establishing Relationships
 Feature Engineering – Basic Operations
 Exercise 17.02: Creating New Features Using Deep Feature Synthesis
 Exercise 17.02: Creating New Features Using Deep Feature Synthesis
 Exercise 17.03: Classification Model after Automated Feature Generation
 Exercise 17.03: Classification Model after Automated Feature Generation
 Featuretools on a New Dataset
 Activity 17.01: Building a Classification Model with Features that have been Generated Using Featuretools
 Summary

18. Model as a Service with Flask
 Overview
 Building a Flask Web API
 Exercise 18.01: Creating a Flask API with Endpoints
 Exercise 18.01: Creating a Flask API with Endpoints
 Deploying a Machine Learning Model
 Exercise 18.02: Deploying a Model as a Web API
 Exercise 18.02: Deploying a Model as a Web API
 Adding Data Processing Logic
 Exercise 18.03: Adding Data Processing Steps into a Web API
 Exercise 18.03: Adding Data Processing Steps into a Web API
 Activity 18.01: Train and Deploy an Income Predictor Model Using Flask
 Summary

Activity Solutions
 Activity 1.01: Train a Spam Detector Algorithm
 Activity 1.01: Train a Spam Detector Algorithm
 Activity 2.01: Fitting a LogLinear Model Using the Statsmodels formula API
 Activity 2.01: Fitting a LogLinear Model Using the Statsmodels formula API
 Activity 2.02: Fitting a Multiple LogLinear Regression Model
 Activity 2.02: Fitting a Multiple LogLinear Regression Model
 Activity 3.01: Business Hypothesis Testing to Find Employment Status versus Propensity for Term Deposits
 Activity 3.01: Business Hypothesis Testing to Find Employment Status versus Propensity for Term Deposits
 Activity 3.02: Model Iteration 2 – Logistic Regression Model with Feature Engineered Variables
 Activity 3.02: Model Iteration 2 – Logistic Regression Model with Feature Engineered Variables
 Activity 4.01: Train a Random Forest Classifier on the ISOLET Dataset
 Activity 4.01: Train a Random Forest Classifier on the ISOLET Dataset
 Activity 5.01: Perform Customer Segmentation Analysis in a Bank Using kmeans
 Activity 5.01: Perform Customer Segmentation Analysis in a Bank Using kmeans
 Activity 6.01: Train Three Different Models and Use Evaluation Metrics to Pick the Best Performing Model
 Activity 6.01: Train Three Different Models and Use Evaluation Metrics to Pick the Best Performing Model
 Activity 7.01: Find an Optimal Model for Predicting the Critical Temperatures of Superconductors
 Activity 7.01: Find an Optimal Model for Predicting the Critical Temperatures of Superconductors
 Activity 8.01: Is the Mushroom Poisonous?
 Activity 8.01: Is the Mushroom Poisonous?
 Activity 9.01: Train and Analyze a Network Intrusion Detection Model
 Activity 9.01: Train and Analyze a Network Intrusion Detection Model
 Activity 10.01: Analyzing Churn Data Using Visual Data Analysis Techniques
 Activity 10.01: Analyzing Churn Data Using Visual Data Analysis Techniques
 Activity 11.01: Preparing the Speed Dating Dataset
 Activity 11.01: Preparing the Speed Dating Dataset
 Activity 12.01: Feature Engineering on a Financial Dataset
 Activity 12.01: Feature Engineering on a Financial Dataset
 Activity 13.01: Finding the Best Balancing Technique by Fitting a Classifier on the Telecom Churn Dataset
 Activity 13.01: Finding the Best Balancing Technique by Fitting a Classifier on the Telecom Churn Dataset
 Activity 14.01: Fitting a Logistic Regression Model on a HighDimensional Dataset
 Activity 14.01: Fitting a Logistic Regression Model on a HighDimensional Dataset
 Activity 14.02: Comparison of Dimensionality Reduction Techniques on the Enhanced Ads Dataset
 Activity 14.02: Comparison of Dimensionality Reduction Techniques on the Enhanced Ads Dataset
 Activity 15.01: Fitting a Logistic Regression Model on Credit Card Data
 Activity 15.01: Fitting a Logistic Regression Model on Credit Card Data
 Activity 15.02: Comparison of Advanced Ensemble Techniques
 Activity 15.02: Comparison of Advanced Ensemble Techniques
 Activity 16.01: Complete ML Workflow in a Pipeline
 Activity 16.01: Complete ML Workflow in a Pipeline
 Activity 17.01: Building a Classification Model with Features that have been Generated Using Featuretools
 Activity 17.01: Building a Classification Model with Features that have been Generated Using Featuretools
 Activity 18.01 Train and Deploy an Income Predictor Model Using Flask
 Activity 18.01 Train and Deploy an Income Predictor Model Using Flask FREE PREVIEW
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