Course curriculum

    1. Types of Machine Learning

    2. Python Machine Learning Libraries

    3. Target and Features

    4. Train Test Split (Model Validation)

    5. Train Test Split (Practice)

    6. Types of Features

    7. Simple Imputer

    8. SimpleImputer Exercise (Practice)

    9. Ordinal and One-Hot Encoders

    10. Standardization

    11. StandardScaler Exercise (Practice)

    12. Pipelines

    13. Pipelines (Practice)

    14. Column Transformers

    15. Column Transformer (Core)

    16. Abalone Pre-Preprocessing (Core)

    17. Project 1 - Part 5 (Core)

    18. Interview Questions (Optional)

    1. CRISP-DM Phase 4 Overview

    2. Linear Regression

    3. Linear Regression in Python

    4. Regression Metrics

    5. Regression Evaluation Functions

    6. Bias and Variance

    7. Fit and Evaluate (Practice)

    8. Model Pipelines

    9. Model Pipeline (Core):

    10. Intro to Decision Trees

    11. Regression Trees in Python

    12. GridSearchCV

    13. GridSearch and Model Pipelines

    14. NEW Saving and Loading Models (joblib)

    15. Regression Trees (Practice)

    16. Bagged Trees

    17. Bagged Trees in Python

    18. Random Forests

    19. Random Forests in Python

    20. Ensemble Trees (Core):

    21. CRISP-DM Phase 5 and 6 Overview

    22. Making a README

    23. Project 1 - Part 6 (Core):

    24. Interview Questions (Optional)

    25. Optional - Machine Learning Resources

    1. Intro to Classification

    2. Update EDA Functions for Classification 1

    3. Update EDA Functions for Classification 2

    4. CRISP-DM- Classification

    5. Classification Trees in Python

    6. Classification Tree (Practice)

    7. Type 1 and Type 2 Error

    8. Confusion Matrices

    9. Confusion Matrices in Python

    10. Confusion Matrix (Practice)

    11. Classification Metrics

    12. Classification Report

    13. Classification Evaluation Functions

    14. Classification Metrics (Core):

    15. Decision Thresholds

    16. KNN

    17. KNN Using Python

    18. KNN (Practice)

    19. Logistic Regression

    20. Purpose of L1 or L2 Regularization

    21. Dealing with Unbalanced Classes

    22. LogReg, Random Forest, or KNN (Core)

    23. ROC Curves

    24. Project 1 - Part 7

    25. Logistic Regression Resources (Optional)

    26. Multiclass Classification (Optional)

    27. Optional - Logistic Regression Resources

    28. Multiclass Classification (Optional)

    29. Tree-Based Model Resources

    1. Welcome to Belt Exam Prep!

    2. Belt Exam Rules and Policies

    3. Mock Belt Exam 1

    4. Mock Belt Exam 2

    1. Insights for Stakeholders

    2. Preprocessing

    3. Feature Importance

    4. Permutation Importance

    5. Linear Regression Coefficients

    6. Visualizing Coefficients

    7. Importances and Coefficients (Practice)

    8. Extracting Insights from a Pipeline

    9. Project 1 - Part 8 (Core)

    10. From Regression to Classification

    11. Logistic Regression Coefficients (Optional)

    1. Exam Policies

    2. Exam grading system

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  • 89 lessons
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