Machine Learning with Python: Zero to GBMs

Description

In this course, you will learn:

• A beginner's guide to supervised machine learning, decision trees, and gradient boosting with Python and Scikit-learn. Completing assignments and developing a real-world project will earn you a validated certificate of success.

Syllabus:

1. Linear Regression with Scikit Learn

• Preparing data for machine learning
• Linear regression with multiple features
• Generating predictions and evaluating models

2. Logistic Regression for Classification

• Downloading & processing Kaggle datasets
• Training a logistic regression model
• Model evaluation, prediction & persistence

3. Train Your First ML Model

• Download and prepare a dataset for training
• Train a linear regression model using sklearn
• Make predictions and evaluate the model

4. Decision Trees and Hyperparameters

• Downloading a real-world dataset
• Preparing a dataset for training
• Training & interpreting decision trees

5. Random Forests and Regularization

• Training and interpreting random forests
• Ensemble methods and random forests
• Hyperparameter tuning and regularization

6. Decision Trees and Random Forests

• Prepare a real-world dataset for training
• Train decision tree and random forest
• Tune hyperparameters and regularize

7. Gradient Boosting with XGBoost

• Training and evaluating a XGBoost model
• Data normalization and cross-validation
• Hyperparameter tuning and regularization

8. Real-World Machine Learning Model

• Perform data cleaning & feature engineering
• Training, compare & tune multiple models
• Document and publish your work online

9. Unsupervised Learning and Recommendations

• Clustering and dimensionality reduction
• Collaborative filtering and recommendations
• Other supervised learning algorithms