Machine Learning with Python: Zero to GBMs

"Machine Learning with Python: Zero to GBMs" is a practical and beginner-friendly introduction to supervised machine learning, decision trees, and gradient boosting using Python. This is a self-paced course where you can:

• Watch hands-on coding-focused video tutorials
• Practice coding with cloud Jupyter notebooks
• Build an end-to-end real-world course project
• Earn a verified certificate of accomplishment
• Interact with a global community of learners

You will solve 2 coding assignments & build a course project where you'll train ML models using a large real-world dataset.

Lesson 1 - Linear Regression with Scikit Learn

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

Lesson 2 - Logistic Regression for Classification

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

Assignment 1 - 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

Lesson 3 - Decision Trees and Hyperparameters

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

Lesson 4 - Random Forests and Regularization

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

Assignment 2 - Decision Trees and Random Forests

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

Lesson 5 - Gradient Boosting with XGBoost

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

Course Project - Real-World Machine Learning Model

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

Lesson 6 - Unsupervised Learning and Recommendations

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