Machine Learning with Python: Zero to GBMs

Overview

"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.

Syllabus

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

Taught by

Aakash N S

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