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. Prerequisite: Data Analysis with Python: Zero to Pandas.
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
Certificate of Accomplishment
Earn a verified certificate of accomplishment (sample) for FREE by completing all weekly assignments and the course project. The certificate can be added to your LinkedIn profile, linked from your Resume, and downloaded as a PDF.
Instructor - Aakash N S
Aakash N S is the co-founder and CEO of Jovian. Previously, Aakash has worked as a software engineer (APIs & Data Platforms) at Twitter in Ireland & San Francisco and graduated from the Indian Institute of Technology, Bombay. He’s also an avid blogger, open-source contributor, and online educator.
