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Machine Learning with Python: Zero to GBMs

Machine Learning with Python: Zero to GBMs


Course Start Date: 13 June 2021

"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 and its ecosystem of ML libraries: scikit-learn, XGBoost, and LightGBM.

  • 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

This is an upcoming live online course that will run over 6 weeks, starting on 13 June 2021 (tentative). Enroll now to get notified when the course starts.

Course Curriculum

This course covers the following topics:

  1. Introduction to Machine Learning and Decision Trees
  2. Random Forests and Ensemble Methods
  3. Feature Engineering and Hyperparameter Tuning
  4. Gradient Boosting with XGBoost and LightGBM
  5. End-to-End Case Study of a Machine Learning Project
  6. Other Supervised Learning Approaches (Linear Models, Naive Bayes, KNN, etc.)

You will work on 3 programming assignments and build a course project where you'll train machine learning models on a large real-world dataset.

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.

Jovian Mentorship Program

Get access to a private Slack group with the course team, attend weekly office hours on Zoom, and get 1-on-1 guidance for your project by joining the Jovian Data Science Mentorship Program. This is a limited and paid program designed to help you get the most out of this course. Apply here: .