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Jovian

Machine Learning with Python: Zero to GBMs

via Jovian

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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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