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.
- 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) 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.