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Machine Learning with Python: Zero to GBMs

Machine Learning with Python: Zero to GBMs

 
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"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
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  • Preparing data for machine learning
  • Linear regression with multiple features
  • Generating predictions and evaluating models

Lesson 2 - Logistic Regression for Classification
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  • Downloading & processing Kaggle datasets
  • Training a logistic regression model
  • Model evaluation, prediction & persistence

Assignment 1 - Train Your First ML Model

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  • 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

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  • Downloading a real-world dataset
  • Preparing a dataset for training
  • Training & interpreting decision trees

Lesson 4 - Random Forests and Regularization

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  • Training and interpreting random forests
  • Ensemble methods and random forests
  • Hyperparameter tuning and regularization

Assignment 2 - Decision Trees and Random Forests

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  • Prepare a real-world dataset for training
  • Train decision tree and random forest
  • Tune hyperparameters and regularize

Lesson 5 - Gradient Boosting with XGBoost

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  • Training and evaluating a XGBoost model
  • Data normalization and cross-validation
  • Hyperparameter tuning and regularization

Course Project - Real-World Machine Learning Model
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  • Perform data cleaning & feature engineering
  • Training, compare & tune multiple models
  • Document and publish your work online

Lesson 6 - Unsupervised Learning and Recommendations

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  • 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.