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Machine Learning with Python: Zero to GBMs | Jovian | Free Course

 

Machine Learning with Python: Zero to GBMs






Machine Learning with Python: Zero to GBMs

A 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. Earn a verified certificate of accomplishment by completing practical assignments and building a real-world course project.

  • 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.

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