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Applied Machine Learning in Python

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  • Module 1: Fundamentals of Machine Learning - Intro to SciKit Learn
    • This module introduces basic machine learning concepts, tasks, and workflow using an example classification problem based on the K-nearest neighbors method, and implemented using the scikit-learn library.
  • Module 2: Supervised Machine Learning - Part 1
    • This module delves into a wider variety of supervised learning methods for both classification and regression, learning about the connection between model complexity and generalization performance, the importance of proper feature scaling, and how to control model complexity by applying techniques like regularization to avoid overfitting. In addition to k-nearest neighbors, this week covers linear regression (least-squares, ridge, lasso, and polynomial regression), logistic regression, support vector machines, the use of cross-validation for model evaluation, and decision trees.
  • Module 3: Evaluation
    • This module covers evaluation and model selection methods that you can use to help understand and optimize the performance of your machine learning models.
  • Module 4: Supervised Machine Learning - Part 2
    • This module covers more advanced supervised learning methods that include ensembles of trees (random forests, gradient boosted trees), and neural networks (with an optional summary on deep learning). You will also learn about the critical problem of data leakage in machine learning and how to detect and avoid it.