Machine Learning with Python

This course is a part of Statistics and Data Science, a 5-course MicroMasters series from edX.

If you have specific questions about this course, please contact us [email protected]

Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk.

As a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control.

In this course, students will learn about principles and algorithms for turning training data into effective automated predictions. We will cover:

Representation, over-fitting, regularization, generalization, VC dimension;

Clustering, classification, recommender problems, probabilistic modeling, reinforcement learning;

On-line algorithms, support vector machines, and neural networks/deep learning.

Students will implement and experiment with the algorithms in several Python projects designed for different practical applications.

This course is part of theMITx MicroMasters Program in Statistics and Data Science. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities. To learn more about this program, please visit https://micromasters.mit.edu/ds/.

Please note : edX Inc. has recently entered into an agreement to transfer the edX platform to 2U, Inc., which will continue to run the platform thereafter. The sale will not affect your course enrollment, course fees or change your course experience for this offering. It is possible that the closing of the sale and the transfer of the edX platform may be effectuated sometime in the Fall while this course is running. Please be aware that there could be changes to the edX platform Privacy Policy or Terms of Service after the closing of the sale. However, 2U has committed to preserving robust privacy of individual data for all learners who use the platform. For more information see the edX Help Center.

What you'll learn

• Understand principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning
• Implement and analyze models such as linear models, kernel machines, neural networks, and graphical models
• Choose suitable models for different applications
• Implement and organize machine learning projects, from training, validation, parameter tuning, to feature engineering.
• Introduction
• Linear classifiers, separability, perceptron algorithm
• Maximum margin hyperplane, loss, regularization
• Stochastic gradient descent, over-fitting, generalization
• Linear regression
• Recommender problems, collaborative filtering
• Non-linear classification, kernels
• Learning features, Neural networks
• Deep learning, back propagation
• Recurrent neural networks
• Recurrent neural networks
• Generalization, complexity, VC-dimension
• Unsupervised learning: clustering
• Generative models, mixtures
• Mixtures and the EM algorithm
• Learning to control: Reinforcement learning
• Reinforcement learning continued
• Applications: Natural Language Processing
• Automatic Review Analyzer
• Digit Recognition with Neural Networks
• Reinforcement Learning