Machine Learning with Python: from Linear Models to Deep Learning edX
Machine Learning with Python: from Linear Models to Deep Learning (edX)

Machine Learning with Python: from Linear Models to Deep Learning (edX)

Machine Learning with Python: from Linear Models to Deep Learning is a Free Online MOOC Course, Offered by the Massachusetts Institute of Technology via edX. This course is part of the MITx MicroMasters Program in Statistics and Data Science.

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Machine Learning with Python: from Linear Models to Deep Learning (edX)

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 the 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.

Syllabus

Lectures

  • 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, backpropagation
  • 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

Projects

  • Automatic Review Analyzer
  • Digit Recognition with Neural Networks
  • Reinforcement Learning

Teacher

  • Regina Barzilay
  • Tommi Jaakkola

Additional information

Course Delivery

Online

Course Efforts

10-14 Hours

Course Enrollment

Free

Course Language

English

Course Length

15 Weeks

Course Level

Advanced

Course Provider

Course School

Course Subtitles

English

Flexible Learning

Yes

Verified Certificate

Paid

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