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Online Course

Machine Learning with Python-From Linear Models to Deep Learning

Massachusetts Institute of Technology via edX

Overview

If you have specific questions about this course, please contact us atsds-mm@mit.edu.

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/.

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, 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

Projects :

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

Taught by

Regina Barzilay and Tommi Jaakkola

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Reviews

1.6 rating, based on 9 reviews

Start your review of Machine Learning with Python-From Linear Models to Deep Learning

  • Anonymous
    I like Professor Regina Barzilay's part and TAs are awesome. Professor Tommi Jaakkola - I am sure is a wonderful person and extraordinary researcher -but his teaching style makes me feel like I am the stupidest person in the planet. This course would have been lot better had they let TAs do all the lecturing. I thought every MIT professor were as good as Walter Lewin or Patrick Winston. I now know I was wrong, terribly wrong!

    If you are enrolled in this course, compare how they teach support vector machine in the terribly delivered course with how Professor Patrick Winson taught it. (https://www.youtube.com/watch?v=_PwhiWxHK8o).
  • Anonymous
    I agree with the other review that it is the worst MITx course ever. We usually watch MITx courses and feel inspired. After watching most MITx courses on youtube, I feel like most students fail because they don't get good education such as the one at MIT. However, this course is absolute disgrace to be called a MIT course.
  • Anonymous
    Worst MIT courses I ever took. Lectures are ineffective and boring. If this course was to be put on youtube, there will be significantly more dislikes than the likes, that is why they do not put it on youtube.
  • Pra
    “Any fool can make something complicated. It takes a genius to make it simple.” - Woody Guthrie

    I hope the course team thinks through this quote before they repeat that it is a 'graduate level' course as a justification on why this course is poorly delivered.
  • Anonymous
    Definitely a very overloaded course. I do not recommend it at all, besides very expensive the certificate and super high probabilities of losing the investment
  • Anonymous
    This course is horrible, it sucks. I don't recommend it at all. Expensive, poorly explained content a many more unpleasant things.
  • Anonymous
    Professor Tommi S. Jaakkola, please watch this: https://youtu.be/4a0FbQdH3dY?t=3443 (the url is at the specific timestamp so that you won't 'waste' your time). Seriously, if you wrote something ineligibly why wouldn't you redo the video?

    This was the first MOOC I paid for. I enrolled even though it did not have a review then because I was under the assumption that all MIT courses are as great as the ones by Walter Lewin or Eric Lander.

    Lesson learnt, never enroll in a course unless there are excellent reviews. You made a lot of us fool because there were no reviews, but you won't be able to tarnish the reputation of MIT lecturers that Lewin and Lander have built.
  • Anonymous
    I think the reviews below are very harsh. It's true that it is not as good as the Stanford one I followed on Coursera, but still the Python projects are interesting and well done. I feel that the videos are short on purpose, to give you the most important information. This leaves possibility for personal work if you want to dig further. Some people might be frustrated not to get more details directly in the package.
  • Anonymous
    If there is possible to give zero star i would prefer that for this course. Truly disgusting course.

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