Cursarium logoCursarium
intermediateCertificate$300

Machine Learning with Python: from Linear Models to Deep Learning

by Regina Barzilay & Tommi Jaakkola · edX

4.6
(1,800 reviews)
50K+ enrolled15 weeksUpdated 2024-08
Go to Course

Our Verdict

Worth it — with caveats

MIT's 6.86x "Machine Learning with Python: from Linear Models to Deep Learning" is a rigorous, graduate-level survey (taught by Regina Barzilay and Tommi Jaakkola) that takes you from perceptrons and SVMs through neural networks, clustering, and reinforcement learning, with five auto-graded Python projects as the core of the experience. It is the third course in MIT's Statistics and Data Science MicroMasters and is widely regarded as the most demanding of that series, with MIT itself estimating ~12 hours/week and learner reviews reporting 7-15 hours/week depending on the project. The standout praise is consistent: the auto-graded projects (digit recognition, an automatic review analyzer, reinforcement learning) are described as "remarkable" and force you to implement algorithms from scratch in NumPy. The most common complaints are equally consistent: uneven lecture delivery, terse problem sets, and lectures that assume you already have multivariable calculus, probability, and linear algebra, making it a poor first ML course. Verified learner sentiment is genuinely polarized rather than uniformly glowing.

It is an excellent choice if you already have solid Python and college-level math (multivariable calculus, probability, linear algebra) and want depth plus hands-on implementation, but a frustrating one as a first ML course. Take it conditional on having the prerequisites and tolerance for terse, theory-heavy delivery; otherwise a gentler course will serve you better first.

Best for: Learners with real Python fluency (functions, OOP, recursion, NumPy comfort) and college-level math who want to implement ML algorithms from scratch rather than just call scikit-learn, people pursuing the MIT Statistics and Data Science MicroMasters or a verified MIT credential, and self-studiers who want the theory and intuition behind classifiers, neural nets, and RL with formalism kept to the minimum necessary.

Skip if: Absolute beginners or career-switchers taking their first ML course, anyone who wants to learn Python within the course (it is assumed, not taught), learners who prefer polished video lectures and heavy hand-holding, and people who mainly want to apply pre-built libraries quickly rather than derive and code algorithms. Multiple verified reviews explicitly recommend more beginner-friendly alternatives for newcomers.

About This Course

MIT course covering linear classifiers, non-linear models, recommender systems, and neural networks with Python.

What You'll Learn

Build and train linear classifiers from scratch: the perceptron algorithm, hinge loss, and support vector machines, including notions of separability and generalization
Move to non-linear models with kernel methods, and implement linear/ridge regression and collaborative-filtering recommender systems
Construct feed-forward neural networks and understand backpropagation, plus exposure to convolutional and recurrent architectures for deep learning
Apply unsupervised learning: k-means and k-medoids clustering, the EM algorithm, and generative/probabilistic models
Understand and implement reinforcement learning algorithms and their applications
Reason about core ML theory: representation, over-fitting, regularization, generalization, and VC dimension
Implement these methods in Python/NumPy through auto-graded projects (e.g., digit recognition with neural networks, an automatic review analyzer, and an RL project)

Curriculum

Unit 0 — Course Overview, Homework 0, Project 0

Onboarding, environment setup, and a warm-up project to confirm Python and math readiness before the graded units begin.

Unit 1 — Linear Classifiers and Generalizations

Linear classification, separability, the perceptron algorithm, hinge loss, and support vector machines; foundational ideas of generalization.

Unit 2 — Nonlinear Classification, Linear Regression, Collaborative Filtering

Kernel methods for non-linear classification, linear/ridge regression, and recommender systems via collaborative filtering.

Unit 3 — Neural Networks

Feed-forward neural networks and backpropagation, extending into deep learning (including convolutional and recurrent networks).

Unit 4 — Unsupervised Learning

Clustering with k-means and k-medoids, the EM algorithm, generative/probabilistic models, and links back to recommendation.

Unit 5 — Reinforcement Learning

Reinforcement learning algorithms and applications, culminating in a hands-on RL project.

Prerequisites

  • Proficiency in Python (functions, tuples/lists, mutability, recursion, dictionaries, and object-oriented programming) — Python is NOT taught in the course
  • Single- and multi-variable calculus
  • Probability
  • Basic linear algebra (vectors, matrices)
  • Comfort with mathematical reasoning and formal notation

Instructor

Regina Barzilay & Tommi Jaakkola

Instructor · edX

Pros & Cons

Pros

  • Auto-graded Python projects are repeatedly called the highlight ("remarkable"): you implement algorithms from scratch (digit recognition, automatic review analyzer, reinforcement learning) rather than just calling libraries
  • Genuine MIT graduate-level depth and rigor — covers the full arc from linear models through deep learning and reinforcement learning, including underlying theory (regularization, VC dimension)
  • Balances theory and intuition while keeping math to the minimum needed: reviewers note it is 'NOT a course for mathematicians' yet still 'NOT a course for programmers' — it teaches the why, not just the how
  • Counts toward the MITx Statistics and Data Science MicroMasters and offers a verified MIT-branded certificate; free audit access is available
  • Active learner community (e.g., class Discord) that reviewers credit with keeping them motivated and unblocked on projects

Cons

  • Steep prerequisite wall: multivariable calculus, probability, linear algebra, and fluent Python are assumed up front, making it a poor first ML course (multiple reviews say beginners should start elsewhere)
  • Uneven teaching delivery: reviewers cite some lecturers' presentation style and poor audio/sound quality, and several call problem sets terse, under-explained, or 'horribly delivered'
  • High and spiky time commitment (project weeks can hit ~15 hours), which can be punishing alongside a job
  • Polarized reception: alongside strong praise there are sharply negative reviews (e.g., 'terrible course,' homework 'not explained'), so experience varies a lot by background and expectations

Alternatives To Consider

Machine Learning Specialization

Coursera

View course

Practical Deep Learning for Coders

fast.ai

View course

Machine Learning

Stanford Online

View course

Frequently Asked Questions

Is Machine Learning with Python: from Linear Models to Deep Learning free?

Machine Learning with Python: from Linear Models to Deep Learning is $300. Free to audit; the verified certificate is US$300 (matches the catalog). Audit access is time-limited and excludes graded problem sets/exams and the certificate. The course is the 3rd of 5 in MIT's Statistics and Data Science MicroMasters, which has its own separate program cost if pursued end-to-end.

Who is Machine Learning with Python: from Linear Models to Deep Learning for?

Learners with real Python fluency (functions, OOP, recursion, NumPy comfort) and college-level math who want to implement ML algorithms from scratch rather than just call scikit-learn, people pursuing the MIT Statistics and Data Science MicroMasters or a verified MIT credential, and self-studiers who want the theory and intuition behind classifiers, neural nets, and RL with formalism kept to the minimum necessary.

What will you learn in Machine Learning with Python: from Linear Models to Deep Learning?

Build and train linear classifiers from scratch: the perceptron algorithm, hinge loss, and support vector machines, including notions of separability and generalization; Move to non-linear models with kernel methods, and implement linear/ridge regression and collaborative-filtering recommender systems; Construct feed-forward neural networks and understand backpropagation, plus exposure to convolutional and recurrent architectures for deep learning; Apply unsupervised learning: k-means and k-medoids clustering, the EM algorithm, and generative/probabilistic models.

What are the prerequisites for Machine Learning with Python: from Linear Models to Deep Learning?

Proficiency in Python (functions, tuples/lists, mutability, recursion, dictionaries, and object-oriented programming) — Python is NOT taught in the course; Single- and multi-variable calculus; Probability; Basic linear algebra (vectors, matrices); Comfort with mathematical reasoning and formal notation.

Is Machine Learning with Python: from Linear Models to Deep Learning worth it?

It is an excellent choice if you already have solid Python and college-level math (multivariable calculus, probability, linear algebra) and want depth plus hands-on implementation, but a frustrating one as a first ML course. Take it conditional on having the prerequisites and tolerance for terse, theory-heavy delivery; otherwise a gentler course will serve you better first.

How we reviewed this course

This is an independent editorial assessment by Cursarium, based on edX's published course materials and aggregated public learner feedback (last reviewed 2026-06). We have not independently completed the course. Links to providers are standard references, not paid placements.

Sources

$300
Go to Course