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

If you have specific questions about this course, please contact us at [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 the MITx 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/.

What's inside

Learning objectives

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.

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.

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

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

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops understanding of principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning

Develops skills in organizing and implementing machine learning projects including data training, validation, and parameter tuning

Provides a strong foundation for learners who wish to build a career or extend their knowledge in data science

Taught by recognized experts in the field of machine learning: Karene Chu, Regina Barzilay, Tommi Jaakkola

Examines applications of machine learning in a variety of fields including natural language processing, document classification, and recommendation engines

Requires proficiency in Python programming

Reviews summary

Challenging machine learning with python: from linear models to deep learning

Students say this is a challenging but rewarding course. Real students have called it a top-notch course, akin to an actual university course, while difficult to understand. Engaging assignments provide practice in applying machine learning concepts. This course assumes you are already familiar with probability, statistics, linear algebra, and Python coding.

Students say this course has engaging assignments.

"In each of five units, students implement the theory in a skeletal-code project."

"There are homework, a mid-term and a final exam."

"A course taught by fields experts, it was certainly worth my time. Note: be sure to meet the pre-requistes and consider the whole micromaster program!"

Students say this is an actual university course.

"This is a REAL course, for REAL students."

"This is not a MOOC where you fool yourself that you learned something."

"It is just a real university course that keeps away all the idiots."

This course assumes you are already familiar with probability, statistics, linear algebra, and Python coding.

"The probability course would require much more time to complete but the video lectures were more than enough to get full grade. P.S."

"The main lecturer's accent is very bad and from time-to-time confusing."

"The TAs do not seem to know much about the class either, sometimes they are posting on the forum asking how to solve a problem...."

Students say this course is challenging.

"The course “Machine Learning with Python: from Linear Models to Deep Learning” offered by Massachusetts Institute of Technology via edX is an excellent introduction to the field."

"It provides a comprehensive overview of fundamental concepts and techniques, guiding learners through hands-on coding exercises."

"The course strikes a perfect balance between theory and practical application."

"Highly recommended."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Machine Learning with Python: from Linear Models to Deep Learning with these activities:

Review Linear Algebra

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Review fundamental concepts in linear algebra to prepare for discussions in representations, over-fitting, and generalization.

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Review matrix operations and properties.
Practice solving systems of linear equations.
Study the concepts of vector spaces and subspaces.

Introduction to Machine Learning

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Review the foundational concepts of machine learning to establish a strong understanding of the course material.

View Machine Learning, revised and updated edition... on Amazon

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Read the first three chapters of the book.
Complete the practice exercises at the end of each chapter.
Summarize the key concepts in your own words.

Follow Machine Learning Tutorials on YouTube

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Supplement your learning by exploring high-quality tutorials on YouTube, expanding your knowledge and gaining different perspectives on machine learning concepts.

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Search for reputable YouTube channels that offer machine learning tutorials.
Select tutorials that cover topics relevant to the course.
Watch the tutorials and take notes on important concepts.

11 other activities

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Machine Learning Study Group

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Enhance your understanding of machine learning by discussing concepts and solving problems with peers.

Browse courses on Machine Learning

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Find a group of peers who are also taking the course.
Meet regularly to discuss the course material.
Work together on problem sets and projects.

Implement Logistic Regression in Python

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Develop a deeper understanding of logistic regression and gain proficiency in implementing it using Python.

Browse courses on Logistic Regression

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Study the theory behind logistic regression and its applications.
Implement logistic regression from scratch in Python.
Practice using logistic regression on real-world datasets.

Solve Machine Learning Challenges on CodinGame

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Engage in problem-solving and coding challenges to solidify your understanding of machine learning algorithms and their practical applications.

Browse courses on Machine Learning Algorithms

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Register on the CodinGame platform.
Select machine learning challenges and attempt to solve them.
Analyze your solutions and learn from the feedback provided.

MNIST Dataset Challenge

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Practice implementing machine learning algorithms by building a model to classify handwritten digits.

Browse courses on MNIST

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Download the MNIST dataset.
Build a convolutional neural network model.
Train and evaluate your model.
Improve your model's accuracy by experimenting with different architectures and hyperparameters.

Kaggle Machine Learning Competitions

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Test your machine learning skills and learn from others by participating in Kaggle competitions.

Browse courses on Kaggle

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Sign up for a Kaggle account.
Choose a competition that interests you.
Read the competition description and data documentation.
Build a model and submit your predictions.
Analyze your results and learn from others.

Design a Neural Network Architecture

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Challenge yourself to design and evaluate neural network architectures, fostering a deeper understanding of their structure and capabilities.

Browse courses on Neural Networks

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Research different neural network architectures and their applications.
Design a neural network architecture for a specific task.
Train and evaluate the neural network on a dataset.
Analyze the results and optimize the architecture.

Machine Learning Blog Post

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Consolidate your understanding of machine learning by writing a blog post explaining a specific concept or algorithm.

Browse courses on Machine Learning

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Choose a topic that interests you.
Research the topic thoroughly.
Write a clear and concise blog post that explains the topic in detail.
Share your blog post with others.

Machine Learning Mentor

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Strengthen your understanding of machine learning by teaching and assisting others.

Browse courses on Machine Learning

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Identify someone who is less experienced with machine learning than you.
Offer to mentor them.
Meet regularly to discuss their progress and answer their questions.

Develop a Machine Learning Model for a Real-World Problem

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Gain hands-on experience by applying machine learning techniques to solve a real-world problem, enhancing your understanding of the entire machine learning pipeline.

Browse courses on Machine Learning Projects

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Identify a real-world problem that can benefit from machine learning.
Gather and prepare a dataset relevant to the problem.
Select and train a machine learning model.
Evaluate the performance of the model and make improvements.
Deploy the model and monitor its effectiveness.

Machine Learning Project

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Apply your machine learning skills to solve a real-world problem by building a project.

Browse courses on Machine Learning

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Define the problem you want to solve.
Gather and prepare the necessary data.
Build and train a machine learning model.
Evaluate the performance of your model.
Deploy your model and track its performance.

Contribute to Open Source Machine Learning Projects

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Gain practical experience and contribute to the machine learning community by contributing to open source projects.

Browse courses on Machine Learning

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Find an open source machine learning project that interests you.
Familiarize yourself with the project's codebase and documentation.
Identify a way to contribute to the project.
Submit a pull request with your changes.

Career center

Learners who complete Machine Learning with Python: from Linear Models to Deep Learning will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers create and maintain machine learning algorithms, and this course can help you to build a strong foundation for this career. You will study linear classifiers, maximum margin hyperplanes, and stochastic gradient descent. You will also learn how to implement and analyze models such as linear models and neural networks. This will give you the skills you need to succeed as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use machine learning to solve business problems, and this course can help you to develop the skills you need for this role. You will learn about clustering, classification, recommender problems, probabilistic modeling, and reinforcement learning. You will also gain experience implementing and organizing machine learning projects, which will give you a competitive edge in the job market.

See salaries and explore the career path for Data Scientist

Software Engineer

Software Engineers who specialize in machine learning are in high demand, and this course can help you to develop the skills you need to succeed in this field. You will learn about linear regression, neural networks, and deep learning. You will also gain experience implementing and organizing machine learning projects, which will give you the hands-on experience you need to be successful as a Software Engineer.

See salaries and explore the career path for Software Engineer

Quantitative Analyst

Quantitative Analysts use machine learning to make financial decisions, and this course can help you to develop the skills you need for this role. You will learn about linear models, kernel machines, and neural networks. You will also gain experience implementing and organizing machine learning projects, which will give you the analytical skills you need to succeed as a Quantitative Analyst.

See salaries and explore the career path for Quantitative Analyst

Product Manager

Product Managers who have a strong understanding of machine learning are in high demand, and this course can help you to develop the skills you need for this role. You will learn about the principles behind machine learning problems such as classification and regression. You will also gain experience implementing and organizing machine learning projects, which will give you the practical skills you need to succeed as a Product Manager.

See salaries and explore the career path for Product Manager

Business Analyst

Business Analysts who have a strong understanding of machine learning are in high demand, and this course can help you to develop the skills you need for this role. You will learn about the principles behind machine learning problems such as clustering and classification. You will also gain experience implementing and organizing machine learning projects, which will give you the analytical skills you need to succeed as a Business Analyst.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

Operations Research Analysts use machine learning to solve operational problems, and this course can help you to develop the skills you need for this role. You will learn about reinforcement learning and how to implement and organize machine learning projects, which will give you the analytical skills you need to succeed as an Operations Research Analyst.

See salaries and explore the career path for Operations Research Analyst

Data Analyst

Data Analysts who have a strong understanding of machine learning are in high demand, and this course can help you to develop the skills you need for this role. You will learn about the principles behind machine learning problems such as clustering and classification. You will also gain experience implementing and organizing machine learning projects, which will give you the analytical skills you need to succeed as a Data Analyst.

See salaries and explore the career path for Data Analyst

Statistician

Statisticians who have a strong understanding of machine learning are in high demand, and this course can help you to develop the skills you need for this role. You will learn about the principles behind machine learning problems such as clustering and classification. You will also gain experience implementing and organizing machine learning projects, which will give you the analytical skills you need to succeed as a Statistician.

See salaries and explore the career path for Statistician

Financial Analyst

Financial Analysts who have a strong understanding of machine learning are in high demand, and this course can help you to develop the skills you need for this role. You will learn about linear models, kernel machines, and neural networks. You will also gain experience implementing and organizing machine learning projects, which will give you the analytical skills you need to succeed as a Financial Analyst.

See salaries and explore the career path for Financial Analyst

Market Researcher

Market Researchers who have a strong understanding of machine learning are in high demand, and this course can help you to develop the skills you need for this role. You will learn about clustering, classification, and recommender problems. You will also gain experience implementing and organizing machine learning projects, which will give you the analytical skills you need to succeed as a Market Researcher.

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Consultant

Consultants who have a strong understanding of machine learning are in high demand, and this course can help you to develop the skills you need for this role. You will learn about the principles behind machine learning problems such as classification and regression. You will also gain experience implementing and organizing machine learning projects, which will give you the analytical skills you need to succeed as a Consultant.

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Teacher

Teachers who have a strong understanding of machine learning can help their students to develop the skills they need to succeed in the 21st century workforce. This course can help you to learn about the principles behind machine learning problems such as classification and regression. You will also gain experience implementing and organizing machine learning projects, which will give you the hands-on experience you need to teach your students about this important topic.

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Writer

Writers who have a strong understanding of machine learning can help to communicate the importance of this technology to a wider audience. This course can help you to learn about the principles behind machine learning problems such as classification and regression. You will also gain experience implementing and organizing machine learning projects, which will give you the hands-on experience you need to write about this important topic in a clear and engaging way.

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Entrepreneur

Entrepreneurs who have a strong understanding of machine learning can develop new products and services that leverage this technology. This course can help you to learn about the principles behind machine learning problems such as classification and regression. You will also gain experience implementing and organizing machine learning projects, which will give you the hands-on experience you need to develop successful machine learning-based businesses.

See salaries and explore the career path for Entrepreneur