Enroll for Free Online Machine Learning Courses on edX from top Universities like: Massachusetts Institute of Technology, Harvard University, Columbia University and the University of Toronto.
Free Online Machine Learning Courses on edX: Top Universities

1- Data Science: Machine Learning from Harvard University

Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.

Perhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors.

In this course, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.

You will learn about training data, and how to use a set of data to discover potentially predictive relationships. As you build the movie recommendation system, you will learn how to train algorithms using training data so you can predict the outcome for future datasets. You will also learn about overtraining and techniques to avoid it such as cross-validation. All of these skills are fundamental to machine learning.

What you'll learn

The basics of machine learning
How to perform cross-validation to avoid overtraining
Several popular machine learning algorithms
How to build a recommendation system
What is regularization and why it is useful?

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Harvard University is devoted to excellence in teaching, learning, and research, and to developing leaders in many disciplines who make a difference globally. Harvard faculty are engaged with teaching and research to push the boundaries of human knowledge. The University has twelve degree-granting Schools in addition to the Radcliffe Institute for Advanced Study.

2- Machine Learning for Data Science and Analytics form Columbia University

Learn the principles of machine learning and the importance of algorithms.

Machine Learning is a growing field that is used when searching the web, placing ads, credit scoring, stock trading and for many other applications.

This data science course is an introduction to machine learning and algorithms. You will develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics. We will also examine why algorithms play an essential role in Big Data analysis.

What you'll learn

What machine learning is and how it is related to statistics and data analysis
How machine learning uses computer algorithms to search for patterns in data
How to use data patterns to make decisions and predictions with real-world examples from healthcare involving genomics and preterm birth
How to uncover hidden themes in large collections of documents using topic modeling
How to prepare data, deal with missing data and create custom data analysis solutions for different industries
Basic and frequently used algorithmic techniques including sorting, searching, greedy algorithms and dynamic programming

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Columbia University is one of the world's most important centers of research and at the same time a distinctive and distinguished learning environment for undergraduates and graduate students in many scholarly and professional fields. The University recognizes the importance of its location in New York City and seeks to link its research and teaching to the vast resources of a great metropolis.

3- Foundations of Data Science: Prediction and Machine Learning from University of California, Berkeley

Learn how to use machine learning, with a focus on regression and classification, to automatically identify patterns in your data and make better predictions.

One of the principal responsibilities of a data scientist is to make reliable predictions based on data. When the amount of data available is enormous, it helps if some of the analysis can be automated. Machine learning is a way of identifying patterns in data and using them to automatically make predictions or decisions. In this data science course, you will learn basic concepts and elements of machine learning.

The two main methods of machine learning you will focus on are regression and classification. Regression is used when you seek to predict a numerical quantity. Classification is used when you try to predict a category (e.g., given information about a financial transaction, predict whether it is fraudulent or legitimate).

For regression, you will learn how to measure the correlation between two variables and compute a best-fit line for making predictions when the underlying relationship is linear. The course will also teach you how to quantify the uncertainty in your prediction using the bootstrap method. These techniques will be motivated by a wide range of examples.

For classification, you will learn the k-nearest neighbor classification algorithm, learn how to measure the effectiveness of your classifier, and apply it to real-world tasks including medical diagnoses and predicting genres of movies.

The course will highlight the assumptions underlying the techniques, and will provide ways to assess whether those assumptions are good. It will also point out pitfalls that lead to overly optimistic or inaccurate predictions.

What you'll learn

Fundamental concepts of machine learning
Linear regression, correlation, and the phenomenon of regression to the mean
Classification using the k-nearest neighbors algorithm
How to compare and evaluate the accuracy of machine learning models
Basic probability and Bayes’ theorem

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The University of California, Berkeley was chartered in 1868, and its flagship campus — envisioned as a "City of Learning" — was established at Berkeley, on San Francisco Bay. Berkeley faculty consists of 1,582 full-time and 500 part-time faculty members dispersed among more than 130 academic departments and more than 80 interdisciplinary research units. Berkeley alumni have received 28 Nobel prizes, and there are eight Nobel Laureates, 32 MacArthur Fellows, and four Pulitzer Prize winners among the current faculty.

4- Quantum Machine Learning Course from University of Toronto

Quantum computers are becoming available, which begs the question: what are we going to use them for? Machine learning is a good candidate. In this course you will be introduced to several quantum machine learning algorithms and implement them in Python.

The pace of development in quantum computing mirrors the rapid advances made in machine learning and artificial intelligence. It is natural to ask whether quantum technologies could boost learning algorithms: this field of inquiry is called quantum-enhanced machine learning. The goal of this course is to show what benefits current and future quantum technologies can provide to machine learning, focusing on algorithms that are challenging with classical digital computers.

A strong emphasis on implementing the protocols, using open source frameworks in Python. Prominent researchers in the field will give guest lectures to provide extra depth to each major topic. These guest lecturers include Alán Aspuru-Guzik, Seth Lloyd, Roger Melko, and Maria Schuld.

By the end of this course, students will be able to:

Distinguish between quantum computing paradigms relevant for machine learning

Assess expectations for quantum devices on various time scales

Identify opportunities in machine learning for using quantum resources

Implement learning algorithms on quantum computers in Python

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Established in 1827, the University of Toronto is a vibrant and diverse academic community. It includes 80,000 students, 12,000 colleagues holding faculty appointments, 200 librarians, and 6,000 staff members across three distinctive campuses and at many partner sites, including world-renowned hospitals. With over 800 undergraduate programs, 150 graduate programs, and 40 professional programs, U of T attracts students of the highest calibre, from across Canada and from 160 countries around the world. The University is one of the most respected and influential institutions of higher education and advanced research in the world.

5- Machine Learning with Python: from MIT

An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects.

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.

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Massachusetts Institute of Technology — a coeducational, privately endowed research university founded in 1861 — is dedicated to advancing knowledge and educating students in science, technology, and other areas of scholarship that will best serve the nation and the world in the 21st century.  MIT Graduates Admissions Programs 2020 . Through MITx, the Institute furthers its commitment to improving education worldwide.