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Machine Learning Coursera Courses

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New to machine learning and seeking ways to enhance your knowledge? Or maybe you work in an industry with artificial intelligence and need a machine learning course to position yourself for advancement?  

Either way, a machine learning Coursera course is worth considering. There are introductory courses to choose from if you’re just getting started, or you can begin with intermediate or advanced options to level up your knowledge. 

Benzinga is here to help you find a course that fits your needs and busy lifestyle. 

Best Machine Learning Courses by Coursera:

Here’s a quick look at Benzinga’s top picks: 

What Makes a Machine Learning Coursera Course Great?

Here are some factors to consider as you sift through machine learning Coursera courses to find the best fit. 

Comprehensive 

Does the course cover all the key topics you’re most interested in? The best machine learning Coursera courses begin with the basics and transition to vital concepts you need to master the art of machine learning in the classroom or workplace. Take a look at the syllabus to learn more about what to expect from the course. 

Supplementary Resources 

Does the instructor implement supplementary resources throughout the course? Video lectures are an effective way to introduce you to the basics, but you will get even more out of the class if it features demonstrations, supplementary readings, quizzes and hands-on exercises. 

Self-Paced

There’s so much to learn about machine learning. You want a course that’s self-paced and doesn’t require you to meet strict deadlines. This allows you to spend as much time as you need on select modules to grasp challenging concepts.

Best Machine Learning Courses for Beginners

These introductory courses are a good starting point for machine learning beginners. 

1. Machine Learning for All by the University of London 

Who it’s for: Beginners

Price: Free

Are you pursuing a career in machine learning or want to learn more to expand your knowledge? This 22-hour course from the University of London is worth considering. 

It covers the essentials, and you don’t need a programming background to comprehend the material. You’ll learn how modern machine learning technologies work, how data impacts machine learning results and more. 

Machine Learning for All is comprised of 4 modules: 

  • Machine Learning
  • Data Features
  • Machine Learning in Practice 
  • Your Machine Learning Project

The course is led by Dr. Marco Gillies, senior lecturer in the computing department. He provides instruction through a series of videos and readings. You’ll also be tasked with taking a series of quizzes to assess your comprehension of the material. 

There’s no cost to enroll. 

Enroll now

2. Machine Learning by Stanford University

Who it’s for: Beginners

Price: Free

Offered by Stanford University, this introductory course is also ideal for individuals who want to learn the fundamentals of machine learning. It is facilitated by Adjunct Professor Andrew Ng.

Machine Learning entails the following modules: 

  • Introduction 
  • Linear Regression with One Variable 
  • Linear Algebra Review
  • Linear Regression with Multiple Variables 
  • Octave/MatLab Tutorial
  • Logistic Regression 
  • Regularization 
  • Neural Networks: Representation 
  • Neural Networks: Learning
  • Advice for Applying Machine Learning 
  • Machine Learning System Design
  • Support Vector Machines
  • Unsupervised Learning 
  • Dimensionality Reduction 
  • Anomaly Detection 
  • Recommender Systems
  • Large Scale Machine Learning
  • Application Example: Photo OCR 

You’ll walk away from the course with the knowledge and skills required to leverage machine learning techniques to solve real-world issues. It takes approximately 56 hours to reach the finish line. 

Enroll now.

3. Machine Learning Foundations: A Case Study Approach by the University of Washington 

Who it’s for: Beginners

Price: Free

Interested in learning how to use machine learning to analyze data and improve business operations? Look no further than this introductory course. It is the 1st course in the machine learning specialization from the University of Washington. 

Machine Learning Foundations: A Case Study Approach consists of video lectures, readings and quizzes. Instruction is also provided through 7 case studies: 

  • Welcome
  • Regression: Predicting House Price
  • Classification: Analyzing Sentiment
  • Clustering and Similarity: Retrieving Documents 
  • Recommending Products
  • Deep Learning: Searching Images
  • Closing Remarks 

The class is co-facilitated by Carlos Guestrin, Amazon professor of machine learning, and Emily Fox, Amazon professor of machine learning in statistics. 

Enroll now

4. Structuring Machine Learning Projects by DeepLearning.AI 

Who it’s for: Beginners

Price: Free

Coursera founder Andrew Ng brings you this free introductory course. With the assistance of Younes Bensouda Mourri, a teaching assistant with deeplearning.ai, and Kian Katanforoosh, founder of Workera, he teaches you how to create a successful machine learning project. 

You’ll also learn how to identify and diagnose machine learning system errors and comprehend complex machine learning settings. Plus some lessons teach you how to implement end-to-end, multi-task and transfer learning. 

It takes approximately 5 hours to work through the video lectures, readings and quizzes. 

Enroll now

Intermediate Machine Learning Coursera Courses

Once you have solid foundational knowledge of machine learning, move on to these intermediate courses. 

5. Machine Learning: Regression by the University of Washington 

Who it’s for: Intermediate students

Price: Free

Machine Learning: Regression is the 2nd installment of the machine learning specialization from the University of Washington. Instructors Carlos and Emily Fox use the“Predicting Housing Prices” case study to delve into regression models. 

Here’s a breakdown of the course.

  • Welcome
  • Simple Linear Regression
  • Multiple Regression 
  • Assessing Performance
  • Ridge Regression 
  • Feature Selection and Lasso
  • Nearest Neighbors and Kernel Regression 
  • Closing Remarks 

The modules feature video lessons, reading and quizzes to ensure you comprehend the key concepts presented in the course. It’s best if you allocate at least 35 hours in your schedule to work through the material. 

You should complete Machine Learning Foundations: A Case Study Approach before you register. 

Enroll now

6. Machine Learning: Classification by the University of Washington 

Who it’s for: Intermediate students 

Price: Free

In the 3rd component of the machine learning specialization from the University of Washington, you’ll work through case studies that address practical ways to analyze sentiment. You’ll also learn how to predict the likelihood of a borrower defaulting on a loan. 

Machine Learning: Classification includes the following modules: 

  • Welcome!
  • Linear Classifiers and Logistic Regression 
  • Learning Linear Classifiers
  • Overfitting and Regularization in Logistic Regression 
  • Decision Trees
  • Preventing Overfitting in Decision Trees
  • Handling Missing Data
  • Boosting 
  • Precision-Recall 
  • Scaling to Huge Datasets and Online Learning 

This course is also co-instructed by Carlos Guestrin and Emily Fox. Complete Machine Learning: Regression before you enroll. 

Enroll now

7. Machine Learning: Clustering and Retrieval by the University of Washington 

Who it’s for: Intermediate students 

Price: Free

The last installment of the machine specialization from the University of Washington dives into proven strategies that you can use to identify pertinent data without spending hours analyzing documents. This practice is referred to as clustering and retrieval, and instruction is provided through the “Finding Similar Documents” case study. 

Machine Learning: Clustering and Retrieval features 6 core modules: 

  • Welcome
  • Nearest Neighbor Search
  • Clustering with K-Means
  • Mixture Models 
  • Mixed Membership Modeling via Latent Dirichlet Allocation 
  • Hierarchical Clustering and Closing Remarks

Expect to spend 14 hours on the video lessons, readings and quizzes. The class is free, but you want to complete Machine Learning: Classification before you sign up to get the most from your online learning experience. 

Enroll now

Advanced Machine Learning Coursera Courses

If you have the skills and expertise to implement artificial intelligence in the field, you may find these machine learning courses useful. 

8. Introduction to Deep Learning by the Higher School of Economics by the National Research University Higher School of Economics 

Who it’s for: Advanced students 

Price: Free

Presented by the National Research University Higher School of Economics, Introduction to Deep Learning covers the essentials of modern neural networks and how they are applied in natural language and computer vision. 

This advanced course is part 1 of the Advanced Machine Learning Specialization and includes a project that allows you to put your newfound skills to use in a practice setting. 

Here’s a quick look at the course syllabus: 

  • Introduction to Optimization 
  • Introduction to Neural Networks
  • Deep Learning for Images
  • Unsupervised Representation Learning 
  • Deep Learning for Sequences
  • Final Project 

A seat in this course is free. 

Enroll now

9. Deep Learning in Computer Vision by the National Research University Higher School of Economics

Who it’s for: Advanced students

Price: Free

Deep Learning in Computer Vision is another stellar offering from the National Research University Higher School of Economics and a component of the Advanced Machine Learning Specialization. It’s designed to provide insight into computer vision for students who are familiar with machine learning. 

Senior Lecturers Alexey Artemov and Anton Konushin categorizes the lessons into the following sections: 

  • Introduction to Image Processing and Computer Vision 
  • Convolutional Features for Visual Recognition 
  • Object Detection 
  • Object Tracking and Action Recognition 
  • Image Segmentation and Synthesis 

This 5-week course takes 13 hours to complete. Grab a free seat today. 

Enroll now

10. Bayesian Methods for Machine Learning by the National Research University Higher School of Economics

Who it’s for: Advanced students

Price: Free

Interested in learning more about Bayesian methods? This advanced course from the National Research University Higher School of Economics discusses the fundamentals along with ways to automate your workflow and apply Bayesian methods to deep learning to generate new images. 

Bayesian Methods for Machine Learning entails 2 primary segments: 

  • Introduction to Bayesian Methods and Conjugate Priors
  • Expectation-Maximization Algorithm 

It is co-instructed by Researcher Alexander Novikov and Senior Research Scientist Daniil Polykovskiy. Both are HSE Faculty in the computer science department. 

Enrollment is free and includes access to a host of video lectures, supplementary readings and quizzes. 

Enroll now

11. Machine Learning with Python by IBM

Who it’s for: Intermediate students 

Price: Free 

This free intermediate course from IBM is a part of the Data Science and AI Engineering Professional Certificate programs. Students discover the fundamentals of machine learning through Python and how it’s used in the real world. 

There are also lessons on pertinent machine learning algorithms, model evaluation and the critical differences between unsupervised and supervised learning. 

Machine Learning with Python is divided into 6 modules:

  • Introduction to Machine Learning 
  • Regression 
  • Classification 
  • Clustering
  • Recommender System
  • Final Project 

The class is co-instructed by Data Scientists Saeed Aghabozorgi, Ph.D. and Joseph Santarcangelo, Ph.D.

Enroll now

12. Advanced Machine Learning and Signal Processing by IBM 

Who it’s for: Advanced students 

Price: Free

Advanced Machine Learning and Signal Processing is taught by Chief Data Scientist Romeo Kienzler and Senior Data Scientist Nikolay Manchev. This 4-week course is the second component of the Advanced Data Science Specialization from IBM. 

There are lessons on Linear Algebra fundamentals, popular Machine Learning frameworks for SparkML and Scikit-Learn, parallel model tuning and more. 

Below is a preview of the topics covered: 

  • Setting the Stage
  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Digital Signal Processing in Machine Learning 

Register today to get started. It’s free, and you should finish the class in about 27 hours. 

Enroll now

Get Started with a Machine Learning Course from Coursera 

Don’t spend hours searching for a machine learning course. Start with 1 of our recommendations for your skill level.

These courses are free and feature interactive instruction to keep you engaged while you work through the video lectures and supplementary material.

Southern New Hampshire University Online

SNHU Online Offers:

  1. Flexible schedules
  2. Affordable tuition
  3. Online tutoring
  4. Access to electronic research materials
  5. Specialized academic advising
  6. Supportive online community

Learn more at SNHU.