Machine Learning is rising, Our team of experts have searched thousands of courses so you don’t have to. We has come up with this list of Courses Tutorials, Classes, Training for Machine Learning,This list includes mostly free courses (some of top notch paid course included as well) from top schools and big name institutes. These courses are ideal for beginners, intermediates, as well as experts to level up your Machine Learning skills.’

## 1. Machine Learning for Data Science and Analytics via edx, by Columbia University

**Required Effort:** 5 Weeks, 7-10 hours per week.

**Total Students Enrolled:** 122,845+

**Cost:** Free

**Level:** Introductory

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…[ReadMore](https://www.edx.org/course/columbiaxds102x?utm_medium=affiliate_partner&utm_source=coursewire-course-wire)

**Why Choose This Course:**

- 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

You can enroll **HERE**

## 2. Data Science: Machine Learning via edx, by Harvard University

**Required Effort:** 8 Weeks, 2-4 hours per week.

**Total Students Enrolled:** 83,571+

**Cost:** Free

**Level:** Introductory

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…[ReadMore](https://www.edx.org/course/harvardxph1258x?utm_source=coursewire-course-wire&utm_medium=affiliate_partner)

**Why Choose This Course:**

- 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?

You can enroll **HERE**

## 3. Machine Learning with Python: A Practical Introduction via edx, by IBM

**Required Effort:** 5 Weeks, 4-6 hours per week.

**Total Students Enrolled:** 36,457+

**Cost:** Free

**Level:** Introductory

This Machine Learning with Python course dives into the basics of Machine Learning using Python, an approachable and well-known programming language. You’ll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a…ReadMore

**Why Choose This Course:**

- Supervised vs Unsupervised Machine Learning
- How Statistical Modeling relates to Machine Learning, and how to do a comparison of each.
- Different ways machine learning affects society

You can enroll **HERE**

## 3. Machine Learning with Python: A Practical Introduction via edx, by IBM

**Required Effort:** 5 Weeks, 4-6 hours per week.

**Total Students Enrolled:** 36,457+

**Cost:** Free

**Level:** Introductory

This Machine Learning with Python course dives into the basics of Machine Learning using Python, an approachable and well-known programming language. You’ll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a…ReadMore

**Why Choose This Course:**

- Supervised vs Unsupervised Machine Learning
- How Statistical Modeling relates to Machine Learning, and how to do a comparison of each.
- Different ways machine learning affects society

You can enroll **HERE**

## 4. Foundations of Data Science: Prediction and Machine Learning via edx, by University of California, Berkeley

**Required Effort:** 6 Weeks, 4-6 hours per week.

**Total Students Enrolled:** 17,064+

**Cost:** Free

**Level:** Introductory

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…[ReadMore](https://www.edx.org/course/berkeleyxdata83x?utm_source=coursewire-course-wire&utm_medium=affiliate_partner)

**Why Choose This Course:**

- 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

You can enroll **HERE**

## 5. Essential Math for Machine Learning: Python Edition via edx, by Microsoft

**Required Effort:** 6 Weeks, 6-8 hours per week.

**Total Students Enrolled:** 82,542+

**Cost:** Free

**Level:** Intermediate

Want to study machine learning or artificial intelligence, but worried that your math skills may not be up to it? Do words like “algebra’ and “calculus” fill you with dread? Has it been so long since you studied math at school that you’ve forgotten much of what you…ReadMore

**Why Choose This Course:**

After completing this course, you will be familiar with the following mathematical concepts and techniques:

- Equations, Functions, and Graphs
- Differentiation and Optimization
- Vectors and Matrices
- Statistics and Probability

You can enroll **HERE**

## 6. Principles of Machine Learning: Python Edition via edx, by Microsoft

**Required Effort:** 6 Weeks, 6-8 hours per week.

**Total Students Enrolled:** 35,534+

**Cost:** Free

**Level:** Intermediate

Machine learning uses computers to run predictive models that learn from existing data in order to forecast future behaviors, outcomes, and trends.

In this data science course, you will be given clear explanations of machine learning theory combined with practical scenarios and hands-on…ReadMore

**Why Choose This Course:**

After completing this course, you will be familiar with the following concepts and techniques:

- Data exploration, preparation and cleaning
- Supervised machine learning techniques
- Unsupervised machine learning techniques
- Model performance improvement

You can enroll **HERE**

## 7. Principles of Machine Learning: R Edition via edx, by Microsoft

**Required Effort:** 6 Weeks, 6-8 hours per week.

**Total Students Enrolled:** 14,992+

**Cost:** Free

**Level:** Intermediate

Machine learning uses computers to run predictive models that learn from existing data in order to forecast future behaviors, outcomes, and trends.

In this data science course, you will be given clear explanations of machine learning theory combined with practical scenarios and hands-on…ReadMore

**Why Choose This Course:**

After completing this course, you will be familiar with the following concepts and techniques:

- Data exploration, preparation and cleaning
- Supervised machine learning techniques
- Unsupervised machine learning techniques
- Model performance improvement

You can enroll **HERE**

## 8. Essential Math for Machine Learning: R Edition via edx, by Microsoft

**Required Effort:** 6 Weeks, 6-8 hours per week.

**Total Students Enrolled:** 13,995+

**Cost:** Free

**Level:** Intermediate

Want to study machine learning or artificial intelligence, but worried that your math skills may not be up to it? Do words like “algebra’ and “calculus” fill you with dread? Has it been so long since you studied math at school that you’ve forgotten much of what you…ReadMore

**Why Choose This Course:**

- Familiarity with Equations, Functions, and Graphs
- Differentiation and Optimization
- Vectors and Matrices
- Statistics and Probability

You can enroll **HERE**

## 9. Developing Big Data Solutions with Azure Machine Learning via edx, by Microsoft

**Required Effort:** 4 Weeks, 3-4 hours per week.

**Total Students Enrolled:** 13,635+

**Cost:** Free

**Level:** Intermediate

The past can often be the key to predicting the future. Big data from historical sources is a valuable resource for identifying trends and building machine learning models that apply statistical patterns and predict future outcomes.

This course introduces Azure Machine Learning, and…[ReadMore](https://www.edx.org/course/microsoftdat228x?utm_medium=affiliate_partner&utm_source=coursewire-course-wire)

**Why Choose This Course:**

- How to create predictive web services with Azure Machine Learning
- How to work with big data sources in Azure Machine Learning
- How to integrate Azure Machine Learning into big data batch processing pipelines
- How to integrate Azure Machine Learning into real-time big data processing solutions

You can enroll **HERE**

## 10. Data Science and Machine Learning Capstone Project via edx, by IBM

**Required Effort:** 6 Weeks, 3-4 hours per week.

**Total Students Enrolled:** 8,047+

**Cost:** Free

**Level:** Intermediate

Employers really care about how well can you apply your knowledge and skills to solve real world problems. Now that you’ve taken several courses on Data Science and Machine Learning, its time to put your learning to practice and work on a data problem involving a real life scenario.

New…ReadMore

**Why Choose This Course:**

- Demonstrate knowledge of Data Science and Machine Learning
- Apply Data Science process to a real life scenario
- Explore New York City – 311 Complaints and Housing datasets
- Analyze and Visualize data using Python
- Perform feature engineering exercise using Python
- Build and validate predictive machine learning model using Python
- Create and share Actionable Insights to real life data problems

You can enroll **HERE**

## 10. Data Science and Machine Learning Capstone Project via edx, by IBM

**Required Effort:** 6 Weeks, 3-4 hours per week.

**Total Students Enrolled:** 8,047+

**Cost:** Free

**Level:** Intermediate

Employers really care about how well can you apply your knowledge and skills to solve real world problems. Now that you’ve taken several courses on Data Science and Machine Learning, its time to put your learning to practice and work on a data problem involving a real life scenario.

New…ReadMore

**Why Choose This Course:**

- Demonstrate knowledge of Data Science and Machine Learning
- Apply Data Science process to a real life scenario
- Explore New York City – 311 Complaints and Housing datasets
- Analyze and Visualize data using Python
- Perform feature engineering exercise using Python
- Build and validate predictive machine learning model using Python
- Create and share Actionable Insights to real life data problems

You can enroll **HERE**

## 11. Amazon SageMaker: Simplifying Machine Learning Application Development via edx, by Amazon Web Services

**Required Effort:** 4 Weeks, 2-4 hours per week.

**Total Students Enrolled:** 6,686+

**Cost:** Free

**Level:** Intermediate

Machine learning is one of the fastest growing areas in technology and a highly sought after skillset in today’s job market.

This course will teach you, an application developer, how to use Amazon SageMaker to simplify the integration of Machine Learning into your applications. Key…ReadMore

**Why Choose This Course:**

- Key problems that Machine Learning can address and ultimately help solve
- How to train a model using Amazon SageMaker’s built-in algorithms and a Jupyter Notebook instance
- How to publish a model using Amazon SageMaker
- How to integrate the published SageMaker endpoint with an application

You can enroll **HERE**

## 12. Dynamic Programming: Applications In Machine Learning and Genomics via edx, by The University of California, San Diego

**Required Effort:** 4 Weeks, 8-10 hours per week.

**Total Students Enrolled:** 6,376+

**Cost:** Free

**Level:** Intermediate

If you look at two genes that serve the same purpose in two different species, how can you rigorously compare these genes in order to see how they have evolved away from each other?

In the first part of the course, part of the Algorithms and Data Structures MicroMasters program, we will…[ReadMore](https://www.edx.org/course/ucsandiegoxalgs205x?utm_medium=affiliate_partner&utm_source=coursewire-course-wire)

**Why Choose This Course:**

- Dynamic programming and how it applies to basic string comparison algorithms
- Sequence alignment, including how to generalize dynamic programming algorithms to handle different cases
- Hidden markov models
- How to find the most likely sequence of events given a collection of outcomes and limited information
- Machine learning in sequence alignment

“This is an extraordinary course. It requires commitment and a fair amount of time, but this is what implies the approach of guiding students step-by-step to implement themselves the algorithms. In my opinion, this is the best way to fully understand how algorithms work.”

— Previous Student

You can enroll **HERE**

## 13. Machine Learning via edx, by Columbia University

**Required Effort:** 12 Weeks, 8-10 hours per week.

**Total Students Enrolled:** 130,673+

**Cost:** Free

**Level:** Advanced

Machine Learning is the basis for the most exciting careers in data analysis today. You’ll learn the models and methods and apply them to real world situations ranging from identifying trending news topics, to building recommendation engines, ranking sports teams and plotting the path of…[ReadMore](https://www.edx.org/course/columbiaxcsmm102x?utm_medium=affiliate_partner&utm_source=coursewire-course-wire)

**Why Choose This Course:**

- Supervised learning techniques for regression and classification
- Unsupervised learning techniques for data modeling and analysis
- Probabilistic versus non-probabilistic viewpoints
- Optimization and inference algorithms for model learning

You can enroll **HERE**

## 13. Machine Learning via edx, by Columbia University

**Required Effort:** 12 Weeks, 8-10 hours per week.

**Total Students Enrolled:** 130,673+

**Cost:** Free

**Level:** Advanced

Machine Learning is the basis for the most exciting careers in data analysis today. You’ll learn the models and methods and apply them to real world situations ranging from identifying trending news topics, to building recommendation engines, ranking sports teams and plotting the path of…[ReadMore](https://www.edx.org/course/columbiaxcsmm102x?utm_medium=affiliate_partner&utm_source=coursewire-course-wire)

**Why Choose This Course:**

- Supervised learning techniques for regression and classification
- Unsupervised learning techniques for data modeling and analysis
- Probabilistic versus non-probabilistic viewpoints
- Optimization and inference algorithms for model learning

You can enroll **HERE**

## 14. Machine Learning Fundamentals via edx, by The University of California, San Diego

**Required Effort:** 10 Weeks, 8-10 hours per week.

**Total Students Enrolled:** 62,162+

**Cost:** Free

**Level:** Advanced

Do you want to build systems that learn from experience? Or exploit data to create simple predictive models of the world?

In this course, part of the Data Science MicroMasters program, you will learn a variety of supervised and unsupervised learning algorithms, and the theory behind…[ReadMore](https://www.edx.org/course/ucsandiegoxds220x?utm_source=coursewire-course-wire&utm_medium=affiliate_partner)

**Why Choose This Course:**

- Classification, regression, and conditional probability estimation
- Generative and discriminative models
- Linear models and extensions to nonlinearity using kernel methods
- Ensemble methods: boosting, bagging, random forests
- Representation learning: clustering, dimensionality reduction, autoencoders, deep nets

You can enroll **HERE**

## 14. Machine Learning Fundamentals via edx, by The University of California, San Diego

**Required Effort:** 10 Weeks, 8-10 hours per week.

**Total Students Enrolled:** 62,162+

**Cost:** Free

**Level:** Advanced

Do you want to build systems that learn from experience? Or exploit data to create simple predictive models of the world?

In this course, part of the Data Science MicroMasters program, you will learn a variety of supervised and unsupervised learning algorithms, and the theory behind…[ReadMore](https://www.edx.org/course/ucsandiegoxds220x?utm_source=coursewire-course-wire&utm_medium=affiliate_partner)

**Why Choose This Course:**

- Classification, regression, and conditional probability estimation
- Generative and discriminative models
- Linear models and extensions to nonlinearity using kernel methods
- Ensemble methods: boosting, bagging, random forests
- Representation learning: clustering, dimensionality reduction, autoencoders, deep nets

You can enroll **HERE**

## 15. Machine Learning with Python: from Linear Models to Deep Learning via edx, by Massachusetts Institute of Technology

**Required Effort:** 14 Weeks, 10-14 hours per week.

**Total Students Enrolled:** 46,018+

**Cost:** Free

**Level:** Advanced

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…ReadMore

**Why Choose This Course:**

- 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.

You can enroll **HERE**

## 16. Robotics: Vision Intelligence and Machine Learning via edx, by University of Pennsylvania

**Required Effort:** 12 Weeks, 8-10 hours per week.

**Total Students Enrolled:** 32,834+

**Cost:** Free

**Level:** Advanced

How do robots “see”, respond to and learn from their interactions with the world around them? This is the fascinating field of visual intelligence and machine learning. Visual intelligence allows a robot to “sense” and “recognize” the surrounding environment. It also enables a robot to “learn”…[ReadMore](https://www.edx.org/course/pennxrobo2x?utm_source=coursewire-course-wire&utm_medium=affiliate_partner)

**Why Choose This Course:**

- The fundamentals of image filtering and tracking, and how to apply those principles to face detection, mosaicking and stabilization
- How to use geometric transformations to determine 3D poses from 2D images for augmented reality tasks and visual odometry for robot localization
- How to recognize objects and the basics of visual learning and neural networks for the purpose of classification

You can enroll **HERE**

## 17. Machine Learning via edx, by The University of Texas at Austin

**Required Effort:** 12 Weeks, 8-12 hours per week.

**Total Students Enrolled:** 1,818+

**Cost:** Free

**Level:** Advanced

Tools from machine learning are now ubiquitous in the sciences with applications in engineering, computer vision, and biology, among others. This class introduces the fundamental mathematical models, algorithms, and statistical tools needed to perform core tasks in machine learning. Applications of…ReadMore

**Why Choose This Course:**

○ Techniques for supervised learning including classification and regression.

○ Algorithms for unsupervised learning including feature extraction.

○ Statistical methods for interpreting models generated by learning algorithms.

You can enroll **HERE**