11 Best Machine Learning Courses Reviewed and Rated

Best Machine Learning Courses

 

What is machine learning? It is the science of having the computers act without any exclusive programming. In the last decade, we saw a massive change in the innovation of machine learning.

 

Some examples include practical self-driving cars, image and speech recognition, traffic prediction, virtual assistants and what not.

 

Today, the use of machine learning is widespread; you use it multiple times in your day without even acknowledging or knowing it. Several researchers also believe it is the ideal way to cross the bridge towards human-level Artificial Intelligence.

 

If you, too, are interested in machine learning – how do you do it? Well, for starters, search for the best machine learning courses online and get yourself enrolled. Of the umpteen number of courses, look for the class that fits the bill for you and matches your requirement. You will need to put in some time, hard work, and practice for the course completion.

 

We at TangoLearn did half the work for you. We have compiled a list of the eleven courses for machine learning in consultation with 15 machine learning experts. You can read the course highlights below and select one that suits you best.

 

In This Article

 

11 Best Machine Learning Courses Online With Certifications

1. Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning A-Z™
 

Two adept Data scientists have designed the course to familiarize you with the coding libraries, algorithms, and complex theory in the most simplistic manner.

 

Moreover, the course gives you a hands-on experience through multiple exercises based on real-life examples. In addition, there are R and Python code templates that can be downloaded and used on your project.

 

Rating 4.5 based on 142.500+ reviews
Duration 44h 29m
Level Beginners
Refund Policy 30-day return policy
Certificate Provided Yes
Course Material Provided Yes
Live Classes/Recorded Lessons Recorded lessons
Course Type Paid
Instructor Kirill Eremenko
Scope for Improvement (Cons)Could be made more affordable, course is difficult to comprehend without the basic understanding of Phyton, R and data science.

 

Do you want to learn everything that is to know about machine learning? If yes, then this course is ideal for you. In this course, you learn machine learning with python and R.

 

Topics Covered

In this course to learn machine learning, the following topics are covered:

  1. Data Pre-processing
  2. Regression – (Simple Linear, Multiple Linear, Polynomial, SVR, Decision Tree, and Random Forest)
  3. Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
  4. Clustering: K-Means, Hierarchical Clustering
  5. Association Rule Learning
  6. Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
  7. Natural Language Processing
  8. Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
  9. Dimensionality Reduction: PCA, LDA, Kernel PCA
  10. Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

 

Learning Outcome

A few things that this best machine learning course online will teach you are listed below:

  1. Making precise predictions and machine learning models
  2. Getting familiar with Machine Learning on R and Python
  3. Having a great intuition of the different machine learning models
  4. Performing powerful analysis
  5. Using machine learning for business and personal uses
  6. Employing machine learning to add value to your business
  7. Handling complex topics, such as deep learning NLP and Reinforcement Learning
  8. Getting familiar with advanced techniques, such as Dimensionality Reduction
  9. Developing a robust army of Machine Learning models and combining them to solve all problems
  10. Always knowing the model that works best for all kinds of problems

 

Prerequisites

To take this course you need to know minimum high school level mathematics. Apart from that a basic understanding of data science and python. If you are a newbie, here are the best courses for data science.

 

Is it the right course for you?

This machine learning online course is ideal for:

 

  1. Anyone who aspires to learn machine learning with python can get their basics straight here.
  2. People who need to use Machine Learning in datasets.
  3. Anyone who is not comfortable with coding but aspires to Machine Learning
  4. Students in college who aspire to start a career in data science
  5. Data analysts who wish to grow their understanding of machine learning

 

Reviews by Sidrah Maryam:

It was a great course and helped me a lot in my academics. Thank you for explaining in so much details that despite being a newbie, I was able to implement it without any difficulty.

 

 

2. Machine Learning – Offered by Stanford – [Coursera]

Machine Learning – Offered by Stanford
 

Ranked at number two on our best machine learning course is this Coursera course offered by Stanford University. This is a 100% online course and comes with flexible deadlines. So, you can start and finish on your timeline.

 

The course is available in English, but you can find subtitles in Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, German, Russian, English, Hebrew, Spanish, Hindi, and Japanese.

 

Rating 4.9
Duration 61 hours
Level Beginners
Refund Policy 2-week refund policy
Certificate Provided Yes
Course Material Provided Yes
Live Classes/Recorded Lessons Recorded lessons
Course Type Paid
Instructor Andrew Ng
Scope for Improvement (Cons)Not very interactive, the written resources provided need to be updated, audio-video and test quality could be improved, course is very basic not suited for intermediate users, expensive certificate, 61 hours wont suffice to teach you ML – this course is just an overview

 

Topics covered

Some topics covered in this course are:

  1. Supervised learning
    • parametric/non-parametric algorithms
    • neural networks
    • support vector machines
    • kernels
  2. Unsupervised learning
    • clustering
    • dimensionality reduction
    • recommender systems
    • deep learning
  3. Best practices in machine learning
    • bias/variance theory
    • innovation process in machine learning and AI

 

Learning Outcome

In this machine learning certification course, you will learn an array of things. A few of these things have been addressed below:

  1. Introduction to machine learning, data mining, and statistical pattern recognition
  2. Theoretical underpinnings of learning
  3. Practical know-how to apply the learning
  4. Best practices of Silicon Valley and its application
  5. Application of the learning algorithms to build smart robots, computer vision, database mining, text understanding, medical informatics, audio, and others

 

RK

It is the best online course for any person who wanna learn machine learning. Andrew sir teaches very well. His pace is very good. The insights which you will get in this course turns out to be wonderful.

 

 

3. Machine Learning Specialization – Offered by University of Washington – [Coursera]

Machine Learning Specialization
 

If the Stanford course to learn machine learning did not work for you, then the University of Washington course can be the next apparent pick. It is a seven-month-long course and requires an effort of seven hours per week from your end.

It is not just any online course to learn machine learning, rather a specialization which will give you an in-depth understanding of machine learning and help you be an expert in the field.

 

Rating 4.7
Duration7 months
Level Intermediate
Refund Policy7-day free trial, 2 weeks refund policy
Certificate Provided Yes
Course Material Provided Yes
Live Classes/Recorded Lessons Recorded lessons
Course Type Paid
InstructorEmily Fox and Carlos Guestrin
Scope for Improvement (Cons)Extremely long course, the first 5 weeks could have been cut short, week 6 could have been more detailed, sessions on deep learning should have been more detailed, too much dependant on GraphLab

 

Different Courses Under This Specialization

Course 1 – Machine Learning Foundations: A Case Study Approach

 

This is the foremost machine learning course under the Coursera machine learning certification courses.

We call it one of the best machine learning course online because in this machine learning Bootcamp, you will not be mugging up things. Instead, there will be a case-study approach, which will simplify the learning process for you.

 

What will you learn?

In this course, you will learn:

  1. To understand the problem
  2. Matching the problem to the right machine learning tool
  3. Gauging the correctness of the output

 

Course 2- Machine Learning: Regression

This is the follow-up to the previous machine learning online course.

 

What will you learn?

Some things you will learn in this machine learning course are:

 

  1. Regression – Math behind the model
  2. Advanced concepts, such as optimization algorithms, bias, and variance
  3. Selection of the right machine learning tool or model
  4. Implementation of the solution in Python

 

In this course, the instructor attempts to build on your knowledge engagingly and interactively.

 

Course 3- Machine Learning: Classification

Now, this is the course where there is an actual introduction to machine learning.

 

What will you learn?

Some things covered in this course are:

 

  1. Prediction of the sentiments in the product review dataset
  2. Non-linear models with decision trees
  3. Prediction of loan frauds

 

Course 4- Machine Learning: Clustering & Retrieval

Up until now, four courses have elapsed, so in this final part of the machine learning certification,there will be a further build upon concepts like clustering and retrieval.

 

What will you learn?

This is one of the best machine learning courses to learn:

 

  1. Prediction of desired outcomes from datasets
  2. Clustering data points
  3. Retrieval of pertinent documents based on the clusters

 
This machine learning certification course is presented to you by the University of Washington researchers. This is not an individual course to learn machine learning with Python. Instead, it is an amalgam of four machine learning online courses.

 

4. Machine Learning Fundamentals – [edX]

Machine Learning Fundamentals
 

It is a free machine learning course. However, in the free version of the course, there are three limitations opposed to the paid version.

 

  1. You will not get the shareable certificate.
  2. You will not be able to take graded assignments or exams
  3. You will have limited access to course content.

 

Rating N/A
Duration10 weeks
Level Advanced
Refund PolicyNo
Certificate ProvidedOptional
Course Material Provided Yes
Live Classes/Recorded Lessons Recorded lessons
Course Type Free (certification can be availed by paying a price)
InstructorSanjoy Dasgupta
Scope for Improvement (Cons)You may have trouble with the instructor’s Indian accent, needs you to have a good knowledge base, this free machine learning course is not accessible to learners from Iran, Cuba, and the Crimea region of Ukraine.

 

Topics Covered

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

 

Learning Outcome

You can take up this course to learn machine learning because it is free and offers a lot. A few things that you will learn with this free machine learning course are:

 

  1. Different supervised and unsupervised learning algorithms
  2. Theory and practical approach behind each of them
  3. Classification of images
  4. Separating people based on personality profiles
  5. Identification of key topics from the myriad of documents
  6. Categorizing the documents
  7. Capturing of the semantic structure of words
  8. Building predictive and descriptive models
  9. Analyzing the variety of different kinds of data

 

Prerequisites

This is an advanced-level free machine learning course. So, there are certain prerequisites needed to take up this course.

 

  1. Two previous courses from the MicroMasters program – DSE200x and DSE210x
  2. Former undergrad level knowledge in linear algebra and multivariate calculus

 

Is this course worth it?

This is a self-paced machine learning course.So, you can start and finish at your schedule. You merely have to dedicate eight to ten hours every week, and you are sorted. Having said that, we are not quite so sure about the course, since their aren’t any student reviews to back up the claim.
 

AG

Nice course with all the practical stuffs and nice analysis about each topic but practical part of LDA was restricted for GraphLab users only which is a weak fallback and rest everything is fine

 


 

5. Learn the Basics of Machine Learning – Code Academy

Learn the Basics of Machine Learning
 

This Codecademy machine learning is ideal for you regardless of whether you are a data analyst wanting to elevate your skills or a data set employing machine learning.

 

RatingN/A
Duration20 hours
LevelIntermediate (Knowledge of Python 2 required)
Refund PolicyNo
Certificate ProvidedYes
Course Material ProvidedYes
Live Classes/Recorded LessonsRecorded
Course TypePaid
InstructorN/A
Scope for Improvement (Cons)20 hours is too less of a time period to finish this subject-this implies that this course isn’t very comprehensive

 

In this Codecademy machine learning module, you will learn the foundational algorithms, which will pave the way to progress in your career.

 

Prerequisites

To take this machine learning Bootcamp,you must be comfortable with Python 2, including loops, lists, control flow, and functions. Here are some of the best courses to learn python online.

 

 

6. Grow your machine learning skills – Pluralsight

Grow your machine learning skills
 
In this Pluralsight machine learning, there is a constant attempt to help you develop machine learning and Artificial Intelligence skills.

 

RatingN/A
DurationVariable
LevelAll levels
Refund PolicyNo
Certificate ProvidedYes
Course Material ProvidedYes
Live Classes/Recorded Lessons Recorded
Course Type Paid
InstructorDr. Emmanuel Tsukerman, Abhishek Kumar, and Axel Sirota
Scope for Improvement (Cons)No certificate in standard pricing, touches upon many topics in a short time

 

Overall Learning Outcome

In this Pluralsight machine learning module, you will learn to yield engaging experiences for your clients. Broadly there are two learning paths of this course:

 

Path 1 – Machine Learning Literacy

This machine learning Bootcamp is a cluster of five courses, which take you from the introductory to the advanced level.

 

What do you need?

It is a short 15-hour machine learning online course. So, it would help if you dedicated that time towards the course. Further, you must have some basic undergrad-level statistics knowledge and should be familiar with data analytics.

 

What will you learn?

In this machine learning certification course, you will learn:

 

  1. Modeling techniques
  2. Workflows
  3. Strategies behind machine learning solution

 

Path 2- AWS Machine Learning

In this machine learning certification,there are a total of eight courses. Of these, one is the beginner-level course, four are intermediate-level courses, and three are advanced-level courses.

 

What do you need?

This is also a 15-hour learning program. So, you must be willing to put that time towards learning. Further, you must have a background in app development and cloud computing.
 

What will you learn?

  • Amazon Lex
  • Amazon Comprehend
  • AWS Polly
  • Amazon Translate
  • Amazon Transcribe
  • AWS Rekognition
  • Sagemaker
  • Deep Learning on AWS

 

Some courses you can take under this path

  1. Understanding Machine Learning
  2. Understanding Machine Learning with R
  3. Preparing Data for Machine Learning
  4. Understanding Machine Learning with Python
  5. Scalable Machine Learning with the Microsoft Machine Learning Server
  6. Production Machine Learning Systems

 

 

7. Machine Learning Certification Course – Simplilearn

Machine Learning Certification Course
 
Anyone who aspires to understand the impact of machine learning in the digital world will find this course helpful.

 

This is one of the best machine learning courses and worth a shot because In addition to theoretical knowledge you will also work on some real-industry projects giving you more exposure. Some projects that you will work on are:

  1. Fare Prediction for Uber
  2. Test bench time reduction for Mercedes Benz
  3. Income qualification prediction

 

Rating4.5
Duration58 hours
LevelIntroductory
Refund PolicyYes – 7-days moneyback guarantee
Certificate ProvidedYes
Course Material ProvidedYes
Live Classes/Recorded Lessons Both
Course Type Paid
InstructorDifferent for different courses
Scope for Improvement (Cons)Students have only good things to say about the course which is rare. It also makes you intrigued about how authentic the reviews are.

 

Learning Outcome

In this Simplilearn machine learning class, you will acquire the skills necessary to be a machine learning engineer. Studying this course will help you:

 

  1. Develop algorithms via unsupervised and supervised learning
  2. Work with real-time data
  3. Classification
  4. Regression
  5. Time series modeling
  6. Employing Python for making data predictions
  7. Recommender systems
  8. Importing and storing data
  9. Sigmoid probability
  10. Feature engineering

 

Prerequisites

To take this machine learning Bootcamp course, you must be thorough with the college level maths and statistics necessary for data science. It will be an added advantage if you are familiar with Python’s & Statistic’s application in data science.
 

Sharath Chenjeri

My trainer Sonal is amazing and very knowledgeable. The course content is well-planned, comprehensive, and elaborate. Thank you, Simplilearn!

 

 

8. Intro to Machine Learning with PyTorch – Udacity

Intro to Machine Learning with PyTorch
 

This machine learning course by Udacity is provided in collaboration with Aws and Kaggle. It is not just any other course. Instead, it is a nanodegree program.

 

RatingN/A
Duration3 months
LevelIntroductory
Refund PolicyNo
Certificate ProvidedYes
Course Material ProvidedYes
Live Classes/Recorded Lessons Both
Course Type Paid
InstructorMultiple
Scope for Improvement (Cons)The course is expensive, doesn’t have a refund policy

 

Learning Outcome

In this machine learning course by Udacity, you will learn the foundational machine learning techniques. A few things that you will learn are:

  1. Data cleaning
  2. Neural network design and training in PyTorch
  3. Supervised learning
  4. Deep learning
  5. Unsupervised learning

 

Prerequisites

To learn machine learning through this course, you need to have a basic knowledge of statistics and probability, Python programming, knowledge of directories, list and libraries (NumPy and Pandas).

 

Siddharth G.

Its going well so far! Its perfectly matched my needs. I am a beginner in Machine Learning but have experience working with Python so its a perfect blend of material for someone like me

 

 

9. Become a Machine Learning Engineer [Udacity]

Become a Machine Learning Engineer
 
Again, this is not just a course, instead, it is a nanodegree program that will prove helpful on your journey to be a machine learning engineer. Compared to the other courses on our best machine learning courses list, this one is somewhat new. It is provided in collaboration with Aws and Kaggle.

 

Rating4.6
Duration3 months
LevelAdvanced Level
Refund PolicyNo
Certificate ProvidedYes
Course Material ProvidedYes
Live Classes/Recorded LessonsBoth
Course TypePaid
InstructorMultiple
Scope for Improvement (Cons)Long course as compared to its alternatives

 

Learning Outcome

This machine learning course will help you understand some of the most advanced machine learning algorithms and techniques. Other things that this course will teach are:

 

  1. Software engineering fundamentals
  2. Deployment of the sentiment analysis model
  3. Capstone proposal and project to arrive at a reasonable solution
  4. Solving real-world tasks, such as plagiarism detection
  5. Practical experience of employing Amazon SageMaker to deploy trained models to a web application
  6. Evaluation of the models’ performance
  7. Creating A/B test models
  8. Updating the models based on the gathered data

 

Prerequisites

To learn machine learning via this course you need to commit ten hours every week for three months. Further, you must have intermediate-level knowledge of Python & Machine Learning Algorithms.

 

Karol K

It is extremely useful. It could be a little more challenging, since a lot of things you are showing and they don’t require much effort. There could be more challenging task, but with specific instructions of output. In the first processing of `test_review` in the first project was not clear how the shape of it should look like before passing to the model it could be derived either by trail and fail, forum or taking a peek into predict.py, which I guess we are not supposed to do at that moment. Some of the things are also outdated, pandas need version specified in ‘requirements.txt’ since new one is missing some method and even though sagemaker is at version 1.72.0 there are some deprecated lines like `.s3_input` method. The idea of this course is awesome and it would be perfect if I could spend more time on more challenging, well designed tasks instead of fixing some bugs connected to evolving libraries.


 

10. Machine Learning – Offered by Columbia University – [edX]

Machine Learning – Offered by Columbia University
 
In this machine learning course, you will understand the different methods and models to apply machine learning in real-world situations.

 

RatingN/A
Duration12 weeks
LevelAdvanced Level
Refund PolicyNo
Certificate ProvidedOptional
Course Material ProvidedYes
Live Classes/Recorded Lessons Both
Course Type Free with paid upgrade option available
InstructorJohn W. Paisley
Scope for Improvement (Cons)No graded assignments and certificate in free course, no flexible deadline as it is instructor-paced course with strict timelines

 

Topics covered

  1. Classification and regression
  2. Clustering methods
  3. Sequential models
  4. Matrix factorization
  5. Topic modeling
  6. Model selection

 

Methods covered

  1. Linear and logistic regression
  2. Support vector machines,
  3. Tree classifiers
  4. Boosting
  5. Maximum likelihood and MAP inference
  6. EM algorithm
  7. Hidden Markov models
  8. Kalman filters
  9. L-means
  10. Gaussian mixture models,
  11. And others

 

Learning Outcome

An array of things will be taught in this course to learn machine learningSome of them include:

 

  1. Identification of the trending news topics
  2. Ranking sports teams
  3. Building the recommendation engines
  4. Plotting the path of zombies in movies
  5. Supervised versus unsupervised learning
  6. Probabilistic versus non-probabilistic modeling

 

Prerequisites

To take this course and earn a machine learning certification, you must be thorough with:

 

  1. Calculus
  2. Linear algebra
  3. Probability and statistical concepts
  4. Coding and comfort with data manipulation

 

 

11. Machine Learning with Python: A Practical Introduction – Offered by IBM – [edX]

Machine Learning with Python
 
This is a good machine learning coursebecause you will earn yourself a skill badge from IBM once you are through with it.
 

RatingN/A
Duration5 weeks
LevelIntroductory Level
Refund PolicyNo
Certificate ProvidedOptional
Course Material ProvidedYes
Live Classes/Recorded Lessons Both
Course Type Free and Paid
InstructorSaeed Aghabozorgi
Scope for Improvement (Cons)No graded assignments and certificate in free course

 

Learning Outcome

In this machine learning course,you will learn:

 

  1. Basics of machine learning via Python
  2. Supervised vs. unsupervised learning
  3. Comparison between statistical modeling and machine learning
  4. Classification, Regression, Clustering, and Dimensional Reduction
  5. Train/Test Split, Root Mean Squared Error (RMSE), and Random Forests
  6. Real-life examples of machine learning
  7. Using the theoretical knowledge practically

 

Prerequisites

You need to have some understanding of Python for Data Science before enrolling in this machine learning online course.

 

 

Conclusion

So, these are the top 11 best machine learning courses. We have tried to include all the information about the courses in this article. But, for exact course commencement dates please visit the attached course homepage.

 

Best Machine Learning Courses Reviewed by 15 Machine Learning Experts 4.6
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