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The 13 Best Machine Learning Online Courses And Certification [Updated 2020]

 

Machine learning is fast becoming an extremely popular field of computer science to work in. In this digital age where computations are becoming lightening fast and we are heading into an era where computers will be doing most of our work, machine learning and artificial intelligence have a huge scope and deserve your attention. Almost everything around us from automated systems, to fraud detection applications, real-time predictions, defence systems are using machine learning. Thus, the need for skilled professionals is only going to rise in the future. Hence the rising demand of machine learning online courses and certification.

 

We, at TrumpLearning, with the help of 10+ machine learning experts, have compiled a list of 13 best machine learning courses which will not only significantly upgrade your skill set but also help you in landing your dream job. These courses will be impressive additions to your resume and would arm you with some serious skills that are quite in demand as of now. Thus without further ado,let’s dive right in!

Best Machine Learning Online Courses And Certifications

  1. Machine Learning [Coursera]
  2. Machine Learning Specialization [Coursera]
  3. Machine Learning [Codecademy]
  4. Grow your Machine Learning Skills [Pluralsight]
  5. Machine Learning Certification Course [SimpliLearn]
  6. Intro to Machine Learning [Udacity]
  7. Become a Machine Learning Engineer [Udacity]
  8. Machine Learning [EdX]
  9. Machine Learning Fundamentals [EdX]
  10. Principles of Machine Learning: Python Edition [EdX]
  11. Machine Learning with Python: A Practical Introduction [EdX]
  12. Mathematics for Machine Learning Specialization [Coursera]
  13. Machine Learning A-Z™: Hands-On Python & R In Data Science [Udemy]

 

 

1. Machine Learning by Stanford University

 

Offered by the super mighty Stanford University, this popular machine learning online course will teach you the most effective machine learning techniques. You will not only be gaining practice by implementing the techniques which are taught but also you would be able to make them work for you. In short, along with the important theoretical aspects, you would be gaining powerful hands-on practical knowledge to apply what you have learnt to the real world problems. The course takes about 56 hours to complete and has subtitles in the Chinese (Simplified), English, Hebrew, Spanish, Hindi, and Japanese languages. Another good aspect about this machine learning course is that the curriculum is neatly divided into 18 modules and take a learner from basics like Linear regression to advanced concepts like Photo OCR.

 

The Stanford machine learning course on Coursera teaches you:

  • The best practices of Silicon Valley in the field of AI & Machine Learning
  • Data mining and statistical pattern recognition
  • Linear regression with one and multiple variables
  • Building smart robots by applying what you have learnt
  • Neural networks
  • Machine learning system design

 

The instructor Andrew Ng is a renowned personality and has some really impressive credentials to his name, some of which include: CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain.

 

Highlight: This machine learning online course is enrolled by 2.7 Million learners and enjoys aggregate rating of 4.9 out of 5. Andrew NG can be considered One of the industry pioneers of Machine Learning.

 

Reviews by student:

Overall the course is great and the instructor is awesome. Machine learning is fascinating and I now feel like I have a good foundation. A few minor comments: some of the projects had too much helper code where the student only needed to fill in a portion of the algorithm. I would have preferred to have worked through more of the code. Also, there were a few times when the slides didn’t contain the complete equations so it was difficult to piece it all together when writing the code. Lastly, I wish that there was more coverage on vectorized solutions for the algorithms.

Robert G C J

 

2. Machine Learning Specialization by University of Washington

 

So if you somehow decided to skip the machine learning online course by Stanford University, then this one being offered by University of Washington should be the second choice that you should definitely consider.

 

The first thing that as a learner you must consider is that it is not simply a machine learning course but a specialization. A specialization takes a learner to great depths (in a field) and makes an expert out of him. So what does this machine learning certificate specialization offer?

 

The specialization takes around 8 months to complete at the suggested pace of 7 hours per week and covers major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval, you will be building intelligent applications and work on complex data sets.

 

The following courses make up this specialization:

 

  • Machine Learning Foundations: A Case Study ApproachSo the first course in this machine learning certification specialization introduces machine learning to learners as black box and focusses on aspects like understanding tasks of interest, matching those tasks to machine learning tools and assessing the quality of the output. This sets the pace just right where learners understand the context rather than straight away diving into the black box. Case Study that will be used is predicting house prices.

 

  • Machine Learning: RegressionSo in the previous course where context was understood, in this (machine learning) course, you will learn the maths behind the model: regression. As a learner you will deep dive in concepts like variance, bias, optimization alogrithms, ways to select the right model etc. At the end, you will be implementing all of this in Python.

 

  • Machine Learning: ClassificationIn this course of machine learning certificate specialization, actual machine learning (as we know it) starts. You will not only build classifiers like predicting sentiments in a product review dataset but also learn non linear models using decision trees. The culmination of all the learning of this course would be in predicting loan frauds.

 

  • Machine Learning: Clustering & RetrievalSo by now you have built classifiers that help you predict desired outcomes from dataset, next step in learning machine learning comes recommendation. And this is what this course teaches you – how to cluster data points and then retrieve pertinent documents based on those clusters.

 

As is visible from the above structure, this machine learning certificate specialization builds the capability from grounds up.

 

Reviews by student:

These courses, from leading institutions all over the world, are only accessible to me through Coursera. I learn something new and fascinating every day.

Dariya K.

 

3. Machine Learning [Codecademy]

 

The machine learning online course covers some foundational machine learning algorithms but requires you to be comfortable with Python 2 including functions, control flow, lists, and loops. Taking almost 20-hours to complete, it is an introductory course to machine learning for complete newbies. Also, if you are someone who is not a newbie, i.e., maybe someone who is trying to analyze a data set or a data analyst looking to upgrade their skills, still this course can do wonders for you.

 

The primary topics covered in this machine learning course on Codecademy are:

  • Linear regression and honey production
  • Multiple linear regression with StreetEasy dataset
  • Yelp Regression Project
  • Regression vs classification
  • K-nearest neighbours algorithm
  • Prediction of Titanic survival through logistic regression

 

Once the Codecademy machine learning course is completed, you would be having completed portfolio projects in your arsenal to showcase your new skills. Further, various quizzes will stress-test your knowledge and help you commit the syntax to memory.

 

Reviews by student:

I know from first-hand experience that you can go in knowing zero, nothing, and just get a grasp on everything as you go and start building right away.

Madelyn

 

4. Grow your Machine Learning Skills [Pluralsight]

 

The course stresses on the development of Artificial Intelligence (AI) and machine learning skills. Through the courses and assessments on Python, TensorFlow, R, Neural Networks, Microsoft Cognitive Services, you would be able to create engaging experiences for all your customers. Pluralsight recommends two machine learning paths:

 

Machine Learning Literacy

A path with five courses from beginner to advanced level that takes around 15-hours to complete. It teaches you about workflows, modeling techniques, and strategies behind any machine learning solution. The prerequisites for choosing this path are data analytics literacy and a background in statistics.

 

AWS Machine Learning/AI

This parth comprises of 8 machine learning online courses that takes around 15-hours to complete. Through one beginner, four intermediate and three advanced level course, it teaches that through a number of AI & Machine Learning AWS products and services working in conjunction with each other, you can build smart applications. Choose this path only if you have some knowledge of cloud computing and application development.

 

The top Pluralsight Machine Learning courses are:

 

Beginner Level Machine Learning Online Courses on Pluralsight

Understanding Machine Learning

Understanding Machine Learning with R

Preparing Data for Machine Learning

Understanding Machine Learning with Python

 

Advanced Level Machine Learning Online Courses on Pluralsight

Scalable Machine Learning with the Microsoft Machine Learning Server

Production Machine Learning Systems

 

Reviews by student:

I just renewed my @pluralsight subscription. I can not live without it

Fran Rodriguez

 

5. Machine Learning Certification Course [SimpliLearn]

 

If you want to explore the core concepts of Machine Learning while also understand how it is changing the digital world around you, then this machine learning certification is just right for you. Imparting the skills you need to become a Machine Learning Engineer, it will make you work on real-time data, developing algorithms using supervised and unsupervised learning, regression, classification, and time series modeling. Further, you would be learning to use Python to draw predictions from data.

 

The key features of this machine learning certification program are:

  • Recommender systems
  • Importing and storing data
  • Data manipulation
  • Supervised learning flow
  • Sigmoid probability
  • Feature engineering
  • Time series modelling

 

The SimpliLearn machine learning course further offers three free courses that are:

  1. Data science with Python
  2. Math refresher
  • Statistics essential for data science

 

Once through with the course  you will have worked on 25-hands on exercises to gain expertise and 4 real life industry projects to your name with integrated labs. Further, you will be able to access dedicated monitoring sessions from the industry experts and 44-hours of instructor led training throughout the duration of the course. The learning will culminate into a certification after an exam on Machine Learning.

 

The high-profile industry projects make it one of the best machine learning certification resources. You would be working on:

  • Fare Prediction for Uber
  • Test bench time reduction for MercedesBenz
  • Income qualification prediction

 

Reviews by student:

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

Sharath Chenjeri

 

6. Intro to Machine Learning [Udacity]

 

From data manipulation to unsupervised and supervised algorithms, this course covers the foundational machine learning techniques. A course that would take you around 3  months to complete at the estimated pace of 10 hours per week, it requires you to have a working know how of the Python language and basic knowledge of probability and statistics as a prerequisite. In collaboration with Kaggle and AWS (Amazon Web Services) this Udacity training is a nanodegree program than just a regular machine learning course.

 

The core concepts taught include:

  • Foundational machine learning algorithms
  • Data cleaning
  • Neural network design and training in PyTorch
  • Supervised learning
  • Deep learning
  • Unsupervised learning

 

By applying your skills to code exercises and projects, you would be gaining some valuable practical experience. Working on practical real-life problems is a far better way to learn machine learning, as compared to mugging up theoretical concepts.

 

7. Become a Machine Learning Engineer [Udacity]

 

This is another nanodegree program available on the Udacity platform to become a machine learning engineer. Relatively new as compared to other machine learning online courses, it will teach you some fairly advanced machine learning techniques and algorithms. Also, you would be learning how to package and deploy your models into a production environment. With an estimated time of around 3 months to complete at the suggested pace of 10 hours per week, it requires knowledge about Python & Machine Learning Algorithms of at least an intermediate level.

 

The primary teachings of this machine learning online course are:

  • Fundamentals of software engineering
  • Deployment of sentiment analysis model
  • Capstone proposal and project to propose a possible solution
  • Solve real world tasks like detection of plagiarism

 

Just like the Udacity nanodegree program above, this too is in collaboration with Kaggle and AWS (Amazon Web Services). You would be gaining some practical experience by using Amazon SageMaker to deploy trained models to a web application. Further, you would be evaluating the performance of your models. You would be creating A/B test models and learn how to update these models based on the data gathered.

 

8. Machine Learning by Columbia University

 

Provided in association with Columbia University this machine learning online course helps you master the essentials of machine learning and algorithms. A part of the MicroMasters® Program – Artificial Intelligence, it shows you two major perspectives namely, probabilistic versus non-probabilistic modeling and supervised versus unsupervised learning. The focus lies on fully developing a mathematical understanding of the algorithms, brief brush with the abstract learning theory and three fundamental problems of unsupervised learning.

 

You will learn the following things via this one of the best machine learning courses on EdX for advanced students:

  • Regression and classification through supervised learning
  • Data modelling and analysis through unsupervised learning
  • Probabilistic vs non-probabilistic viewpoints
  • Optimization and inference algorithms
  • Mixtures of Gaussians, matrix factorization
  • Continuous state-space models

 

You would be able to finish the course in approximately 12 weeks if you are able to devote at least 8-10 hours per week. The EdX resource is a free machine learning course to take unless you want to add a verification certificate to your credentials. This will cost you some money but is worth taking as it will add credibility to your skills. This course is not for beginners and requires you to have an intermediate level knowledge of machine learning, calculus, linear algebra, probability and statistical concepts, along with coding. You should also be comfortable with data manipulation

 

9. Machine Learning Fundamentals on edX

 

Offered in collaboration with UC San Diego, this course is a part of the Data Science MicroMasters® Program which focuses on understanding machine learning’s role in data-driven modeling, prediction, and decision-making. You would be able to classify images, identify salient topics in a corpus of documents, partition people, and categorize documents using real world case studies. Building descriptive and predictive models will feel like a cakewalk as you would be adept at analyzing different types of data. The programming examples mentioned in the course make use of the Python language and Jupyter notebooks.

 

The important concepts covered in this online learning machine learning course include:

  • Conditional probability estimation
  • Generative and discriminative models
  • Kernel methods for extensions to nonlinearity
  • Boosting, bagging, random forests
  • Clustering, dimensionality reduction
  • Autoencoders, deep nets

 

This is an advanced level machine learning course which would require you to complete the previous courses in the MicroMasters program: DSE200x and DSE210x and have undergraduate level education in multivariate calculus and linear algebra. You should take around 10 weeks to complete the course if you put in an effort of around 8-10 hours per week. This course too is free to take but you would be required to pay for the added certification.

 

10.Principles of Machine Learning: Python Edition by Microsoft

 

This EdX course is a part of a professional certificate that makes use of Python and Azure Notebooks to build and derive insights from machine learning models. The teaching methodology in this machine learning online course focuses equally on both theory and practical scenarios while giving you hands-on experience building, validating, and deploying machine learning models. EdX unlike many other platforms out there understands financial restraints and totally encourages you to seek financial assistance. We are providing you with the link mentioned on their website to seek financial assistance.

 

The following core concepts and techniques will be familiar to you once you are done with one of the best machine learning certification courses on EdX:

  • Exploration, preparation and cleansing of data
  • Supervised learning
  • Unsupervised learning
  • Model performance improvement
  • Machine learning algorithms

 

The Artificial Intelligence Program is associated to this Machine Learning program from EdX. The prerequisites of this course require you to basic knowledge of math, and some experience in Python programming. It would take you around 6 weeks to complete this course if you work at the suggested pace of 6-8 hours per week. The course is totally free to take but a certification would cost you some money.

 

11. Machine Learning with Python: A Practical Introduction by IBM

 

The course, as its name suggests focuses on the two extremely important aspects of Machine Learning i.e. supervised and unsupervised learning through Python. Further, you would be able to dive into statistical modelling and understand its relation to machine learning. Apart from exploring multiple algorithms, and various popular models, you would be given an insight into the real-life examples of machine learning. And finally, using the hands on lab, all your theoretical knowledge would be transformed into practical, useful applications.

 

The main teachings of the course to learn machine learning with Python are:

  • Difference between supervised and unsupervised machine learning
  • Statistical Modeling
  • Model evaluation methods
  • Support Vector Machines
  • Density-Based Clustering
  • Content-based recommender systems

 

Offered by IBM, the course is free and would only cost you if you need the certification to verify the fact that you’ve taken the course. The course should not take you more than 5 weeks to complete if you put in an effort of around 4-6 hours per week.

 

12. Mathematics for Machine Learning Specialization [Coursera]

 

This is one of a kind specialization aimed to teach individuals the prerequisite mathematics behind data science and machine learning instead of machine learning itself. Offered by Imperial College of London, this specialization makes you connect the math you studied in school, college or university to the machine learning scenario and bridge the gap of understanding. There are three courses that make up this Coursera specialization. They are:

 

Mathematics for Machine Learning: Linear Algebra

Mathematics for Machine Learning: Multivariate Calculus

Mathematics for Machine Learning: PCA

 

The key concepts taught in this specialized machine learning online course are:

  • Linear Algebra
  • Eigenvalues and Eigenvectors
  • Taylor series and linearisation
  • Multivariate chain rule and its applications
  • Statistics of Datasets
  • Principal Component Analysis

 

This specialization course should take you approximately 2-months to complete at the suggested pace of 13-hours per week. You can take this beginner level course, complete the hands-on project and earn a certificate for the same. Financial aid is also available on Coursera in case you have budget issues.

 

Reviews by student:

The design courses I took on Coursera gave me the tools I needed to propel my career into a new direction.

Sara P.

 

13. Machine Learning A-Z™: Hands-On Python & R In Data Science [Udemy]

 

If you want to be able to create machine learning algorithms through Python and R, then you’d find this to be a highly effective machine learning online course. Just requiring you to know some high school level mathematics, the course has been designed by two professional Data Scientists to help you get a strong hold on to learn complex theory, algorithms and coding libraries. The course is packed with practical real life examples that will aid you in building your own models. A bonus feature of this course are the Python and R code templates that you can use in your projects.

 

The key features of this one of the best machine learning course online on Udemy are:

  • Developing the skills to use machine learning for personal use
  • Advanced concepts like dimensionality reduction
  • Develop, combine machine learning models to solve problems
  • Reinforcement Learning, NLP and Deep Learning
  • Usage of the correct machine learning model as per the problem
  • Support vector regression

Reviews by student:

Amazing Course! Highly recommended if you want to get some hands on experience of Implementing Machine Learning algorithms with proper understanding. The intuition videos provide a good framework to understand the coding tutorials that follow each concept. Overall the course is very well structured covering a large portion of ML universe. Thank you so much for the amazing instructors!

Akshay Dangare

 

The Udemy course is around 41-hours long at the end of which you would be getting a certificate of completion. Moreover, it is backed by a complete 30-day money back guarantee.

 

The TrumpLearning Suggestion To Approach Machine Learning

  1. Read the course prerequisites thoroughly
  2. Brush up the mathematical skills required
  3. Understand your algorithms like the bible
  4. Take the desired course or specialization
  5. Work diligently on the project
  6. Keep up to date with the latest happenings

 

Conclusion

Machine learning is a great field to work in as long as you have the right training and mindset for it. We have given you some incredibly useful resources when it comes to learn machine learning and these would surely arm you with industry level skills. In case you know of more resources or have any kind of queries, suggestions, etc, then please let us know.