Best machine learning course online and certifications for beginners

Best machine learning course online and certifications for beginners in 2022. Learn Machine Learning this year from these top courses. How to become a data scientist in Nigeria.

If you are aiming to learn Machine Learning in 2022 and looking for the best online courses for Machine learning then you have come to the right place. Best Machine Learning Frameworks(ML) for Experts in 2022

If you are working in the technology field today like you are a programmer or software engineer, then I am sure you have heard about terms like Data Science, Machine Learning, Deep Learning, Artificial Intelligence, etc. Best automatic machine learning frameworks to consider in 2022

Machine Learning is becoming one of the most exciting and fast-paced computer science fields. There’s an endless supply of industries and applications that machine learning can make more efficient and intelligent. Chatbots, spam filtering, ad serving, search engines, and fraud detection are among just a few examples of how machine learning models underpin everyday life.

Below we dig into the best machine learning course online and certifications for beginners 2022

Machine Learning — Coursera

This is the course for which all other machine learning courses are judged. This beginner’s course is taught and created by Andrew Ng, a Stanford professor, co-founder of Google Brain, co-founder of Coursera, and the VP that grew Baidu’s AI team to thousands of scientists.

The course uses the open-source programming language Octave instead of Python or R for the assignments. This might be a deal-breaker for some, but Octave is a simple way to learn the fundamentals of ML if you’re a complete beginner.

Overall, the course material is extremely well-rounded and intuitively articulated by Ng. The math required to understand each algorithm is completely explained, with some calculus explanations and a refresher for Linear Algebra. The course is fairly self-contained, but some knowledge of Linear Algebra beforehand would help.

Provider: Andrew Ng, Stanford
Cost: Free to audit, $79 for Certificate

Machine Learning Course from Coursera structure:

  • 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
  • Dimensionality Reduction
  • Anomaly Detection
  • Recommender Systems
  • Large Scale Machine Learning
  • Application Example: Photo OCR

All of this is covered over eleven weeks. If you can commit to completing the whole course, you’ll have a good base knowledge of machine learning in about four months.

After that, you can comfortably move on to a more advanced or specialized topic, like Deep Learning, ML Engineering, or anything else that piques your interest. This is undoubtedly the best course to start with a newcomer.

Deep Learning Specialization — Coursera

Also taught by Andrew Ng, this specialization is a more advanced course series for anyone interested in learning about neural networks and Deep Learning, and how they solve many problems.

The assignments and lectures in each course utilize the Python programming language and use the TensorFlow library for neural networks. This is naturally an excellent follow-up to Ng’s Machine Learning course since you’ll receive a similar lecture style but now will be exposed to using Python for machine learning.

Provider: Andrew Ng,
Cost: Free to audit, $49/month for Certificate

Deep Learning Specialization from Coursera Courses:

  1. Neural Networks and Deep Learning
  2. Improving Neural Networks: Hyperparameter Tuning, Regularization, and Optimization
  3. Structuring Machine Learning Projects
  4. Convolutional Neural Networks
  5. Sequence Models

To understand the algorithms presented in this course, you should already be familiar with Linear Algebra and machine learning in general. If you need some suggestions for picking up the math required, see the Learning Guide towards the end of this article.

 Machine Learning Crash Course — Google AI

This course comes from Google AI Education, a completely free platform that’s a mix of articles, videos, and interactive content.

The Machine Learning Crash Course covers the topics needed to solve ML problems as soon as possible. Like the previous course, Python is the programming language of choice, and TensorFlow is introduced. Each main section of the curriculum contains an interactive Jupyter notebook hosted on Google Colab.

Video lectures and articles are succinct and straightforward, so you’ll be able to quickly move through the course at your own pace.

Provider: Google AI

Cost: Free

Machine Learning Crash Course from Google Ai Curriculum

  1. Linear and Logistic Regression
  2. Classification
  3. Training and loss
  4. Reducing Loss – gradient descent, learning rates
  5. TensorFlow
  6. Overfitting
  7. Training sets, splitting, and validation
  8. Feature Engineering and cleaning data
  9. Feature Crosses
  10. Regularization – L1 and L2, Lambda
  11. Model performance metrics
  12. Neural Networks – single and multi-class
  13. Embeddings
  14. ML Engineering

Machine Learning with Python — Coursera

Another beginner course, but this one focuses solely on the most fundamental machine learning algorithms. The instructor, slide animations, and explanation of the algorithms combine very nicely to give you an intuitive feel for the basics.

This course uses Python and is somewhat lighter on the mathematics behind the algorithms. With each module, you’ll get a chance to spool up an interactive Jupyter notebook in your browser to work through the new concepts you just learned. Each notebook reinforces your knowledge and gives you concrete instructions for using an algorithm on real data.

Provider: IBM, Cognitive Class
Price: Free to audit, $39/month for Certificate

 Machine Learning with Python from Coursera Course structure:

  • Intro to Machine Learning
  • Regression
  • Classification
  • Clustering
  • Recommender Systems
  • Final Project

One of the best things about this course is the practical advice given for each algorithm. When introduced to a new algorithm, the instructor provides you with how it works, its pros and cons, and what sort of situations you should use it in. These points are often left out of other courses and this information is important for new learners to understand the broader context.

Machine Learning — EdX

This is an advanced course with the highest math prerequisite out of any other course on this list. You’ll need a very firm grasp of Linear Algebra, Calculus, Probability, and programming. The course has interesting programming assignments in either Python or Octave, but the course doesn’t teach either language.

One of the biggest differences with this course is the coverage of the probabilistic approach to machine learning. If you’ve been interested in reading a textbook, like Machine Learning: A Probabilistic Perspective — which is one of the most recommended data science books in Master’s programs — then this course would be a fantastic complement.

Provider: Columbia
Cost: Free to audit, $300 for Certificate

Machine Learning from EdX Course structure:

  • Maximum Likelihood Estimation, Linear Regression, Least Squares
  • Ridge Regression, Bias-Variance, Bayes Rule, Maximum a Posteriori Inference
  • Nearest Neighbor Classification, Bayes Classifiers, Linear Classifiers, Perceptron
  • Logistic Regression, Laplace Approximation, Kernel Methods, Gaussian Processes
  • Maximum Margin, Support Vector Machines (SVM), Trees, Random Forests, Boosting
  • Clustering, K-Means, EM Algorithm, Missing Data
  • Mixtures of Gaussians, Matrix Factorization
  • Non-Negative Matrix Factorization, Latent Factor Models, PCA and Variations
  • Markov Models, Hidden Markov Models
  • Continuous State-space Models, Association Analysis
  • Model Selection, Next Steps

Many of the topics listed are covered in other courses aimed at beginners, but the math isn’t watered down here. If you’ve already learned these techniques, are interested in going deeper into the mathematics behind ML, and want to work on programming assignments that derive some of the algorithms, then give this course a shot.

Can you understand machine learning from online courses.

Yes, Coursera, Edx, Google AI,, Alison, Udacity, Udemy, and DataCamp, among other e-learning sites, provide machine learning courses. These websites offer the best machine learning classes available online.

While mastering machine learning may sound tough, you can gain fundamental or practical knowledge if you invest enough time in online programs.

Furthermore, the amount of time you devote to practicing machine learning determines how quickly you learn it.

Learning Guide

Now that you’ve seen the course recommendations, here’s a quick guide for your learning machine learning journey. First, we’ll touch on the prerequisites for most machine learning courses.

Course Prerequisites

More advanced courses will require the following knowledge before starting:

  • Linear Algebra
  • Probability
  • Calculus
  • Programming

These are the general components of being able to understand how machine learning works under the hood. Many beginner courses usually ask for at least some programming and familiarity with linear algebra basics, such as vectors, matrices, and their notation.

The first course in this list, Machine Learning by Andrew Ng, contains refreshers on most of the math you’ll need, but it might be challenging to learn machine learning and Linear Algebra if you haven’t taken Linear Algebra before at the same time.

If you need to brush up on the math required, check out:

  • Matrix Algebra for Engineers from Coursera to cover Linear Algebra
  • Fat Chance: Probability from the Ground Up from EdX to cover Probability
  • Single Variable Calculus from MIT OpenCourseWare to cover intro Calculus.
  • Programming for Everybody course on Coursera to learn Python programming

I’d recommend learning Python since the majority of good ML courses use Python. If you take Andrew Ng’s Machine Learning course, which uses Octave, you should learn Python either during the course or after since you’ll need it eventually. Additionally, another excellent Python resource is, which has many free Python lessons in their interactive browser environment.

After learning the prerequisite essentials, you can start to really understand how the algorithms work.

Fundamental Algorithms

There’s a base set of algorithms in machine learning that everyone should be familiar with and have experience using. These are:

  • Linear Regression
  • Logistic Regression
  • k-Means Clustering
  • k-Nearest Neighbors
  • Support Vector Machines (SVM)
  • Decision Trees
  • Random Forests
  • Naive Bayes

These are the essentials, but there are many, many more. The courses listed above contain essentially all of these with some variation. Understanding how these techniques work and when to use them will be critical when taking on new projects.

After the basics, some more advanced techniques to learn would be:

  • Ensembles
  • Boosting
  • Dimensionality Reduction
  • Reinforcement Learning
  • Neural Networks and Deep Learning

This is just a start, but these algorithms are what you see in some of the most interesting machine learning solutions, and they’re practical additions to your toolbox.

And just like the basic techniques, with each new tool, you learn you should make it a habit to apply it to a project immediately to solidify your understanding and have something to go back to when in need of a refresher.

Tackle a Project

Learning machine learning online is challenging and extremely rewarding. It’s important to remember that just watching videos and taking quizzes doesn’t mean you’re really learning the material. You’ll learn even more if you have a side project you’re working on that uses different data and has other objectives than the course itself.

As soon as you start learning the basics, you should look for interesting data that you can use while experimenting with your new skills. The courses above will give you some intuition on when to apply certain algorithms, and so it’s a good practice to use them in a project of your own immediately.

Through trial and error, exploration, and feedback, you’ll discover how to experiment with different techniques, how to measure results, and how to classify or make predictions. For some inspiration on what kind of ML project to take on, see this list of examples.

Tackling projects gives you a better high-level understanding of the machine learning landscape. As you get into more advanced concepts, like Deep Learning, there’s virtually an unlimited number of techniques and methods to understand.

Read New Research

Machine learning is a rapidly developing field where new techniques and applications come out daily. Once you’re past the fundamentals, you should be equipped to work through some research papers on a topic that piques your interest.

There are several websites to get notified about new papers matching your criteria. Google Scholar is always a good place to start. Enter keywords like “machine learning” and “Twitter”, or whatever else you’re interested in, and hit the little “Create Alert” link on the left to get emails.

Make it a weekly habit to read those alerts, scan through papers to see if their worth reading, and then commit to understanding what’s going on. If it has to do with a project you’re working on, see if you can apply the techniques to your own problem.

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