This article will explore reviews and other relevant materials on ML and AI courses. I have ranked them based on my own experience. Also, I included all the useful materials that I remember, links to some websites that I found very helpful.

Overview

Fourth Industrial revolution

The fourth industrial revolution will usher in a completely new business landscape. Promising to change the way we interact with ourselves and the environment This industrial revolution transcends beyond a technology-powered change. It is actually about the birth of new processes, technologies, and business models that are poised to disrupt businesses. Fourth Industrial Revolution (4IR) is comprised of several fields, including:

  • Internet of Things (IoT)
  • Robotics
  • Data Science
  • Machine Learning
  • 3D printing
  • And many other technologies.

One of the key components of the Fourth Industrial Revolution is a large amount of data. Every day, colossal amounts of data are stored and collected. Machine learning and AI algorithm can optimize and automate several analytics processes, which will give rise to revolutionary advances in business.

With the exponential growth of data, accompanied by rapid advances in technology, there is a strong need for us to be data literates. With this in mind, I structured a few development courses associated with the field of data science.

Data Science and Machine learning

Data Science aims to extract as much insight and information from data that can be used to solve complex business processes and aid decision making. With data science, one can uncover unknown patterns and trends in data that were never before imagined. Data science is effective and beneficial when there is a large amount of data that can be analyzed to provide more accurate results.

Machine Learning (ML) is a subset of AI. It is the science of enabling computers to carry out and learn tasks that aren’t explicitly programmed to do. Machine Learning is used to discover patterns in data and make predictions.

How To Start Learning

Machine learning and Artificial Intelligence fields have intrigued me with all their possibilities. The idea of an impending technical revolution is something that I wish to be a part of. Therefore, learning and building my skills in Artificial Intelligence and Machine Learning was essential.

I want to share my feedback on some of the courses I completed recently. I attended some offline classes, but learning online gave me the option to learn whatever I want whenever I want.

Prerequisites

There are a few prerequisites that can help you better understand the concepts. These are areas that you need to learn before continuing:

  • Basic knowledge of calculus
  • Linear algebra
  • Probability and statistics
  • Algorithms and Python are also prerequisites for machine learnin.
Helpful Resources/Suggestions
  • Mathematics for Machine Learning Specialization — Coursera

    • This course is taught by Imperial College London. I must mention that they are cumbersome courses that will require a lot of dedication and effort to complete. But it’s vital that you learn the concepts explained in it before learning ML.

  • StatQuest with Josh Starmer — YouTube

    • This course is excellent for learning ML concepts and statistics. This course breaks down very complex ML and statistics concepts into easy-to-understand bits that are simple to grasp. I bet you will love this course, especially the fun singing part.

  • Python for Everybody — Coursera

    • This course was easy to follow and understand. It is also an excellent beginner course for learning Python and would teach most of what you need to know before you start taking the ML courses.

  • Jupyter Notebook for Beginners Tutorials by Dataquest

    • I didn’t take this course. But since it’s highly recommended, you should give it a try.

Once your Python skills are good, you’ll want to get comfortable working with and manipulating data. NumPy, pandas, and Matplotlib are some helpful resources to help you get started.

Learn Machine Learning Concepts

  • Data Analyst Nanodegree — Udacity

    • So many people have recommended this course, and fortunately, Udacity recently partnered with the Saudi MSK academy. Thus, I registered for this Nanodegree. If you prefer practicals to theories, then you should take more courses on Udacity. Their courses are very practical-oriented. But I found this course to be easier than the others. If you are a young developer, this is an excellent course, to begin with.

  • CS221: Artificial Intelligence: Techniques and Principles — Stanford

    • I have no complaints about this course; the assignments and practicals were perfect. We had interesting weekly coding assignments.

  • CS229: Machine Learning — Stanford

    • I enrolled in this course and CS221 last summer at Stanford. I have shared my experience The courses are also available online.

    This course was great. But if you aren’t very good at linear algebra, I advise that you brush up on it before you take this course. Also, don’t take this course as your first introductory course to ML.

    You may find this cheat sheet helpful. It was designed by Shervine Amidi, a Stanford Uni graduate.

  • Machine Learning — Coursera

    • Taught by Andrew Ng, this course is unarguably the most popular machine learning course right now. The course is very theoretical, so you would appreciate it more if you take some practical courses before you register for it.

  • Data science immersive — General assembly

    • General assembly recently partnered with the Saudi MSK academy. Thus, I registered for this course. This course is an offline course but due to the pandemic, this course had to be taken online. However, it was still a very good one. Passing this course depending on whether you complete the assignments and the final project. the good thing is that this course had a significant amount of coding required.

  • Deep learning specialization — Coursera

    • This specialization is amazing, and I advise you to take this course if you have any interest in AI. Professor Andrew NG explains the value of AI and the improvements it can bring. This course is simple, easy to learn, and well defined. If you need a great foundation for your AI career, then start with this course.

  • Data Science Ethics — Coursera

    • This course is offered by the University of Michigan, and it explains some of the ethical and legal frameworks surrounding data science. It is good to take it to understand the ethics when using the data

    Wrapping up!
    Machine Learning is a very practical field of study, so the best way to learn is by doing. You can find a lot of projects in Kaggle

    Conclusion!

    Data Science will continue to cause digital disruptions as it continues to be used in various technological and business processes. It is no longer a question of if, but when. If you don’t adapt quickly, you risk getting left behind.

    Businesses must start now to plan for the future and focus on how they can leverage the Fourth Industrial Revolution to improve the value that their business can provide to customers.

    Good luck!