Is Machine Learning Worth Learning?

Artificial Intelligence (AI) searches for patterns in vast amounts of data, and we know this technique by the term ‘machine learning.’ It comes in four types: supervised, unsupervised, reinforcement, and deep. With machine learning algorithms a part of every aspect of life, it has become a hot topic in data science, but is it worth learning?

Machine learning is well worth learning. It is an excellent investment for your future, as thousands of jobs are becoming available in almost every industry. Machine learning is in everything we do, and it isn’t going anywhere any time soon.

In the following article, we will explain why you should learn machine learning, how to learn machine learning and the types of machine learning. We will also share some recommended resources for machine learning, such as courses and books.

Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!

Why Should You Learn Machine Learning?

Machine learning is a big business worldwide, and the salaries for machine learning engineers are well worth it. For example, some engineers earn upwards of $142,000, but there is still plenty of demand for deep learning engineers. There’s never been a better time to get into machine learning.

AI job growth has seen a 29% increase in the last year, indicating that more employment opportunities are opening up in this field.

Many organizations are adding positions due to AI and machine learning, so the rise of automation and artificial intelligence isn’t costing jobs; it’s creating different ones.

Machine learning can be applied to thousands of jobs in almost every industry you can think of. Therefore, if you’re going to put in the time, effort, and money to study, machine learning is an excellent investment.

Another excellent reason to learn machine learning is that most of what you do daily uses it. If you like to understand the technology beneath your fingers, learning about machine learning is one of the best ways to do this.

Here is an excellent video on the basics of machine learning by Simplilearn on YouTube: 

How to Learn Machine Learning?

With two main ways to use machine learning, you’ll want to choose your course or method of learning carefully. For example, if you’re going to go into machine learning research or analysis, you’ll need to study the mathematics and science behind machine learning. However, if you’d only prefer to understand what you need for developing and programming, you probably won’t want to wade through mathematics. 

Learning at least one programming language first, preferably Python, will make learning machine learning much more manageable.

  • Machine learning for developers: If you intend to use machine learning in programming or development, you might do better finding a practical course, such as Practical Machine Learning, or some of’s courses. You can also search for beginner projects and get stuck in on your own. We recommend this option if you find it hard to stick to set courses and learn better by doing something. 
  • Machine learning for researchers and analysts: If you’re looking at going into research or analysis, we’d recommend studying machine learning theory. You’ll need a working understanding of linear algebra and a good head for mathematics, but don’t let this put you off. Most machine learning courses cover everything you need to know.

Best Resources for Learning Machine Learning

With so much to choose from, it can be challenging to find the right resources. However, we’ve come up with this handy list of all the best machine learning resources, so everything you’ll ever need is right here. From online courses to books and even community spaces, there is something for you. 

  • Massive Open Online Courses (MOOCs) like Coursera, Datacamp, edX, Udacity, or The most popular way to learn skills like machine learning is MOOCs. You can often access all course materials for free and only need to pay if you want certification. There are some excellent machine learning courses online, run by the best in the business, such as Andrew Ng.
  • Kaggle and Github. The data science community is a huge fan of sharing and learning together. You’ll find plenty of free resources, projects, and even competitions on sites, like Kaggle and Github. Also, there will always be someone around to help when you get stuck.
  • The Hundred-Page Machine Learning Book by Andriy Burkov from Amazon. The book is praised by industry superheroes like Peter Norvig and is the perfect guide to keep on your desk while you practice.
  • Hands-On Machine Learning with Scikit Learn, Keras, and Tensorflow by Aurélien Géron from Amazon. The book is a detailed, practical guide to machine learning, perfect for getting stuck in. It covers deep learning and includes techniques for using deep neural nets, and it is available in Kindle or print editions.

Don’t forget to upload and share your work to Github or Kaggle, as the data science and machine learning community thrives on and celebrates open-source code. You can also read through, modify, and use other peoples’ open-source code, which is a great way to learn. You may be able to ask that person questions on the forums or even compete against them on Kaggle. 

What Is Machine Learning?

Machine learning is when a computer or AI searches through massive amounts of data, either looking for particular targets or discovering patterns. Machine learning is in everything we do, from Watch Next lists on Netflix to spam filters. It’s also the technology that chooses what you see on social media.

Types of Machine Learning

Machine learning comes in four types. However, any of the first three forms can be considered deep learning. For example, deep supervised learning, deep unsupervised learning, and deep reinforcement learning. You come across all of them every day, but some are more common than others.


In supervised learning, AI searches through labeled data to find what it needs. Supervised machine learning involves the computer searching for a specific target, input by a human being. 

An example of supervised learning is the spam filter in your email account. Spam filters are programmed to funnel out risky emails, and you even give the AI more targets yourself every time you click ‘Mark as spam.’


Unsupervised learning allows the machine to filter through data with no intention other than to see what patterns it can find. 

This type of machine learning is widely used in the marketing industry to determine the demographic of customers shopping for particular items or ranges. Business leaders and marketing experts tailor offers and adverts to people based on the findings of unsupervised machine learning.


Machines using reinforcement learning work through data in much the same way as children solve puzzles — by trial and error. For example, the famous AI that beat human players at Go, AlphaGo, uses reinforcement learning. Self-driving cars are another excellent example of AI using reinforcement learning.


Machine learning is sometimes considered ‘deep learning’ when the computer uses deep neural networks. The technique is inspired by the human brain and how the neural networks function within our gray matter. 

Deep learning uses much more data, creates better algorithms, and gets much more accurate results. All the other types of machine learning can also be used in conjunction with deep learning, for example, deep reinforcement learning.

Author’s Recommendations: Top Data Science Resources To Consider

Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.

  • DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
  • MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
  • Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.


This article explained why machine learning is worth learning and how you can learn it. We shared details of the available resources, such as MOOCs, books, and community spaces like Kaggle and Github. We also looked at what machine learning is and explained the four types of machine learning.

BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.

  1. 5 top careers in artificial intelligence. (2020, November 25). Northeastern University Graduate Programs.
  2. AI careers and salaries: 7 telling statistics. (n.d.). The Enterprisers Project | A community helping CIOs and IT leaders solve problems.
  3. AlphaGo: The story so far. (n.d.). Deepmind.
  4. Andrew Ng. (2009, December 2). Wikipedia, the free encyclopedia. Retrieved December 20, 2020, from
  5. Artificial intelligence. (2001, October 8). Wikipedia, the free encyclopedia. Retrieved December 20, 2020, from
  6. Artificial neural network. (2001, October 2). Wikipedia, the free encyclopedia. Retrieved December 20, 2020, from
  7. Bourke, D. (2019, September 24). 5 beginner-friendly steps to learn machine learning and data science with Python. Medium.
  8. Careers in data science and machine learning. (2019, December 20). College of Information & Computer Sciences.
  9. (n.d.). · Making neural nets uncool again.
  10. Hao, K. (2018, November 17). What is machine learning? MIT Technology Review.
  11. How to learn machine learning. (2017, February 1). EliteDataScience.
  12. How to start a career in artificial intelligence & ML. (n.d.). College of Computing & Informatics.
  13. Peter Norvig. (2004, March 31). Wikipedia, the free encyclopedia. Retrieved December 20, 2020, from
  14. (n.d.).
  15. What is machine learning? (2020, June 26). I School Online – UC Berkeley School of Information.

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Daisy is the founder of Passionate for the field of Data Science, she shares her learnings and experiences in this domain, with the hope to help other Data Science enthusiasts in their path down this incredible discipline.

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