How to Learn Machine Learning

Have you ever wondered how Netflix can predict what you want to watch? Have you ever noticed how these predictions change as you watch more videos? Behind the scenes, machine learning algorithms are at play which aim to predict what you may like to watch.

Netflix is not the only company that uses machine learning: this type of data analysis is everywhere in the technology industry.

In this guide, we’re going to talk about how you can learn machine learning. We will start by talking about the skills you need to learn machine learning and why you should learn machine learning. Then, we will talk about the resources you can use to learn about machine learning.

What is Machine Learning?

Machine Learning (ML) is a field within data science devoted to building programs that improve over time. ML algorithms depend on finding patterns within a dataset and using those patterns to improve. The more data that is passed through an algorithm, the more accurate the ML algorithm becomes. Google, Amazon, and Spotify all use machine learning in some way to enhance their services.

Here are a few use cases of machine learning:

  • To provide recommendations to customers on an online shopping site.
  • To recommend what movie or song you may want to listen to next on a streaming platform.
  • To identify spam in emails.
  • To build chatbots capable of interpreting responses from a user.

Machine learning is one of the newer fields in programming so things are constantly changing; expect to see new technologies and techniques to pop up every now and again.

What You Need to Know About Machine Learning

To help you start learning about machine learning, we have compiled a list of essential machine learning topics.

  • Coding Basics. To write machine learning code, you will need to have a good understanding of a language used in machine learning. R and Python are both good choices to start with. Python is an especially good choice because the language is considered easier to learn than many other languages used for machine learning.
  • What is Machine Learning? You should have a good understanding of what machine learning is, why machine learning is used, and where machine learning techniques are applied. This information will give you a solid footing upon which you can build your knowledge.
  • Regression. Regression is the foundation of a lot of machine learning analyses. You should understand linear regression and multiple linear regression and how to use these features in a programming language of your choice.
  • Classification. Classification is commonly used in machine learning algorithms. You should learn how to implement, debug, and improve classification algorithms.
  • Trees. Decision trees are common in machine learning. You should understand the tree data structure, the basics of decision trees, and how you can implement a decision tree.
  • Neutral Networks. You should understand what a neural network is, why they are used, and how to implement one in a programming language.

The above points are a high-level overview of what you need to know. You will also need to learn about logistic regression, clustering, Naive Bayes algorithms, and reinforcement learning. You will likely encounter these topics at some point as you learn because they are featured in so many machine learning courses.

Skills Needed to Learn Machine Learning

To understand machine learning, you need a working knowledge of a programming language, data analysis, and statistics. All of these three fields are key to know because they are applied every day in machine learning work.

You will use programming languages to implement machine learning algorithms. You will use data analysis to prepare data for your algorithms, choose what data to use, and work with that data in your programming language of choice. Statistics come up a lot when you plan how you are going to work with a data set and during the implementation phase of data analysis and machine learning.

Having a mathematical mindset is helpful. Overall, you should feel comfortable working with numbers and have a bit of an intuition when it comes to solving mathematical problems. This knowledge will set you in good stead, especially as you go on to learn more complex machine learning skills.

Why You Should Learn Machine Learning

Although machine learning methods are changing all of the time, you can bet that having an understanding of machine learning will set you up for a stable career in years to come. Companies around the world are constantly looking for talented machine learning technicians. These posts pay well due to the amount of work necessary to become proficient in machine learning.

Knowing about machine learning will also give you the opportunity to work on interesting problems. Machine learning algorithms are not only used in entertainment to predict what songs a person might want to listen to or movies someone might want to watch. Machine learning is used to predict credit card fraud, to detect email spam, and in voice assistants.

How Long Does It Take to Learn Machine Learning?

Machine learning is a complicated role to learn. Expect to spend at least six months learning the basics of machine learning and even longer practicing and refining your skills. You will likely spend about a year refining your skills to the point where you are ready to become a machine learning professional. But your learning journey will never truly be over: there will always be a new challenge for you to tackle or concept to learn.

Learning Machine Learning: A Study Guide

There are hundreds of machine learning resources online, from courses to online tutorials. This means there is no shortage of places to go to learn about machine learning. But because there are so many resources, you may struggle to find a place to start. Therefore, we have created a list of a few machine learning resources to help you get started on your learning journey.

Machine Learning Mastery

  • Resource Type: Website
  • Price: Free
  • Audience: All audiences interested in machine learning

Machine Learning Mastery is a website that covers machine learning. On the site, you will find tutorials on a range of machine learning topics, from building a neural network to installing the tools you need as a machine learning engineer. There are also so-called “quick-start guides” which are written specifically for those with little to no background in machine learning who are just starting their studies.

Machine Learning by Georgia Tech and Udacity

  • Resource Type: Course
  • Price: Free
  • Audience: Intermediate

This machine learning course is part of Georgia Tech’s CS7641 class but can be taken for free online by anyone. The course, which features interactive quizzes and detailed learning content, teaches three pillars of machine learning: supervised, unsupervised, and reinforcement learning. You will find some real-world challenges in this course which will help you build and refine your machine learning skills.

Learn the Basics of Machine Learning by Codecademy

  • Resource Type: Course
  • Price: Codecademy Pro subscription
  • Audience: Beginners to machine learning

This online course produced by Codecademy covers the essentials of machine learning. You will learn the basic machine learning algorithms that will come up in your career such as regression and classification.

Toward the end of the course, you will learn about clustering, neural nets, and more. The course culminates in a module on creating a game-playing artificial intelligence program. Codecademy estimates this course will take 20 hours to complete.

Introduction to Machine Learning for Coders by

  • Resource Type: Course
  • Price: Free
  • Audience: Beginners to machine learning

This course, which was recorded as part of the University of San Francisco M.S. Data Science degree, is a deep-dive into the basics of machine learning. This course uses up-to-date tools such as Pandas and scikit-learn so you will graduate with an understanding of top-tier tools you are likely to use in a career in machine learning.

This course takes what Jeremy Howard, the teacher of the course, refers to as a “code first” approach, meaning that you can expect to work on a lot of practical tasks throughout the course. In total, the course consists of 12 lessons of a two-hour duration each.

Machine Learning by Andrew Ng

  • Resource Type: Course
  • Price: Free
  • Audience: Beginners to machine learning

This course, taught by machine learning expert Andrew Ng, is considered one of the best courses on machine learning available online. The course takes about 60 hours to complete and features lectures on linear regression, linear algebra, logistic regression, and other machine learning topics you need to know.

This course refers to practical examples which will help you build an understanding of machine learning essentials. There are also quizzes and articles you can read to revise your knowledge of the concepts covered in the course lectures.

Communities for People Studying Machine Learning

Where can you go to find people who know about machine learning? Great question. We have compiled a list of some top communities for people studying machine learning that you may want to take a look at.

Cross Validated Stack Exchange Community

The Cross Validated Stack Exchange community features thousands of questions on machine learning. This forum is a great place to go to answer your questions about machine learning, whether you choose to ask your own questions or find answers in existing threads.

On this forum, you will find discussions covering time series, R, cross validation, data visualization, and regression.


The Reddit r/MachineLearning thread is a good place to go to learn about machine learning. You will find threads on projects people are working on and questions about machine learning topics. This community is very active and has over 1.7 million members at the time of writing this article.


No list of machine learning communities would be complete without mentioning Kaggle. While Kaggle is more broadly focused on data science, there are plenty of resources on the Kaggle website for machine learning developers. You will find community threads devoted to machine learning topics and datasets you can use for your machine learning projects.

How Hard is It to Learn Machine Learning?

Becoming knowledgeable in the field of machine learning takes some time. You will need to learn about data analysis, machine learning theory, and coding machine learning projects. There is a lot of hard work ahead for anyone who chooses to learn machine learning. But if you have the right mindset and a real interest in machine learning, there is no reason that you cannot learn what you need to know to become a machine learning developer.

Will Learning Machine Learning Help Me Find a Job?

Having a good understanding of machine learning will have a massively positive impact on your career. Not only are machine learning engineers paid well, such engineers will have a good choice of companies for whom to work because talent is scarce and demand is high.

We have written a list of statistics about machine learning to help you see how learning about machine learning will change your career.

  • Salaries. At the time of writing this guide, Glassdoor reports the average machine learning engineer in the United States earns $114,121 per annum. That is a substantial sum and is much higher than the average salary for many other technical occupations.
  • Job Openings. There are 19,571 jobs open for machine learning engineers on Glassdoor at the time of writing this guide.
  • Industry Growth. The Bureau of Labor statistics reports that jobs in computer and information research science (the category under which machine learning engineers fall) are expected to grow by 15% until 2029. This growth is described as “much faster than average” by the Bureau.

Conclusion: Should You Learn Machine Learning?

A career in machine learning will offer you opportunities to work on challenging problems that involve data. You may end up working to detect fraud for a financial services company. Or you may help a retail company offer a more personalized service to their customers.

Careers in machine learning take time to build. You will spend months — perhaps over a year — training in the skills you need to know and even longer finding an entry-level position. But your efforts will pay off as machine learning engineers are paid commensurately for the complexity of their job.

Ask yourself: are you interested in working with data? Do you see yourself working on a team to plan out algorithms? Do you like mathematics? If you answered “yes” to these three questions, maybe you should consider whether learning about machine learning is right for you.

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