IN THIS ARTICLE
- 4 Reasons to Study Machine Learning
- Why Learn Machine Learning on Your Own?
- 40 Resources to Learn Machine Learning
- The Best Way to Learn Machine Learning: Never Stop Studying
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When you decide that you want to learn machine learning (ML), the first challenge is simply figuring out where to start. This form of artificial intelligence (AI) technology is a deep and complex field that continues to develop at a rapid pace. This can make it daunting for those who aspire to study machine learning.
Fortunately, we’ve discovered a treasure trove of fantastic resources. From machine learning basics to complex AI techniques, blogs to ebooks, videos to real-life case studies, the internet is rich with opportunities for new students and professionals looking for a refresh.
And the best part?
These machine learning resources are free. So, newcomers can progress and pros can stay sharp all without spending a cent.
It’s time to show you how to learn about machine learning.
4 Reasons to Study Machine Learning
Before diving in, let’s consider just why you should learn machine learning. This isn’t an easy ride, so what makes it worth the effort?
Here are a few good reasons:
ML Is Future-Proof
AI is not just a passing fad—this is the future. The revolutionary technology in these fields is already changing many industries, including healthcare, energy, and marketing.
According to Forbes, by 2022 there will be 58 million new jobs in AI and machine learning. So, if job security is important to you, there’s no safer bet.
The World Is Data-Driven
From one-man startups to Fortune 500 companies, the world can’t get enough data. It has the power to transform a business, letting you learn more about your market, your competitors, and your customers. However, that’s only if you have the right person for the job. The increasing value of a data scientist or machine learning engineer is undeniable now.
Big Data, Big Money
Data from Indeed on average salary rates found that machine learning engineers typically reel in around $145,000 each year. That’s among the highest salaries in tech, and those rates only increase with experience.
Exciting Career Path
While there is much more to machine learning jobs than building futuristic robots and clever programs that will reduce repetitive work, there’s no doubt that exploring the most revolutionary technology on our planet is a pretty cool way to make a living. It’s full of possibilities, so you’re sure to have fun.
Why Learn Machine Learning on Your Own?
Since the technology has gone mainstream, more people have been attracted to machine learning and AI, even transferring from unrelated fields. However, many others are deterred by the idea of expensive and lengthy study routes.
If you opt for the traditional academic path, you could quickly get bogged down by prolonged periods of tackling high-level mathematics theories and textbook learning.
We believe that the best way to learn machine learning is to do it. There are three key advantages to adopting a self-starter attitude:
- You’ll have more fun – With greater freedom to switch between theory and practical work, you’ll enjoy the learning process more. Better yet, you’ll see faster results, which is great for morale and motivation.
- You’ll develop more practical skills – Nowadays, employers care less about your qualifications and more about what you can actually do for them—can you transform data into profits? By learning machine learning your own way, you can focus on the skills the industry wants.
- You’ll build up your portfolio – Through continuous projects, your professional portfolio will grow throughout your studying, which makes you a more attractive prospect for a machine learning internship or entry-level role.
To sum it up, studying machine learning on your own is a quicker, more effective approach—at least to pick up the foundational skills needed to succeed.
Now that you understand that, it’s time to start your journey.
Get To Know Other Data Science Students
40 Resources to Learn Machine Learning
The most logical way to learn machine learning is by starting with the basics, then building up your knowledge, one level at a time. By taking a linear approach, this will help you reinforce new knowledge, and you should see a steady progression in your skills.
With that in mind, we’ve divided our list of free machine learning resources into three core stages:
Without further ado, let’s dive in.
Stage 1 – Learning
In the first stage, you must get to grips with the three fundamental aspects of machine learning:
All of these are directly related to data science, and anyone who wants to enter the field should build a strong foundation in these areas before anything else.
A lot of machine learning engineers use R, but Python is still the best programming language to learn if you want a career in machine learning or AI. You can study with Springboard for free, as our well-structured learning path offers students an introduction to Python in a flexible way that is full of concise yet rigorous hands-on tutorials.
When you begin to study machine learning, you’ll soon realize the importance of KDnuggets. This tutorial is an invaluable resource for delving into data science statistics, as it covers core topics like sampling distribution and the central limit theorem.
If math is not your jam, this may not be the career for you. Math is an intrinsic component of every machine learning job, so you need to get acquainted with complex topics like pre-calculus and matrices. Thankfully, there are plenty of courses that build from basic skills all the way to the tougher stuff. This course is one of the best.
Machine Learning Basics
Probability concepts and conditional probability are vital machine learning basics. If you don’t know these topics, you’ll struggle to understand algorithms. We recommend you take Khan Academy’s free course on statistics and probability so you can build up a sound knowledge of descriptive and inferential statistics.
Understanding statistical error is essential in order to comprehend machine learning algorithms, and also to be able to determine their accuracy. Here’s a visual prompt to help you remember the difference between Type I (false positive) errors and Type II (false negative) errors.
When you want to manipulate large data sets, you’ll need to be good at linear algebra. In this post, you’ll find a list of great resources that explain linear algebra in relation to machine learning concepts and problems.
If you’ve only started to learn machine learning, you may not have encountered Bayesian principles yet. Rest assured, you’ll soon get familiar with them—they are a vital part of understanding machine learning. This online textbook will kickstart your knowledge of Bayesian statistical principles.
You’ll need to know the fundamentals of data pipelines, and how they process streaming data. This is essential to understanding how machine learning works with dynamic data sets. Machine Learning Mastery has created a useful tutorial that explains the basics of data pipelines.
One of the best ways to learn statistical concepts is by actually sitting down to write code and play around with functions. This online textbook on “Probabilistic Programming & Bayesian Methods for Hackers” puts math in the back seat, making programming the priority in an interactive iPython Notebook style that is enjoyable to learn with.
A lot of machine learning revolves around function loss and optimizing for these losses. Understanding the error rate in machine learning systems is only possible if you have a solid knowledge of calculus—specifically multivariate calculus. Check out this simplified rundown to get to grips with essential calculus principles.
When you’re studying math for machine learning, there are several core aspects that really require their own dedicated study program. Matrix factorization is one of those aspects. This is a critical facet of machine learning implementation, so you must work hard to develop your knowledge in this area.
Every algorithm has its own intricate features and functions. You must understand how to use algorithms properly if you’re going to solve any machine learning problems. This extensive exploration of machine learning algorithms is a great primer for machine learning beginners.
Quite often, you’ll find data sets that have not been cleaned. Therefore, it’s not easy to dive in and start working with them right away. It’s a worthwhile venture to learn how to format and collapse data in any format. This article will help you clean and process data sets to use in whatever machine learning project you need it for.
Stage 2 – Practice
After you have solidified your knowledge and basic abilities in machine learning, the next stage is to put your skills to the test.
The internet has a ton of great machine learning resources for learners to practice with, no matter what stage of education you are at.
This collection of insightful case studies from Towards Data Science explores a diverse range of real-world problems, showing learners just how we may encounter machine learning in everyday life. From Netflix to hotel bookings, football predictions to Spotify selections, you can see how machine learning is impacting the world all around us.
Email marketing may seem like Stone Age tech in comparison to machine learning. However, the latter has the power to turbocharge the former, much to the delight of modern marketers. This round-up by eConsultancy explores how machine learning has helped major brands like Dell, Sky, and Harley Davidson boost their bottom line through smarter, data-driven email marketing.
In case you’re thinking AI and machine learning were only for business and profits, here is a list of real-world examples where the technology has been used for social good and sustainability. From tackling climate change and ocean pollution to improving agriculture and healthcare, machine learning and AI have the power to change our world for the better.
Data sets are essential as they allow your algorithms to learn how to perform text classification, mining, and categorization tasks. By accessing data sets and creating projects with them, you can develop a solid understanding of software engineering. When you implement machine learning at scale, you’ll need to be adept at using distributed data tools like Spark and Hadoop. Check out this introduction for frameworks you can use.
All who aspire to a career in machine learning should be familiar with Kaggle. In addition to a wealth of resources, Kaggle offers an unrivaled community of people studying machine learning. Each of the data sets here is the centerpiece of the ongoing discussion. It’s easy to jump in and use these resources to create your own projects, and many other data scientists have uploaded notebooks dedicated to each data set, helping learners with algorithms and specific problems.
Another stronghold of machine learning education is the GitHub community. Here, you’ll find a deep reserve of data sets that have been organized by topics. Looking for something to do with education, economics, or biology? You name it, and there’s a good chance GitHub has it.
The University of California has done the machine learning community a great service with this repository. The School of Information and Computer Science at UCI currently has over 470 data sets, all of which have been classified by the type of problem. Many of these data sets are cleaned, making them easier to use.
That’s right, Amazon has data sets too. You’ll find them in the Amazon Web Services (AWS) resources section. This makes it easy for anyone who is using AWS to experiment with machine learning. Similar to GitHub, the data sets here have been categorized by various niche topics including ecological resources and public transport.
The resources here may not be anywhere near as vast as Kaggle, but Gengo.AI has some good links to offer for specialist interests. This includes a lot on sentiment analysis, natural language processing (NLP), and data sets for autonomous vehicles.
Over time, you’ll probably need to develop skills in C++ and Java. However, for as long as you’re coding in Python, you’ll struggle to find a better tool than scikit-learn. This is among the most popular Python libraries for people studying machine learning, boasting a vast array of algorithms for classification, preprocessing, regression, clustering, and model selection. It’s also user-friendly and full of good tutorials and working examples.
This cloud platform is a great training ground for developers. You can build and train your own AI models here. Microsoft continues to update the platform with new tools and improved features. You can sign up for 12 months free.
IBM’s cloud service is designed for users who want to put their machine learning models into production. You can use the tool to conduct fundamental operations including training and scoring. IBM Watson works best when you are building your machine learning applications through an API connection. The “Lite” version is free.
Google uses TensorFlow for research and production purposes. This open-source library is full of useful software for dataflow projects and programming. This makes it an excellent machine learning framework, especially because of its easy visualization of neural networks.
Stage 3 – Deployment
While we think the best way to learn machine learning (or any discipline) is through a self-starter approach, you don’t need to go it alone all the way. In fact, it’s much better if you actively work with others.
So, once you’ve built your skills and confidence, it’s time to get involved with the wider machine learning community.
By networking with other people who are studying and working in machine learning, you’ll learn from others and may get the chance to collaborate on projects. Over time, you’ll develop a professional network that can present new job opportunities.
This is the biggest subreddit for machine learning. With 647,000 members, you’re sure to find all the help you need, whatever your problem may be. One of the best parts about this group is their active Slack channel that encourages collaboration and brainstorming sessions.
At just over 8,600 members, this group is much smaller. However, it is rich in resources, offering members all sorts of tutorials, projects, tools, papers, and videos about machine learning. This is a very active, engaging community that is great for those who want access to practical AI problems and solutions.
Much of the focus here is on machine learning, but you can find plenty of discussions about related topics like statistics, artificial intelligence, and computer programming.
This group is specifically for beginners, with members sharing knowledge and experience about machine learning basics. You’ll get a lot of engaging discussion about core concepts, as well as useful machine learning resources for beginner projects.
Are you still trying to wrap your head around Python? This group may be your savior. The members are always chatting about Python and R in relation to machine learning and deep learning. Most of the people here are experienced data scientists with a lot of astute knowledge to share with the group.
Ultimately, the time will come when you want to get a machine learning job. You can only learn machine learning for so long before you really want to test your mettle and make some money. Aside from broader job boards and networks like LinkedIn, here are a few places to look for machine learning jobs.
This is one of the best places to look for a machine learning job. It has over 200 new positions listed each month, covering many pathways, including research and development, data science, and machine learning engineering. You can upload your resume and share your profile with top recruiters and companies.
This website is regularly updated with new jobs, many of which are senior positions. You can search by keyword or narrow your scope to specific skills like Python, Java, or data analysis.
ML Conf curates jobs from other sites and focuses solely on machine learning jobs. All jobs are sorted by date, making it easy to find the latest postings.
If you want to get in on the ground floor with an exciting new startup, AngelList is the place to be. New enterprises list job openings here, quite often offering long-term positions with solid pay and sometimes equity in the company.
You don’t have to be truly elite to enter competitions. Yes, you will find some incredible programmers and engineers here, but these competitions are open to everyone. It’s a great way to continue learning and building your network.
Kaggle isn’t just a place to learn machine learning—it’s a battleground for you to pit your wits against others. This platform hosts regular competitions in AI, data science, and machine learning.
This community enables experts to come together to collaborate on machine learning and AI projects. You can join and find open contests. If you win, you’ll gain kudos on the site from your peers, and could also claim a cash prize.
Xander Steenbrugge is a renowned machine learning professional, currently working as a researcher at ML6. He has a fantastic YouTube channel where he covers key points about machine learning and AI. He definitely looks at things from a technical perspective, however, his unique delivery and insights make the topics accessible to a wider audience.
This podcast has been going strong since 2015, building an audience around its discussion on machine learning topics and robotics. The show features regular industry experts, who come on to give their insights and expert opinions on news and trends in the industry, making it one of the best machine learning podcasts for newcomers to learn from.
This podcast aims to pull back the curtain on AI and machine learning, effectively demystifying the field to explain important concepts in an entertaining way that is easy to digest and understand. Things do get technical now and then, but these short 20-30 minute episodes are great for listeners at all levels.
The Best Way to Learn Machine Learning: Never Stop Studying
If you really want to learn machine learning and actually forge a successful career in the field, you must be prepared to pursue the subject for the long term. It’s a fast-moving field and there is no room for complacency.
By taking a self-starter approach, you can progress faster and learn more practical uses and skills than any textbook will show you.
From forums and videos to competitions and collaborative projects, there is no end of great machine learning resources for you to learn from.
It all comes down to your will and passion. With a proactive attitude, you’re sure to find a path forward.