Can I Learn Machine Learning For Free?

Alongside the rapid growth of technology over the last ten years, machine learning has seen a significant rise in popularity. It has an active job market and is a skill that touches all industries that regularly depend on machines. This includes fields like healthcare, education, automobile and even finance. Taking courses in machine learning is highly recommended, but they can be costly and are not always in a student’s immediate budget. In those cases, there are plenty of resources that will allow you to learn fundamental skills for free. Certain platforms are dedicated to providing free education and making sure that you can start your journey even if you are not financially prepared to take on the cost of full-time training. Though advanced and professional skills will cost you, with the right information and dedication, you can learn the basics that will empower you to explore this rapidly growing field without spending a dime.

What Free Resources are Available for Learning Machine Learning?

There are several recommended free resources available for learning machine learning. The first is online courses and tutorials. Websites like Coursera, edX, and Khan Academy host free introductory courses on machine learning. These cover very basic or foundational concepts like the history of AI and machine learning, related mathematical concepts, and model evaluation. Even though these courses are free, they are still structured and led by professors or industry professionals, so the information is vetted and high-quality. These platforms include learning tools like quizzes, downloadable resources, and hands-on exercises to reinforce learning. Free online courses like this often lack the feedback and mentorship you may achieve through a paid course, but for those students who thrive in self-directed and paced learning, websites like these are valuable options for getting started. 

There are also more traditional ways of learning, through books and textbooks. Most machine learning textbooks are available online, and with the help of a library card or even just an extra bit of searching, many of them are free. These resources provide high-quality practical knowledge that has been cross-checked against editors, publishers, and other industry professionals for correctness. It is important when using these tools to double-check the date and make sure that the information isn’t too old, especially with how quickly machine learning evolves. Outside of that, textbooks are a great starting point for those who are looking for peer-reviewed materials, or academically significant resources.

Open-source tools and libraries are also highly valuable. These libraries are collections of resources that programmers can use for free. This includes documentation, tutorials, and community support from like-minded individuals. Tools like Tensorflow, Scikit Learn, and Pytorch are great places to start. These resources are vital for those who want to learn quickly, as they provide ready-made templates for things like algorithms and machine learning models. Though this method is not as structured as other options, and finding mentorship or people to guide you through questions is much more work, many of the foundational questions that one might have about the machine learning tools can often be found in the downloadable manuals and documentation. 

How to Make Use of Free Machine Learning Resources

Because a majority of the free machine learning resources do not come with a structured outline, or those that do are enacted by the student instead of an instructor, there is advice for how you can make the most out of the resources available to you. If some form of curriculum is provided, it is worthwhile to follow the recommended learning path. Free resources are geared toward beginner or foundational concepts, so many of them will only cover things like basic algorithms, model evaluation techniques, the history and modern field of machine learning, and other introductory concepts. They typically focus on making sure that you have a solid understanding so that you can confidently move on to intermediate or advanced concepts. These key principles still benefit from a certain order, so if you are able to find guiding documentation that can help you pick which topics to prioritize, it can make the process more official, and it can keep you from misunderstanding key concepts.

When possible, it’s also recommended that you combine different free resources. If you are reading literature about machine learning that doesn’t make sense, perhaps visit a YouTube video, or find a platform that specifically creates multimedia literature so that you can find what best connects with your learning style. This is also a way to double-check that you fully understand what you’re learning, as you can see if tutorials and documents make sense to you in multiple formats, from multiple creators. These free resources are intended to be a bridge to more advanced concepts that can be carried into your professional career, so mastering the basics is critical for making sure the building blocks are solid. Joining communities to get some of the feedback, mentorship, and support that may not come with freebies will help fill a gap in terms of social engagement. You can find machine learning communities in many places, including social media websites like Reddit, Facebook, Instagram, or LinkedIn, or more technology-centered websites like GitHub or Kaggle.

Limitations of Free Resources

One of the more obvious limitations to free resources is the quality of those resources. Though there are platforms that are high quality, it is often the responsibility of the student to research which websites fall under that umbrella, and more, which will have appropriate resources for their skill level and understanding. For websites that are certified, known in the field, or confirmed by the community to be a good source, many of the upper-level resources are behind a paywall. There are fees associated with accessing content that is niche, in-depth, or complex. Building a curriculum does not come easily to everyone, and once you have your resources, there is a lot of research that goes into prioritizing subjects and making sure you’re learning things in an efficient way. With paid products, all of that is already done for you.

Limited support will also be a struggle for some students. Unlike paid programs, there is no explicit community support tied to free resources. It often comes in the form of the student searching out communities, mentors, or peers on their own. It is the student’s responsibility to ensure that the information coming from those communities is valid. Personalized feedback and direct mentorship are critical for addressing individual challenges or concerns. Seeking someone to help cover this area of your learning can be more difficult if the content is free. It is also much harder to use free sources as official documentation in professional pursuits. Comprehensive, in-depth, and professionally-tailored training or instruction will require payment, but freebies can certainly still be a valuable stepping stone on the way to those higher-level resources.

What to Do After Utilizing Free Machine Learning Resources

Once you have tapped into free machine-learning resources, it’s time to consider the advantages of paid, live training through an official institution, like those at Noble Desktop. Paid courses provide a structured curriculum and a more comprehensive set of resources. The topics are not limited to only foundational or key elements, they weave in intermediate and advanced concepts. This helps with professional transitions and can keep you motivated and on track in terms of machine-learning growth. Paid courses will provide updated sources which is important in a rapidly growing field like artificial intelligence where it doesn’t take long for information to become obsolete.

You also have access to industry professionals who can support you through the process. This is important because it ensures that you have expert guidance that is backed up by endorsements in the field. They can give tailored feedback so that you can quickly correct any misunderstandings or misconceptions. Networking with professionals, peers, and other community members positions you for unique job opportunities. There are collaborative projects that are only available through paid courses, and sharing knowledge or gaining a diverse perspective is vital to preparing for real-world machine-learning environments.

Finally, there are exclusive resources that are available only through paid options. These range from data sets to certification. There are workshops, seminars, and other events that can be accessed through local institutions, and offer information on niche topics or new research. If you want a deeper understanding of machine learning or need assistance with machinery or technology required to fully research the subject, paid opportunities will often assist you, especially if they are live and on-campus. A paid course will ensure that you gain practical skills that can be used even outside of the classroom, and will provide you with guided support every step of the way.