So, you want to become a machine learning master? The good news is that there are countless tutorials and helpful reference guides all over the internet, ripe for perusal. But with the sheer amount of content to sift through, you’re bound to find a few bad apples. Instead of going through every AI video tutorial out there, let us do some of the work for you; take a look at our top 10 machine learning YouTube videos and channels.
While CrashCourse’s YouTube channel is typically associated with lectures on topics like biology and literature, there’s also a regular series on computer science that looks into everything from the history of computing to, yes, artificial intelligence and machine learning. This video on ML helps to demystify a typical ML pipeline and includes a solid overview of some of the more common machine learning algorithms, like support-vector machines. The style of video tutorials that CrashCourse has developed makes for an interesting and easy-to-understand explanation of important and typically complicated concepts.
It’s easy to copy and paste code into your notebooks, but to get to the next level, understanding the underlying intuitions behind some of the most common algorithms you might use in the real world is of absolute importance. Harrison Kinsley of sentdex manages to balance clear explanations of complex concepts with a tutorial rundown of how the code for an algorithm can be created and further tweaked for improvement—all within the span of 10-20 minutes per video. His series on machine learning explores common algorithms, like K-nearest neighbors, in order to develop a strong foundation in building ML algorithms.
If you’re interested in more formal and structured machine learning lectures, CS50 is a popular channel that uploads talks from Harvard University’s “introduction to the intellectual enterprises of computer science and the art of programming.” This lecture on machine learning covers some of the most important base concepts, such as vector space and how it links to machine learning in terms of the analysis and further processing of unstructured data. It even includes an overview of the basics of Python and introduces the main functions and features of this powerful and increasingly common high-level programming language.
Equipped with a comprehensive understanding of machine learning concepts, the next step is to put that learning into practice—by writing code and developing your very own models. Data School provides that kind of guidance, looking into the specifics of how to utilize common industry technologies and tools like Jupyter Notebook and Scikit-learn, among others, to move through the A to Zs of machine learning models. Data School’s other series also act as machine learning tutorials, diving deep into best practices in the industry and answering some of the most frequently asked questions that programmers of all skill levels typically have.
It’s important to go beyond the code and beyond the concepts to understand how your newfound skills and knowledge can and should be applied in the real world. As one of the true giants in the industry, Google’s educational YouTube channels focus on bridging the gap between problems and solutions. Google Cloud’s series on ML and artificial intelligence, in particular, equips viewers with a multitude of case studies, sparking new ideas and building on your foundational knowledge in machine learning and AI.
Neural networks have been researched for years, but only recently has the research been pushed to the next level and commercialized. Xander Steenbrugge of Arxiv Insights’ series on neural networks and how they develop is one of the most thorough on the web. While this video does require previous knowledge in order to understand some of the jargon used and concepts discussed, it manages to make as complicated an idea as neural networks easier to follow. Xander breaks down everything from the different ways that a neural network can be developed to how generalization and overfitting affect neural networks. If you’re looking for a good guide to understanding neural networks, Arxiv Insights is a go-to channel.
Let’s face it. Concepts like deep learning and neural networks can sound a little scary. The worst thing is that explanations of these concepts typically involve even more jargon and complex code, which creates more confusion. DeepLearning.TV’s “Deep Learning SIMPLIFIED” series aims to take away that confusion and replace it with a better understanding of neural networks and deep learning as a whole, explaining the terminology step by step in order to piece together what deep learning is, how it works, and why it’s used in technology today.
Similar to DeepLearning.TV, Machine Learning TV delivers in-depth dives into other common algorithms and ideas, from error and bias to deep learning training tips and natural language processing concepts. The series on deep learning systematically goes through everything you need to know to go one step further from the base concepts to actually build your own deep learning algorithm, highlighting some of the most exciting features of deep learning and its applications in a more comprehensive way. This is one of the best machine learning YouTube options.
Every single day, exciting discoveries are made and the boundaries of machine learning and artificial intelligence are pushed. In fact, the applications and possibilities with ML are boundless, and it’s important to keep up to date with some of the most inspiring and interesting developments around the world. Two Minute Papers uploads content weekly featuring the latest and greatest in technology, from AIs that have learned to “Photoshop” and change human faces to how AI can be applied to fix issues like the broken peer review system plaguing scientific journals.
Rapper, author, data scientist, and AI educator Siraj Raval aims to offer world-class AI education to anyone and everyone for free through his School of AI. Raval’s channel combines interesting video editing, pop culture references, and news on developments in the world of ML and AI to create fascinating and highly informative series that goes beyond the basic concepts. Clear and concise, Raval’s is a fantastic channel for ML enthusiasts looking to stay up to date not just on new developments but on how these concepts apply to the way machine learning models are created in the first place.
For even more AI and machine learning resources, check out these Springboard posts:
- 20 Must-Read AI, Data Science Newsletters
- 40 Free Resources to Help You Learn Machine Learning on Your Own
- What Is Machine Learning Engineering?
- Learn Machine Learning Using These Online Courses
- Our Favorite Books on Artificial Intelligence, Explained
- 11 Must-Watch TED Talks on Data Science
Are you interested in career-focused AI training? Find out more about Springboard’s Machine Learning Engineering Career Track, the first of its kind to come with a job guarantee.