Best YouTube Channels to Learn Machine Learning (Free & Beginner-Friendly)
If you’re starting your journey in machine learning and don’t want to spend money on courses just yet — YouTube is one of the best places to learn. Seriously, some of the most helpful content is 100% free.
But with so many videos out there, it can be confusing to know where to start. So I’ve made a simple list of my favorite YouTube channels and playlists, grouped by topic — from learning Python and math to deep learning.
Let’s make this easy❤️
Feel free to check out and subscribe to my YouTube channel for more ML content: MLTUT
1. Learn Programming (Python & R)
Before you start machine learning, you need to know how to code — especially in Python (or R for some data science stuff). These channels are great if you’re new or need a refresher:
▶️ CS DOJO
▶️ Telusko
▶️ R Programming Full Course – Simplilearn
They explain things clearly and are great for learning the basics like loops, functions, and data handling.
2. Understand the Math Behind ML
Machine learning uses a lot of math — but don’t worry! These channels explain it in a simple way. You’ll learn about statistics, linear algebra, and calculus here:
▶️ Statistics for Data Science – Great Learning
▶️ Mathematics for Machine Learning [Full Course] – Edureka
▶️ Mathematics For Machine Learning – Simplilearn
▶️ Mathematics for Machine Learning – My CS
Start with statistics and linear algebra. These videos are beginner-friendly and explain with examples.
3. Learn Machine Learning Algorithms
Now comes the exciting part — learning the actual machine learning algorithms like decision trees, linear regression, and k-means.
▶️ Machine Learning with Python – Great Learning
▶️ Machine Learning Tutorial Python – codebasics
▶️ Python Machine Learning Tutorial – Programming with Mosh
▶️ Machine Learning – Krish Naik
These tutorials teach you how the algorithms work and how to build models with code. Really useful if you want to do hands-on learning.
4. Learn TensorFlow (for Building ML Models)
TensorFlow is a popular tool used to build ML and deep learning models. These YouTube playlists make it easy to start:
▶️ TensorFlow Tutorial – Aladdin Persson
▶️ Coding TensorFlow – Official Channel
They’ll teach you how to use TensorFlow and Keras to build and train models from scratch.
5. Dive into Deep Learning
Once you know the basics, you can move to deep learning — where you build neural networks for images, text, and more.
▶️ Complete Deep Learning – Krish Naik
▶️ Deep Learning With Tensorflow 2.0, Keras and Python – codebasics
▶️ Deep learning Tutorial – Great Learning
These videos walk you through concepts like CNNs, RNNs, backpropagation, and how deep learning is used in real life.
Final Thoughts
Learning machine learning can feel overwhelming at first — but you don’t need to buy expensive courses to begin.
These YouTube videos are:
✅ Beginner-friendly
✅ Easy to follow
✅ Completely free
If I were starting again today, this is exactly where I’d begin. So don’t just watch — try coding along, take small notes, and build something simple to practice.
Got a favorite YouTube channel I missed? Or a question about where to begin? Drop it in the comments — I’d love to chat
Happy learning!
MEL Specialist | Data Analytics- Transforming Data into Actionable Insights! | Driving Impact & Growth | Project Management | Data Science & Strategy | Evidence-Based Decision Making |
1moThanks for sharing, Aqsadn c b itc red. CebbvvvccccV hi I’m c c vvc/@8 M c
Senior Technical Marine engineering at CTN chez CTN - Compagnie Tunisienne de Navigation
1moThanks for sharing, Aqsa. I always appreciate your advice. However, I’m wondering about some points related to the math required for Data Science and Machine Learning Engineering. Most of your posts discuss statistics, probability, linear algebra, and calculus, but they don’t specify the recommended and appropriate level for an engineer. This level could, for example, be defined by the number of study hours needed to achieve the intended goals.other thing I am not convinced that a roadmap of 6 months even with intensive studying hours per day has same value as someone who studied between 3 to 5 years like , Bsc , master degree or for some countries engineer diploma. In my opinion, following these kind of roadmap is a supplement competency for someone who has at least Bsc degree. You as an expert Ds with PhD , maybe you have to be clear and give detailed informations concerning this point. Thanks again for the information and effort you share with us , i am a big fan of you and eager to learn more . Waiting for your Feedback! 👌