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Best YouTube Machine Learning Channels That You Should Subscribe to

Machine learning has revolutionized the world in a very short span of time. Since the data is growing at an exponential rate we need to learn how to process it and gain insights from it. 

Machine learning is a field that combines stats and software development into one profession whose primary goal is to build predictive models.

In order to stay top of the field, we need to constantly learn new things. One of my favorite ways is to learn from smarter people and, if possible, do that for free.

And one of the most effective ways to do so is to subscribe to the best machine learning YouTube channels. It’s a great source of knowledge, the latest trends, and an easy way to develop new skills.

In this article, we will go through the top 14 YouTube channels for you to harness yourself with the knowledge of machine learning.

1. Sentdex

Source: Sentdex

If you are someone who likes to understand everything from scratch then this is by the far the best YouTube channel to learn about Machine Learning.

Harrison Kinsley who is the owner of the YouTube channel Sentdex educates people about various technologies included Python Programming, Web-development, Machine Learning, etc.

If you are keen to learn every algorithm’s workflow like how does bias and intercept get updated at every epoch, or how to implement a given machine learning algorithm from scratch then you must check the following series made by Harrison Kinsley himself.

Channel: Sentdex

2. Data School

Kevin Markham who is the founder of dataschool.io and the owner of the YouTube channel Data School educates machine learning enthusiasts. You can get a comprehensive understanding of machine learning regardless of your educational background thanks to Kevin‘s teaching.

Kevin also makes videos that cover several tools like pandas, NumPy, scikit-learn that will help you build your machine learning models.

You can binge-watch the following series created by Kevin to get a good grasp of the machine learning fundamentals.

Channel: Data School

3. Artificial Intelligence – All in One

Source: Coursera

There are excellent courses available on the channel Artificial Intelligence – All in One which is taught by experts like Andrew Ng, Nitish Srivastava, Geoffery Hinton.

The courses on Artificial Intelligence – All in One covers topics like text mining, text retrieval, and search engines, Neural Networks, and Computer Vision.

You may want to check out the following series to get an excellent grasp over machine learning concepts which is taught by Andrew Ng himself.

Channel: Artificial Intelligence – All in One

4. Deeplearning.ai

“Deep Learning is a superpower. With it, you can make a computer see, synthesize novel art, translate languages, render a medical diagnosis, or build pieces of a car that can drive itself. If that isn’t a superpower, I don’t know what is.” – Andrew Ng

If you want to dive deep into deep learning then you should check out the following series.

Channel: deeplearning.ai

5. Machine Learning with Phil

Phil Tabor is a machine learning engineer who creates educational videos in the domain of machine learning and deep learning.

He has created a great playlist regarding Deep Reinforcement Learning tutorials where he is teaching the core concepts of reinforcement learning like deep deterministic policy gradients in TensorFlow 2, Soft actor-critic in PyTorch, Robotic Control with TD3, and many more.

Channel: Machine Learning with Phil

6. Jeremy Howard

Source: fast.ai

Jeremy Howard is a data scientist who has an educational background in philosophy but later out of the curiosity he harnessed himself with the knowledge of stats and programming to build the most effective and easy-to-use library for deep learning tasks fastai.


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Making deep learning models was never that easy before fast.ai came into the picture. If you are someone who wants to build deep learning models that complete the task in the field of computer vision like image segmentation, image classification, image restoration with the minimal coding, and maximum results then the fast.ai is suitable for you.

You might wanna binge watch the following series to get a good grasp of deep learning with the help of the fast.ai library.

Channel: Jeremy Howard

7.Two Minute Papers

Two Minute Papers is an awesome channel for anyone who loves to be updated with the latest research going on in the Machine Learning domain.

Two Minute Papers make 2 minutes (almost) long videos explaining a research paper.

If you are keen into research field then you may want to check out the following series

Channel: Two Minute Papers

8. Lex Fridman Podcast

Lex Fridman podcast

Lex Fridman Podcast is one of the most popular and best machine learning YouTube channels. Its host is an AI researcher working on autonomous vehicles, human-robot interaction, and machine learning at MIT and beyond.

Lex talks with his guests on everything related to AI and ML. But he’s not limiting himself only to this theme. He talks about other things that can inspire, teach, and push you to exceed your limitations.

Insights from all the superstars, influencers, and leading scientists from the world of machine learning. He interviewed such personas like Elon Musk, Nick Bostrom, Andrew Ng, Yann LeCun, Vladimir Vapnik, Mat,t Botvinick and many, many more.

He also has a second YouTube channel called Lex Clips where he posts clips from his podcast and other videos.

9. Kaggle

Kaggle YouTube channel

Kaggle channel is a spot on YouTube where you can dive into the world of Kaggle community, learn, and do your data science work.

The channel offers videos with interviews with data scientists, lessons, and insightful tips.

This is one of the best machine learning YouTube channels for everyone who wants to learn tricks, experiment, and implement new practices into their own work, no matter what environment you work in.

10. Arxiv Insights

Arxiv Insights

Arxiv Insights is a channel owned by Xander Steenbrugge. He summarizes his core takeaways from a technical point of view while making them accessible for a bigger audience.

If you love technical breakdowns on ML and AI but want a nice summary of the difficult and technical topics, that’s the right place for you!

Although the author doesn’t upload videos often on a regular basis, the channel is praised for its interesting content.

11. Google Cloud Platform

Google Cloud Platform Youtube

It would be a sin not to subscribe to Google Cloud Platform. On the channel, you can get to know such topics as secure infrastructure, developer tools, APIs, data analytics, and machine learning.

This ML YouTube channel lets you learn about how things work at Google, how to become a better data scientist, and all things Google.

Here’s an interesting video about Google Data Center Security: 6 Layers Deep 

12. DeepLearning.TV

DeepLearningTV

DeepLearning.TV is all about Deep Learning. The channel features topics such as How To’s, reviews of software libraries and applications, and interviews with key individuals in the field.

There’s also a series of concept videos showcasing the intuition behind every Deep Learning method so you can better understand how deep learning works.

13. Springboard

Springboard

Springboard channel is all about data science. There are data science and machine learning talks with experts from the leading companies, Women in Data Science playlist with interesting conversations with women who work in ML, deep dives, or mini lessons.

It’s a great machine learning YouTube for those who want to learn how to get a job, what to pay attention to, and find out what it means to work in data science.

14. The TWIML AI Podcast with Sam Charrington

TWIML podcast

If you’re looking for the latest news from the world of machine learning, make sure to check out the TWIML (This Week in Machine Learning) Podcast YouTube channel.

Here, you’ll find each week’s most interesting and important stories from the world of machine learning and artificial intelligence. It’s a great source of information and knowledge for everyone who wants to stay on top of the latest trends, innovations, and get interesting insights from the experts of ML.

Final thoughts

We have listed out the best channels to learn machine learning for free, but you have to be adamant to learn it and you can only learn machine learning by putting your knowledge into practice.

Best of luck on your machine learning journey. 🙂

Python & Machine Learning Instructor | Founder of probog.com

READ NEXT

Where Can You Learn About MLOps? What Are the Best Books, Articles, or Podcasts to Learn MLOps?ML Experiment Tracking: What It Is, Why It Matters, and How to Implement It

4 mins read | Paweł Kijko | Updated May 31st, 2021

MLOps is not a piece of cake. Especially in today’s changing environment. There are many challenges—construction, integrating, testing, releasing, deployment, and infrastructure management. You need to follow good practices and know how to adjust to the challenges.

And if you don’t learn and develop your knowledge, you’ll fall out of the loop. The right resources can help you follow the best practices, discover helpful tips, and learn about the latest trends.

You don’t have to look far, we’ve got you covered! Here’s your list of the best go-to resources about MLOps—books, articles, podcasts, and more. Let’s dive in!

1. Introducing MLOps from O’Reilly

Introducing MLOps: How to Scale Machine Learning in the Enterprise is a book written by Mark Treveil and the Dataiku Team (collective authors). It introduces the key concepts of MLOps, shows how to maintain and improve ML models over time, and tackles the challenges of MLOps.

The book was written specifically for analytics and IT operations team managers—the people directly facing the task of scaling machine learning (ML) in production. It’s a guide for creating a successful MLOps environment, from the organizational to the technical challenges involved.

Introduction to MLOps

The book is divided into three parts:

  1. An introduction to the topic of MLOps, how and why it has developed as a discipline, who needs to be involved to execute MLOps successfully, and what components are required.
  2. The second part follows the machine learning model life cycle, with chapters on developing models, preparing for production, deploying to production, monitoring, and governance.
  3. Provides tangible examples of how MLOps looks in companies today, so readers can understand the setup and implications in practice.
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