Top 5 machine learning courses on Youtube

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

There are many resources from which you can learn right now, paid or free, below is my list of amazing machine learning courses that you can start using right now on Youtube.

1. Applied Machine Learning Lecture videos and materials from the Applied Machine Learning course at Cornell Tech, taught in Fall 2020. Course Materials on Github: https://github.com/kuleshov/cornell-c... Starting from the very basics, we cover all of the most important ML algorithms and how to apply them in practice.

2. Probabilistic Machine Learning
   This playlist collects the lectures on Probabilistic Machine Learning by Philipp Hennig at the University of Tbingen during the Summer Term of 2020. The lectures were recorded for online teaching during the Covid19 pandemic. They are publicly available under the Creative Commons license.

3. NYU Deep Learning SP21 Deep learning from scratch.

4. Advanced NLP Videos for UMass CS685: Advanced Natural Language Processing (Fall 2020)

5. Deep Learning for Computer Vision Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification and object detection. Recent developments in neural network approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of neural-network based deep learning methods for computer vision. During this course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. We will cover learning algorithms, neural network architectures, and practical engineering tricks for training and fine-tuning networks for visual recognition tasks.

There are many options out there for learning topics associated with machine learning, this is just my list and I think that you can use one of these channels to dive into ML, if you have any other suggestions please tell me in the comments.

Thank you all.