Top 10+ Youtube AI / Machine Learning Courses

Online Courses Reskilling

In this post, you get access to top Youtube free AI/machine learning courses. The courses are suitable for data scientists at all levels and cover the following areas of machine learning:

  • Machine learning
  • Deep learning
  • Natural language processing (NLP)
  • Reinforcement learning

Here are the details of the free machine learning / deep learning Youtube courses. 

S.NoTitleDescriptionType
1CS229: Machine Learning (Stanford)Machine learning lectures by Andrew NG; In case you are a beginner, these lectures are highly recommendedMachine learning
2Applied machine learning (Cornell Tech CS 5787)Covers all of the most important ML algorithms and how to apply them in practice. Includes 3 full lectures on how to apply ML in practice in a principled way. This includes topics such as how to prioritize model improvements, diagnose overfitting, perform error analysis, visualize loss curves, etc.Machine learning
3Machine learning with Graphs (Stanford CS224W)Covers concepts related to building machine learning models that leverage the data entities’ relationships and interactions; Covers topics such as applications of graph ML, choice of graph representation, traditional feature-based methods vs Graphs, node embeddings concepts etc.Machine learning
4Probabilistic machine learning (Philipp Hennig at the University of Tübingen 2020) Covers the probabilistic (“Bayesian”) paradigm for machine learning, and occasionally draws direct connections to statistical and deep learning. Machine learning
5CS182: Deep learning (UC Berkeley)Covers deep learning fundamentals including machine learning basics, error analysis, optimization, backpropagation, CNN, RNN, sequence-to-sequence modeling, transformers, NLP, imitation learning, reinforcement learning concepts, etc.Deep learning
6SP21 Deep learning (NYU)Deep learning course by Yann lecun; Covers concepts on deep learning fundamentals, energy models, AE, DE, VAE with PyTorch, GAN, attention & transformers, etcDeep learning
7Neural networks for NLP (CMU)Covers topics such as language modeling, efficiency/training tricks, RNN, attention, conditioned generation, Bias in NLP, multilingual learning, advanced search algorithms, margin-based and reinforcement learning for structured prediction, model interpretation, etcNLP
8Deep learning for computer visionDeep dive into details of neural network-based deep learning methods for computer vision. Covers learning algorithms, neural network architectures, and practical engineering tricks for training and fine-tuning networks for visual recognition tasks.Deep learning
9Full-stack deep learning (UC Berkeley)Covers topics such as deep learning fundamentals, CNN & RNN deep dive, transfer learning and transformers, MLOPs topics such as deploying and monitoring ML models, testing and continuous integration, ML testing, and explainability etc.Deep learning
10Multilingual NLP (CMU – CS11-737)Concepts related to how do we build a multilingual NLP system; Covers topics such as text classification and sequence labeling, advanced text labeling, translation, evaluation, language contact and similarity across languages, multilingual training, and cross-lingual transferNLP
11Advanced NLP (CS 685, University of Massachusetts)Covers different algorithms related to supervised learning tasks (sentiment analysis, question answering, textual entailment, machine translation), semi-supervised learning (language modeling, masked language modeling), transfer learning, text generation, datasets, security, and ethicsNLP
12Deep reinforcement learning (CS 285, UC Berkeley)Deep dive into reinforcement learningReinforcement learning
13Deep unsupervised learning (UC Berkeley)Concepts on unsupervised deep learning techniques including autoregressive, flow, latent variable, implicit (GAN) and semi-supervised learning modelsDeep learning
Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data Science and Machine Learning / Deep Learning. In addition, I am also passionate about various different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia etc and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data etc. I would love to connect with you on Linkedin.
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