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.No | Title | Description | Type |
| 1 | CS229: Machine Learning (Stanford) | Machine learning lectures by Andrew NG; In case you are a beginner, these lectures are highly recommended | Machine learning |
| 2 | Applied 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 |
| 3 | Machine 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 |
| 4 | Probabilistic 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 |
| 5 | CS182: 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 |
| 6 | SP21 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, etc | Deep learning |
| 7 | Neural 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, etc | NLP |
| 8 | Deep learning for computer vision | Deep 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 |
| 9 | Full-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 |
| 10 | Multilingual 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 transfer | NLP |
| 11 | Advanced 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 ethics | NLP |
| 12 | Deep reinforcement learning (CS 285, UC Berkeley) | Deep dive into reinforcement learning | Reinforcement learning |
| 13 | Deep unsupervised learning (UC Berkeley) | Concepts on unsupervised deep learning techniques including autoregressive, flow, latent variable, implicit (GAN) and semi-supervised learning models | Deep learning |
I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about 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.
Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.
Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.
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I found it very helpful. However the differences are not too understandable for me