BC AI
Saved by @vsamk

A lot of Machine Learning (ML) I learned during my Ph.D. was from youtube. I didn't have a guide to do this effectively and thus here it is:

A complete guide to studying ML from youtube: 13 best and most recent ML courses available on YouTube. 👩‍🏫🧵⤵️

 We will start with "Stanford CS229: Machine Learning" by Andrew Ng to start and learn the following ML concepts:

Linear & Logistic Regression,
Naive Bayes, SVMs, Kernels
Decision Trees, Introduction to Neural Networks
Debugging ML Models.
https://t.co/cMLzvsdIcT
A series of mini-lectures (~5 mins) covering various introductory topics in ML by Cassie Kozyrkov, covering:

Explainability in AI, Precession vs. Recall, Statistical Significance, Clustering and K-means, and finally, Ensemble models. https://t.co/LiujYMWFbT
Beyond an AI genius, Andrej Karpathy is a brilliant teacher. His creative teaching methods make this intro to Neural Networks (NN): Zero to Hero makes one of the best ways to get introduced to NN. https://t.co/WaYzmyHYKU
"MIT: Deep Learning for Art, Aesthetics, and Creativity " covers the application of deep learning for art, aesthetics, and creativity, including Neural Abstractions, Efficient GANs, and explorations in AI for Creativity. https://t.co/cANOWM1M2B
(Striking again) Andrew Ng's "Stanford CS230: Deep Learning (2018)" covers:

The foundations of deep learning, how to build different neural networks (CNNs, RNNs, LSTMs, etc...), how to lead machine learning projects and finally - AI and Healthcare. https://t.co/F1jBHejS5k
Applied Machine Learning teaches some of the most widely used techniques in ML, including:

Optimization and Calculus, Overfitting and Underfitting, Regularization, Monte Carlo Estimation, and Maximum Likelihood Learning. https://t.co/2znEMgrJvf
The first part of 'Practical Deep Learning for Coders' teaches you how to:

Build & deploy deep learning models for vision & NLP. Use PyTorch, plus popular libraries like fastai.
https://t.co/xTg00k7wrt
The 2-hour second part of 'Practical Deep Learning for Coders' takes a deep dive into a recent hot ML topic - Diffusion Models. https://t.co/82AHonifNK
ML with graphs teaches some of the latest graph techniques in machine learning:

PageRank, Matrix Factorizing, Node Embeddings, Graph Neural Networks, Knowledge Graphs, and finally, Deep Generative Models for Graphs.
https://t.co/hkgfoFoB9O
This course focuses on the probability and maths behind ML, covering:

Reasoning about uncertainty, Continuous Variables, Sampling, and Markov Chain Monte Carlo. https://t.co/Z76gVxeI3d
This 12-part Deep Unsupervised Learning aims to teach the latest and most widely used techniques in deep unsupervised learning:

Autoregressive Models, Latent Variable Models, & Self-supervised learning.
https://t.co/ywkSKC5r5w
'Foundation Models' is a recent course (June 2022) that aims to teach about foundation models like GPT-3, CLIP, Flamingo, and cross-language generalization. https://t.co/owLqaDXAwj
8 out of 10 ML breakthroughs you recently heard of are likely based on transformers. "Stanford CS25 - Transformers United" aims to introduce us to the following:

Transformers, its applications in Language (GPT-3), vision & universal compute
engines. https://t.co/nkTtSCG854
I will add here as I find more.

I tweet resources for big data research in healthcare. Follow me @Sanjusinha7 if that is of interest. See below for other such resources.
A list of almost all the big data resources available in cancer research. https://t.co/RjGMw2sxD4
28 common issues you will likely face while using ML for biomedicine and how to address them.
https://t.co/1kqZ03zmJd
11 computational resources to study immune system
https://t.co/drV8IQSDJY
12 resources to best analyze Spatial Transcriptomics. https://t.co/3WAb4z8HRL
10 educational resources for anyone interested in building skills to analyze big data in healthcare.
https://t.co/U1NbzVZMlN
20 open grand challenges to understand the relationship btw cancer and aging better.
https://t.co/IUqnLLlmuW
I am developing a drug discovery startup based on a recent computational technology I co-developed (See below).

I would love to chat if you would like to collaborate on this or a potential investor. DM/email [email protected]
https://t.co/ketww3SwSY

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