Course References#
Awesome list of gold standard curated courses on Machine Learning and related topics, which will help you build conceptual level understanding of modern machine learning.
Machine Learning#
Stanford CS229: Machine Learning
Learn some of the basics of ML:
Linear Regression and Gradient Descent
Logistic Regression
Naive Bayes
SVMs
Kernels
Decision Trees
Introduction to Neural Networks
Debugging ML Models …
Applied Machine Learning
To learn some of the most widely used techniques in ML:
Optimization and Calculus
Overfitting and Underfitting
Regularization
Monte Carlo Estimation
Maximum Likelihood Learning
Nearest Neighbours
…
Introduction to Machine Learning (Tübingen)
The course serves as a basic introduction to machine learning and covers key concepts in regression, classification, optimization, regularization, clustering, and dimensionality reduction.
Linear regression
Logistic regression
Regularization
Boosting
Neural networks
PCA
Clustering
…
Machine Learning Lecture (Stefan Harmeling)
Covers many fundamental ML concepts:
Bayes rule
From logic to probabilities
Distributions
Matrix Differential Calculus
PCA
K-means and EM
Causality
Gaussian Processes
…
Statistical Machine Learning (Tübingen)
The course covers the standard paradigms and algorithms in statistical machine learning.
KNN
Bayesian decision theory
Convex optimization
Linear and ridge regression
Logistic regression
SVM
Random Forests
Boosting
PCA
Clustering
…
Probabilistic Machine Learning
To learn the probabilistic paradigm of ML:
Reasoning about uncertainty
Continuous Variables
Sampling
Markov Chain Monte Carlo
Gaussian Distributions
Graphical Models
Tuning Inference Algorithms
…
Deep Learning#
Neural Networks: Zero to Hero (by Andrej Karpathy)
Course providing an in-depth overview of neural networks.
Backpropagation
Spelled-out intro to Language Modeling
Activation and Gradients
Becoming a Backprop Ninja
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 career advice for deep learning practitioners.
Deep Learning Intuition
Adversarial examples - GANs
Full-cycle of a Deep Learning Project
AI and Healthcare
Deep Learning Strategy
Interpretability of Neural Networks
Career Advice and Reading Research Papers
Deep Reinforcement Learning
🔗 Link to Course 🔗 Link to Materials
Deep Learning: CS 182
To learn some of the widely used techniques in deep learning:
Machine Learning Basics
Error Analysis
Optimization
Backpropagation
Initialization
Batch Normalization
Style transfer
Imitation Learning
…
Deep Unsupervised Learning
To learn the latest and most widely used techniques in deep unsupervised learning:
Autoregressive Models
Flow Models
Latent Variable Models
Self-supervised learning
Implicit Models
Compression
…
Foundation Models
To learn about foundation models like GPT-3, CLIP, Flamingo, Codex, and DINO.
Deep Learning (Tübingen)
This course introduces the practical and theoretical principles of deep neural networks.
Computation graphs
Activation functions and loss functions
Training, regularization and data augmentation
Basic and state-of-the-art deep neural network architectures including convolutional networks and graph neural networks
Deep generative models such as auto-encoders, variational auto-encoders and generative adversarial networks
…
Practical Machine Learning#
LLMOps: Building Real-World Applications With Large Language Models
Learn to build modern software with LLMs using the newest tools and techniques in the field.
Evaluating and Debugging Generative AI
You’ll learn:
Instrument A Jupyter Notebook
Manage Hyperparameters Config
Log Run Metrics
Collect artifacts for dataset and model versioning
Log experiment results
Trace prompts and responses for LLMs
…
ChatGPT Prompt Engineering for Developers
Learn how to use a large language model (LLM) to quickly build new and powerful applications.
LangChain for LLM Application Development
You’ll learn:
Models, Prompt, and Parsers
Memories for LLMs
Chains
Question Answering over Documents
Agents
LangChain: Chat with Your Data
You’ll learn about:
Document Loading
Document Splitting
Vector Stores and Embeddings
Retrieval
Question Answering
Chat
Building Systems with the ChatGPT API
Learn how to automate complex workflows using chain calls to a large language model.
LangChain & Vector Databases in Production
Learn how to use LangChain and Vector DBs in Production:
LLMs and LangChain
Learning how to Prompt
Keeping Knowledge Organized with Indexes
Combining Components Together with Chains
…
Building LLM-Powered Apps
Learn how to build LLM-powered applications using LLM APIs
Unpacking LLM APIs
Building a Baseline LLM Application
Enhancing and Optimizing LLM Applications
…
Full Stack LLM Bootcamp
To learn how to build and deploy LLM-powered applications:
Learn to Spell: Prompt Engineering
LLMOPs
UX for Language User Interfaces
Augmented Language Models
Launch an LLM App in One Hour
LLM Foundations
Project Walkthrough: askFSDL
…
Full Stack Deep Learning
To learn full-stack production deep learning:
ML Projects
Infrastructure and Tooling
Experiment Managing
Troubleshooting DNNs
Data Management
Data Labeling
Monitoring ML Models
Web deployment
…
Practical Deep Learning for Coders
This course covers topics such as how to:
Build and train deep learning models for computer vision, natural language processing, tabular analysis, and collaborative filtering problems
Create random forests and regression models
Deploy models
Use PyTorch, the world’s fastest growing deep learning software, plus popular libraries like fastai and Hugging Face
Foundations and Deep Dive to Diffusion Models
…
Stanford MLSys Seminars
A seminar series on all sorts of topics related to building machine learning systems.
Machine Learning Engineering for Production (MLOps)
Specialization course on MLOPs by Andrew Ng.
Introduction to Deep Learning and Deep Generative Models
Covers the fundamental concepts of deep learning
Single-layer neural networks and gradient descent
Multi-layer neural networks and backpropagation
Convolutional neural networks for images
Recurrent neural networks for text
Autoencoders, variational autoencoders, and generative adversarial networks
Encoder-decoder recurrent neural networks and transformers
PyTorch code examples
Natural Language Processing#
XCS224U: Natural Language Understanding (2023)
This course covers topics such as:
Contextual Word Representations
Information Retrieval
In-context learning
Behavioral Evaluation of NLU models
NLP Methods and Metrics
…
Stanford CS25: Transformers United (2023)
This course consists of lectures focused on Transformers, providing a deep dive and their applications
Introduction to Transformers
Transformers in Language: GPT-3, Codex
Applications in Vision
Transformers in RL & Universal Compute Engines
Scaling transformers
Interpretability with transformers
…
NLP Course (Hugging Face)
Learn about different NLP concepts and how to apply language models and Transformers to NLP:
What is Transfer Learning?
BPE Tokenization
Batching inputs
Fine-tuning models
Text embeddings and semantic search
Model evaluation
…
CS224N: Natural Language Processing with Deep Learning (Stanford)
To learn the latest approaches for deep learning based NLP:
Dependency parsing
Language models and RNNs
Question Answering
Transformers and pretraining
Natural Language Generation
T5 and Large Language Models
Future of NLP
…
Natural Language Understanding*
To learn the latest concepts in natural language understanding:
Grounded Language Understanding
Relation Extraction
Natural Language Inference (NLI)
NLU and Neural Information Extraction
Adversarial testing
…
Advanced NLP
To learn advanced concepts in NLP:
Attention Mechanisms
Transformers
BERT
Question Answering
Model Distillation
Vision + Language
Ethics in NLP
Commonsense Reasoning
…
Graph Machine Learning#
Machine Learning with Graphs (Stanford)
Machine Learning with Graphs (Stanford)
To learn some of the latest graph techniques in machine learning:
PageRank
Matrix Factorizing
Node Embeddings
Graph Neural Networks
Knowledge Graphs
Deep Generative Models for Graphs
…