Machine Learning Studies and Adjencent Technologies

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Python Programming

Resource	Progress
Datacamp: Writing Efficient Python Code	✅
Datacamp: Writing Functions in Python	✅
Datacamp: Object-Oriented Programming in Python	✅
Datacamp: Intermediate Object-Oriented Programming in Python	✅
Datacamp: Importing Data in Python (Part 1)	✅
Datacamp: Importing Data in Python (Part 2)	⬜
Datacamp: Intermediate Python for Data Science	⬜
Datacamp: Python Data Science Toolbox (Part 1)	⬜
Datacamp: Python Data Science Toolbox (Part 2)	⬜
Datacamp: Developing Python Packages	⬜
Datacamp: Conda Essentials	⬜
Youtube: Tutorial: Sebastian Witowski - Modern Python Developer's Toolkit	⬜
Datacamp: Working with Dates and Times in Python	⬜
Datacamp: Command Line Automation in Python	⬜
Book: Python 201	⬜
Book: Writing Idiomatic Python 3	⬜
Article: Python's many command-line utilities	⬜
Article: A Programmer’s Introduction to Unicode	⬜

Testing & Profiling

Resource	Progress
Datacamp: Unit Testing for Data Science in Python	⬜
Book: Test Driven Development with Python	⬜
Article: Introduction to Memory Profiling in Python	⬜
Article: Profiling Python code with memory_profiler	⬜
Article: How to Use "memory_profiler" to Profile Memory Usage by Python Code?	⬜

Data Structures and Algorithms

Resource	Progress
Book: Grokking Algorithms	⬜
Book: The Tech Resume Inside Out	⬜
Neetcode: Algorithms and Data Structures for Beginners	⬜
Udacity: Intro to Data Structures and Algorithms	⬜

Linux & Command Line

Resource	Progress
Datacamp: Introduction to Shell for Data Science	⬜
Datacamp: Introduction to Bash Scripting	⬜
Datacamp: Data Processing in Shell	⬜
MIT: The Missing Semester	⬜
Udacity: Linux Command Line Basics	⬜
Udacity: Shell Workshop	⬜
Udacity: Configuring Linux Web Servers	⬜

Version Control

Resource	Progress
Udacity: Version Control with Git	⬜
Datacamp: Introduction to Git for Data Science	⬜
Udacity: GitHub & Collaboration	⬜
Udacity: How to Use Git and GitHub	⬜

Databases

Resource	Progress
Datacamp: Database Design	⬜
Datacamp: Intermediate SQL	⬜
Book: High Performance MySQL	🔄
Udemy: MySQL High Performance Tuning Guide	⬜
Udacity: Intro to relational database	⬜
Udacity: Database Systems Concepts & Design	⬜
Datacamp: Introduction to Databases in Python	⬜
Datacamp: Intro to SQL for Data Science	⬜
Datacamp: Joining Data in PostgreSQL	⬜
Udacity: SQL for Data Analysis	⬜
Datacamp: Exploratory Data Analysis in SQL	⬜
Datacamp: Applying SQL to Real-World Problems	⬜
Datacamp: Analyzing Business Data in SQL	⬜
Datacamp: Reporting in SQL	⬜
Datacamp: Data-Driven Decision Making in SQL	⬜
Datacamp: NoSQL Concepts	⬜
Datacamp: Introduction to MongoDB in Python	⬜

Backend Engineering

Resource	Progress
Udacity: Authentication & Authorization: OAuth	⬜
Udacity: HTTP & Web Servers	⬜
Udacity: Client-Server Communication	⬜
Udacity: Designing RESTful APIs	⬜
Datacamp: Introduction to APIs in Python	⬜
Udacity: Networking for Web Developers	⬜

Production System Design

Resource	Progress
Book: Designing Machine Learning Systems	⬜
Neetcode: System Design for Beginners	⬜
Neetcode: System Design Interview	⬜
Datacamp: Customer Analytics & A/B Testing in Python	⬜
Datacamp: A/B Testing in Python	⬜
Udacity: A/B Testing	⬜
Datacamp: MLOps Concepts	⬜
Datacamp: Machine Learning Monitoring Concepts	⬜

Maths

Resource	Progress
Datacamp: Foundations of Probability in Python	⬜
Datacamp: Introduction to Statistics	⬜
Datacamp: Introduction to Statistics in Python	⬜
Datacamp: Hypothesis Testing in Python	⬜
Datacamp: Statistical Thinking in Python (Part 1)	⬜
Datacamp: Statistical Thinking in Python (Part 2)	⬜
Datacamp: Experimental Design in Python	⬜
Datacamp: Practicing Statistics Interview Questions in Python	⬜
edX: Essential Statistics for Data Analysis using Excel	⬜
Udacity: Intro to Inferential Statistics	⬜
MIT 18.06 Linear Algebra, Spring 2005	⬜
Udacity: Eigenvectors and Eigenvalues	⬜
Udacity: Linear Algebra Refresher	⬜
Youtube: Essence of linear algebra	⬜

Traditional Machine Learning

Resource	Progress
Book: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition	⬜
Book: A Machine Learning Primer	⬜
Book: Grokking Machine Learning	⬜
Book: The StatQuest Illustrated Guide To Machine Learning	⬜
Datacamp: Ensemble Methods in Python	⬜
Datacamp: Extreme Gradient Boosting with XGBoost	⬜
Datacamp: Clustering Methods with SciPy	⬜
Datacamp: Unsupervised Learning in Python	⬜
Udacity: Segmentation and Clustering	⬜
Datacamp: Intro to Python for Data Science	⬜
edX: Implementing Predictive Analytics with Spark in Azure HDInsight	⬜
Datacamp: Supervised Learning with scikit-learn	⬜
Datacamp: Machine Learning with Tree-Based Models in Python	⬜
Datacamp: Linear Classifiers in Python	⬜
Datacamp: Model Validation in Python	⬜
Datacamp: Hyperparameter Tuning in Python	⬜
Datacamp: HR Analytics in Python: Predicting Employee Churn	⬜
Datacamp: Predicting Customer Churn in Python	⬜
Datacamp: Dimensionality Reduction in Python	⬜
Datacamp: Preprocessing for Machine Learning in Python	⬜
Datacamp: Data Types for Data Science	⬜
Datacamp: Cleaning Data in Python	⬜
Datacamp: Feature Engineering for Machine Learning in Python	⬜
Datacamp: Predicting CTR with Machine Learning in Python	⬜
Datacamp: Intro to Financial Concepts using Python	⬜
Datacamp: Fraud Detection in Python	⬜

Deep Learning

Resource	Progress
Article: An overview of gradient descent optimization algorithms	⬜
Book: Make Your Own Neural Network	⬜
Fast.ai: Practical Deep Learning for Coder (Part 1)	⬜
Fast.ai: Practical Deep Learning for Coder (Part 2)	⬜
Datacamp: Convolutional Neural Networks for Image Processing	⬜
Karpathy: Neural Networks: Zero to Hero	⬜
Article: Weight Initialization in Neural Networks: A Journey From the Basics to Kaiming	⬜

Natural Language Processing

Resource	Progress
Book: Natural Language Processing with Transformers	⬜
Stanford CS224U: Natural Language Understanding \ Spring 2019	⬜
Stanford CS224N: Stanford CS224N: NLP with Deep Learning \ Winter 2019	⬜
CMU: Low-resource NLP Bootcamp 2020	⬜
CMU Multilingual NLP 2020	⬜
Datacamp: Feature Engineering for NLP in Python	⬜
Datacamp: Natural Language Processing Fundamentals in Python	⬜
Datacamp: Regular Expressions in Python	⬜
Datacamp: RNN for Language Modeling	⬜
Datacamp: Natural Language Generation in Python	⬜
Datacamp: Building Chatbots in Python	⬜
Datacamp: Sentiment Analysis in Python	⬜
Datacamp: Machine Translation in Python	⬜
Article: The Unreasonable Effectiveness of Collocations	⬜
Article: FuzzyWuzzy: Fuzzy String Matching in Python	⬜
Article: Mamba Explained	⬜
Article: A Visual Guide to Mamba and State Space Models	⬜
Article: Transformers: Origins	⬜

LLM Theory

Resource	Progress
Book: Hands-On Large Language Models: Language Understanding and Generation	⬜
Book: AI Engineering: Building Applications with Foundation Models	⬜
Book: Designing Large Language Model Applications	⬜
Book: Large Language Models: A Deep Dive: Bridging Theory and Practice	⬜
Book: A Little Bit of Reinforcement Learning from Human Feedback	⬜
Article: From Digits to Decisions: How Tokenization Impacts Arithmetic in LLMs	⬜
Article: SolidGoldMagikarp (plus, prompt generation)	⬜
Article: Sampling for Text Generation	⬜
Article: You could have designed state of the art Positional Encodin	⬜
Article: Scaling test-time compute - a Hugging Face Space by HuggingFaceH4	⬜
Article: DeepSeek R1's recipe to replicate o1 and the future of reasoning LMs	⬜
Article: The Illustrated DeepSeek-R1	⬜
DeepLearning.AI: Pretraining LLMs	⬜
DeepLearning.AI: Reinforcement Learning from Human Feedback	⬜
Karpathy: Intro to Large Language Models	⬜
Karpathy: Let's build the GPT Tokenizer	⬜
Karpathy: Let's reproduce GPT-2 (124M)	⬜
Youtube: A Hackers' Guide to Language Models	⬜
Youtube: 5 Years of GPTs with Finbarr Timbers	⬜
Youtube: Stanford CS229 I Machine Learning I Building Large Language Models (LLMs)	⬜
Youtube: LLaMA explained: KV-Cache, Rotary Positional Embedding, RMS Norm, Grouped Query Attention, SwiGLU	⬜
Youtube: CMU Advanced NLP Fall 2024 (7): Prompting and Complex Reasoning	⬜
Youtube: CMU Advanced NLP Fall 2024 (6): Instruction Tuning	⬜
Youtube: CMU Advanced NLP Fall 2024 (12): Domain Specific Modeling: Code and Math	⬜
Youtube: CMU Advanced NLP Fall 2024 (15): Tool Use and LLM Agent Basics	⬜
Youtube: CMU Advanced NLP Fall 2024 (14): Ensembling and Mixture of Experts	⬜
Youtube: A little guide to building Large Language Models in 2024	⬜
Youtube: How to approach post-training for AI applications	⬜
Youtube: Speculations on Test-Time Scaling (o1) 47m	⬜
Youtube: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning	⬜

Multi-modality

Resource	Progress
Article: Understanding Multimodal LLMs	⬜
Article: GPT-4 Vision Alternatives	⬜
Article: Computer-Using Agent	⬜
Youtube: AI Visions Live \ Merve Noyan \ Open-source Multimodality	⬜
DeepLearning.AI: How Diffusion Models Work	⬜
DeepLearning.AI: Prompt Engineering for Vision Models	⬜
DeepLearning.AI: Building Multimodal Search and RAG	⬜
Pinecone: Embedding Methods for Image Search	0/8

Information Retrieval / RAG

Resource	Progress
Article: Pretrained Transformer Language Models for Search - part 1	⬜
Article: Pretrained Transformer Language Models for Search - part 2	⬜
Article: Pretrained Transformer Language Models for Search - part 3	⬜
Article: Pretrained Transformer Language Models for Search - part 4	⬜
Article: How not to use BERT for Document Ranking	⬜
Article: Understanding LanceDB's IVF-PQ index	⬜
Article: A little pooling goes a long way for multi-vector representations	⬜
Article: Levels of Complexity: RAG Applications	⬜
Article: Systematically Improving Your RAG	⬜
Article: Stop using LGTM@Few as a metric (Better RAG)	⬜
Article: Low-Hanging Fruit for RAG Search	⬜
Article: What AI Engineers Should Know about Search	⬜
Article: Evaluating Chunking Strategies for Retrieval	⬜
Article: Sentence Embeddings. Introduction to Sentence Embeddings	⬜
Article: LambdaMART in Depth	⬜
Article: Guided Generation with Outlines	⬜
Article: RAG tricks from the trenches	⬜
Course: Fullstack Retrieval	⬜
DeepLearning.AI: Building and Evaluating Advanced RAG Applications	⬜
DeepLearning.AI: Vector Databases: from Embeddings to Applications	⬜
DeepLearning.AI: Advanced Retrieval for AI with Chroma	⬜
DeepLearning.AI: Prompt Compression and Query Optimization	⬜
DeepLearning.AI: Large Language Models with Semantic Search	⬜
DeepLearning.AI: Building Applications with Vector Databases	⬜
DeepLearning.AI: Knowledge Graphs for RAG	⬜
DeepLearning.AI: Preprocessing Unstructured Data for LLM Applications	⬜
DeepLearning.AI: Embedding Models: From Architecture to Implementation	⬜
DeepLearning.AI: Retrieval Optimization - From Tokenization to Vector Quantization	⬜
Pinecone: Vector Databases in Production for Busy Engineers	⬜
Pinecone: Retrieval Augmented Generation	⬜
Pinecone: Faiss: The Missing Manual	⬜
Pinecone: Natural Language Processing for Semantic Search	⬜
Youtube: Systematically improving RAG applications	⬜
Youtube: Back to Basics for RAG w/ Jo Bergum	⬜
Youtube: Beyond the Basics of Retrieval for Augmenting Generation (w/ Ben Clavié)	⬜
Youtube: RAG From Scratch	⬜
Youtube: CMU Advanced NLP Fall 2024 (10): Retrieval and RAG	⬜
Guidance: Token Healing	⬜

Agentic Pattern

Resource	Progress
Article: Tool Invocation - Demonstrating the Marvel of GPT's Flexibility	⬜
Anthropic: Building effective agents	⬜
Anthropic: Building Effective Agents Cookbook	⬜
OpenAI: Assistants & Agents Build Hour	⬜
OpenAI: Function Calling Build Hour	⬜
DeepLearning.AI: Functions, Tools and Agents with LangChain	⬜
DeepLearning.AI: Building Agentic RAG with LlamaIndex	⬜
DeepLearning.AI: Multi AI Agent Systems with crewAI	⬜
DeepLearning.AI: Building Towards Computer Use with Anthropic	⬜
DeepLearning.AI: Practical Multi AI Agents and Advanced Use Cases with crewAI	⬜
DeepLearning.AI: LLMs as Operating Systems: Agent Memory	⬜
DeepLearning.AI: Serverless Agentic Workflows with Amazon Bedrock	⬜
DeepLearning.AI: AI Agentic Design Patterns with AutoGen	⬜
DeepLearning.AI: AI Agents in LangGraph	⬜
DeepLearning.AI: Building Your Own Database Agent	⬜
DeepLearning.AI: Function-Calling and Data Extraction with LLMs	⬜

Prompt Engineering

Resource	Progress
Article: OpenAI Prompt Engineering	⬜
Article: Prompting Fundamentals and How to Apply them Effectively	⬜
Article: How I came in first on ARC-AGI-Pub using Sonnet 3.5 with Evolutionary Test-time Compute	⬜
Anthropic Courses	⬜
Anthropic: The Claude in Amazon Bedrock Course	⬜
Article: Prompt Engineering(Liliang Weng)	⬜
Article: Prompt Engineering 201: Advanced methods and toolkits	⬜
Article: Optimizing LLMs for accuracy	⬜
Article: Primers • Prompt Engineering	⬜
Article: Anyscale Endpoints: JSON Mode and Function calling Features	⬜
Article: Guided text generation with Large Language Models	⬜
Book: Prompt Engineering for LLMs	⬜
DeepLearning.AI: Reasoning with o1	⬜
OpenAI: Reasoning with o1 Build Hour	⬜
DeepLearning.AI: ChatGPT Prompt Engineering for Developers	⬜
DeepLearning.AI: Prompt Engineering with Llama 2 & 3	⬜
Wandb: LLM Engineering: Structured Outputs	⬜
Series: Prompt injection	⬜
Youtube: Prompt Engineering Overview	⬜
Youtube: Prompt Engineering Workshop	⬜

Quantization

Resource	Progress
Article: Quantization Fundamentals with Hugging Face	⬜
DeepLearning.AI: Quantization in Depth	⬜
DeepLearning.AI: Introduction to On-Device AI	⬜
Article: A Visual Guide to Quantization	⬜
Article: QLoRA and 4-bit Quantization	⬜
Article: Understanding AI/LLM Quantisation Through Interactive Visualisations	⬜
Youtube: CMU Advanced NLP Fall 2024 (11): Distillation, Quantization, and Pruning	⬜

Inference Optimization

Resource	Progress
Article: How to make LLMs go fast	⬜
Article: In the Fast Lane! Speculative Decoding - 10x Larger Model, No Extra Cost	⬜
Article: Accelerating Generative AI with PyTorch II: GPT, Fast	⬜
Article: Harmonizing Multi-GPUs: Efficient Scaling of LLM Inference	⬜
Article: Multi-Query Attention is All You Need	⬜
Article: Transformers Inference Optimization Toolset	⬜
DeepLearning.AI: Efficiently Serving LLMs	⬜
Article: LLM Inference Series: 3. KV caching explained	⬜
Article: LLM Inference Series: 4. KV caching, a deeper look	⬜
Article: LLM Inference Series: 5. Dissecting model performance	⬜
Article: Transformer Inference Arithmetic	⬜
Article: Optimizing AI Inference at Character.AI	⬜
Article: Optimizing AI Inference at Character.AI (Part Deux)	⬜
Youtube: SBTB 2023: Charles Frye, Parallel Processors: Past & Future Connections Between LLMs and OS Kernels	⬜
Youtube: Deploying Fine-Tuned Models	⬜

Evals and Guardrails

Resource	Progress
Article: Your AI Product Needs Evals	⬜
Article: Task-Specific LLM Evals that Do & Don't Work	⬜
Article: Evaluation & Hallucination Detection for Abstractive Summaries	⬜
Article: LLM-as-a-Judge vs Human Evaluation	⬜
DeepLearning.AI: Automated Testing for LLMOps	⬜
DeepLearning.AI: Red Teaming LLM Applications	⬜
DeepLearning.AI: Evaluating and Debugging Generative AI Models Using Weights and Biases	⬜
DeepLearning.AI: Quality and Safety for LLM Applications	⬜
OpenAI: Evals Build Hour	⬜
Youtube: Instrumenting & Evaluating LLMs	⬜
Youtube: LLM Eval For Text2SQL	⬜
Youtube: A Deep Dive on LLM Evaluation	⬜

Finetuning and Distillation

Resource	Progress
Article: Tokenization Gotchas	⬜
Article: Practical Tips for Finetuning LLMs Using LoRA (Low-Rank Adaptation)	⬜
OpenAI: GPT-4o mini Fine-Tuning Build Hour	⬜
OpenAI: Distillation Build Hour	⬜
Article: How to Generate and Use Synthetic Data for Finetuning	⬜
DeepLearning.AI: Finetuning Large Language Models	⬜
Youtube: Fine-Tuning with Axolotl	⬜
Youtube: Creating, Curating, and Cleaning Data for LLMs	⬜
Youtube: Best Practices For Fine Tuning Mistral	⬜
Youtube: Fine Tuning OpenAI Models - Best Practices	⬜
Youtube: When and Why to Fine Tune an LLM	⬜
Youtube: Slaying OOMs with PyTorch FSDP and torchao	⬜
Youtube: Napkin Math For Fine Tuning Pt. 1 w/Johno Whitaker	⬜
Youtube: Napkin Math For Fine Tuning Pt. 2 w/Johno Whitaker	⬜
Youtube: Fine Tuning LLMs for Function Calling w/Pawel Garback	⬜
Youtube: From Prompt to Model: Fine-tuning when you've already deployed LLMs in prod w/Kyle Corbitt	⬜
Youtube: Why Fine Tuning is Dead w/Emmanuel Ameisen	⬜
Benchmarking QLoRA+FSDP	⬜

LLM System Design

Resource	Progress
Article: What We’ve Learned From A Year of Building with LLMs	⬜
Article: Data Flywheels for LLM Applications	⬜
Article: LLM From the Trenches: 10 Lessons Learned Operationalizing Models at GoDaddy	⬜
Article: Emerging UX Patterns for Generative AI Apps & Copilots	⬜
Article: The Novice's LLM Training Guide	⬜
Article: Pushing ChatGPT's Structured Data Support To Its Limits	⬜
Article: GPTed: using GPT-3 for semantic prose-checking	⬜
Article: Don't worry about LLMs	⬜
Article: Things we learned about LLMs in 2024	⬜
Article: Data acquisition strategies for AI-first start-ups	⬜
DeepLearning.AI: Building Systems with the ChatGPT API	⬜
DeepLearning.AI: Building Generative AI Applications with Gradio	⬜
DeepLearning.AI: Open Source Models with Hugging Face	⬜
DeepLearning.AI: Getting Started with Mistral	⬜
LLMOps: Building with LLMs	⬜
LLM Bootcamp - Spring 2023	⬜
Youtube: A Survey of Techniques for Maximizing LLM Performance	⬜
Youtube: Building Blocks for LLM Systems & Products: Eugene Yan	⬜
Youtube: Building LLM Applications	⬜
Article: Emerging Architectures for LLM Applications	⬜
Article: Patterns for Building LLM-based Systems & Products	⬜
DeepLearning.AI: LLMOps	⬜
DeepLearning.AI: Serverless LLM apps with Amazon Bedrock	⬜
Youtube: Getting the Most Out of Your LLM Experiments	⬜

AWS

Resource	Progress
Udemy: AWS Certified Developer - Associate 2018	⬜

Langchain

Resource	Progress
Pinecone: LangChain AI Handbook	⬜
DeepLearning.AI: LangChain for LLM Application Development	⬜
DeepLearning.AI: LangChain: Chat with Your Data	⬜

Matplotlib

Resource	Progress
Datacamp: Introduction to Seaborn	⬜
Datacamp: Introduction to Matplotlib	⬜

MLFlow

Resource	Progress
Datacamp: Introduction to MLFlow	⬜

Pandas

Resource	Progress
Datacamp: Pandas Foundations	✅
Datacamp: Pandas Joins for Spreadsheet Users	⬜
Datacamp: Manipulating DataFrames with pandas	⬜
Datacamp: Merging DataFrames with pandas	⬜
Datacamp: Data Manipulation with pandas	⬜
Datacamp: Optimizing Python Code with pandas	⬜
Datacamp: Streamlined Data Ingestion with pandas	⬜
Datacamp: Analyzing Marketing Campaigns with pandas	⬜
Datacamp: Analyzing Police Activity with pandas	⬜

PyTorch

Resource	Progress
Article: PyTorch internals	⬜
Article: Taking PyTorch For Granted	⬜
Datacamp: Introduction to Deep Learning with PyTorch	⬜
Datacamp: Intermediate Deep Learning with PyTorch	⬜
Datacamp: Deep Learning for Text with PyTorch	⬜
Datacamp: Deep Learning for Images with PyTorch	⬜
Deeplizard: Neural Network Programming - Deep Learning with PyTorch	⬜

Spacy

Resource	Progress
Datacamp: Advanced NLP with spaCy	⬜

Tensorflow & Keras

Resource	Progress
Datacamp: Introduction to TensorFlow in Python	⬜
Datacamp: Deep Learning in Python	⬜
Datacamp: Introduction to Deep Learning with Keras	⬜
Datacamp: Advanced Deep Learning with Keras	⬜
Deeplizard: Keras - Python Deep Learning Neural Network API	⬜
Udacity: Intro to TensorFlow for Deep Learning	⬜

Design

Resource	Progress
Course: How to Visualize Value	⬜

Marketing

Resource	Progress
Course: Build Once, Sell Twice	⬜

Search Engine Optimization (SEO)

Resource	Progress
Course: Compound Content	⬜

Technical Writing

Resource	Progress
Google: Technical Writing Course	⬜