Study Topics

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🎯All Topics🤖LLM Engineer⚙️MLOps Engineer🔬ML Researcher📊Data Scientist🚀Full-Stack AI

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All🧮Foundations🤖Machine Learning🧠Deep Learning💬LLM & GenAI⚙️Engineering🔬Research
🧮

Foundations

Math, statistics, and programming fundamentals

📐

Linear Algebra

Master vectors, matrices, and transformations that form the mathematical backbone of machine learning

🔬 ML📊 Data🤖 LLM
∂

Calculus & Optimization

Learn differentiation, gradients, and optimization techniques essential for training neural networks

🔬 ML📊 Data🤖 LLM
📊

Probability & Statistics

Master probabilistic thinking and statistical inference for understanding ML models

🔬 ML📊 Data🤖 LLM
🐍

Python for ML

Master Python programming with focus on data science and machine learning applications

🤖 LLM⚙️ MLOps📊 Data+1
🤖

Machine Learning

Core ML algorithms and techniques

🎯

Supervised Learning

Master classification and regression algorithms from fundamentals to advanced techniques

📊 Data🔬 ML🤖 LLM
🔍

Unsupervised Learning

Learn clustering, dimensionality reduction, and pattern discovery in unlabeled data

📊 Data🔬 ML
✅

Model Evaluation & Validation

Learn to properly evaluate ML models and avoid common pitfalls like overfitting

📊 Data🔬 ML⚙️ MLOps
🧠

Deep Learning

Neural networks and architectures

🧠

Neural Network Fundamentals

Understand the building blocks of deep learning from perceptrons to multi-layer networks

🤖 LLM🔬 ML📊 Data
🖼️

Convolutional Neural Networks

Master CNNs for image classification, object detection, and computer vision tasks

🔬 ML📊 Data
📈

RNNs & Sequence Models

Learn recurrent architectures for sequential data like text and time series

🤖 LLM🔬 ML
🔧

Deep Learning Frameworks

Master PyTorch and TensorFlow for building and training deep learning models

🤖 LLM⚙️ MLOps🔬 ML
💬

LLM & GenAI

Large language models and generative AI

🤖

Transformer Architecture

Master the Transformer - the foundational architecture behind GPT, BERT, and modern LLMs

🤖 LLM🔬 ML
💬

Prompt Engineering

Learn techniques to effectively communicate with and extract value from LLMs

🤖 LLM🚀 Full-Stack📊 Data
📚

RAG Systems

Build Retrieval-Augmented Generation systems to ground LLMs with external knowledge

🤖 LLM🚀 Full-Stack
🎛️

Fine-tuning LLMs

Learn to customize LLMs for specific tasks through fine-tuning and adaptation

🤖 LLM🔬 ML
🤝

AI Agents & Tool Use

Build autonomous AI agents that can plan, use tools, and accomplish complex tasks

🤖 LLM🚀 Full-Stack
📊

LLM Evaluation

Learn to properly evaluate LLM outputs for quality, safety, and task performance

🤖 LLM🔬 ML
⚙️

Engineering

Deployment, MLOps, and production systems

⚙️

MLOps Fundamentals

Learn the practices and tools for deploying and maintaining ML systems in production

⚙️ MLOps🚀 Full-Stack
🚀

Model Deployment

Learn to deploy ML models as scalable APIs and services

⚙️ MLOps🚀 Full-Stack🤖 LLM
⚡

LLM Inference Optimization

Optimize LLM inference for speed, cost, and efficiency

🤖 LLM⚙️ MLOps
🗄️

Vector Databases

Learn to store and query embeddings efficiently for semantic search and RAG

🤖 LLM⚙️ MLOps🚀 Full-Stack
🔬

Research

Research methodology and advanced topics

📖

Reading ML Papers

Develop the skill of efficiently reading and understanding machine learning research papers

🔬 ML🤖 LLM
🔬

ML Research Methodology

Learn the scientific method applied to machine learning research

🔬 ML
🛡️

AI Safety & Alignment

Understand the challenges of building AI systems that are safe, aligned, and beneficial

🔬 ML🤖 LLM