Study Topics

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

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

💬

LLM & GenAI

Large language models and generative AI

💬

Prompt Engineering

Learn techniques to effectively communicate with and extract value from LLMs

🤖 LLM🚀 Full-Stack📊 Data