Study Topics
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Foundations
Math, statistics, and programming fundamentals
Linear Algebra
Master vectors, matrices, and transformations that form the mathematical backbone of machine learning
Calculus & Optimization
Learn differentiation, gradients, and optimization techniques essential for training neural networks
Probability & Statistics
Master probabilistic thinking and statistical inference for understanding ML models
Machine Learning
Core ML algorithms and techniques
Supervised Learning
Master classification and regression algorithms from fundamentals to advanced techniques
Unsupervised Learning
Learn clustering, dimensionality reduction, and pattern discovery in unlabeled data
Model Evaluation & Validation
Learn to properly evaluate ML models and avoid common pitfalls like overfitting
Deep Learning
Neural networks and architectures
Neural Network Fundamentals
Understand the building blocks of deep learning from perceptrons to multi-layer networks
Convolutional Neural Networks
Master CNNs for image classification, object detection, and computer vision tasks
RNNs & Sequence Models
Learn recurrent architectures for sequential data like text and time series
Deep Learning Frameworks
Master PyTorch and TensorFlow for building and training deep learning models
LLM & GenAI
Large language models and generative AI
Transformer Architecture
Master the Transformer - the foundational architecture behind GPT, BERT, and modern LLMs
Fine-tuning LLMs
Learn to customize LLMs for specific tasks through fine-tuning and adaptation
LLM Evaluation
Learn to properly evaluate LLM outputs for quality, safety, and task performance
Research
Research methodology and advanced topics
Reading ML Papers
Develop the skill of efficiently reading and understanding machine learning research papers
ML Research Methodology
Learn the scientific method applied to machine learning research
AI Safety & Alignment
Understand the challenges of building AI systems that are safe, aligned, and beneficial