AI/ML Learning Roadmap

Python Programming

Variables, loops, functions, OOP, file handling

Mathematics Basics

Linear algebra, calculus, probability, statistics

Data Manipulation

NumPy, Pandas, data cleaning, preprocessing

Data Visualization

Matplotlib, Seaborn, Plotly, exploratory analysis

Supervised Learning

Linear/logistic regression, decision trees, SVM, KNN

Unsupervised Learning

K-means, hierarchical clustering, PCA, t-SNE

Model Evaluation

Cross-validation, metrics, bias-variance tradeoff

Feature Engineering

Feature selection, encoding, scaling, feature creation

Neural Networks

Perceptrons, activation functions, backpropagation

CNNs

Convolutional layers, pooling, image classification

RNNs & LSTMs

Sequence modeling, time series, text processing

Transformers

Attention mechanism, BERT, GPT architectures

Computer Vision

Object detection, segmentation, GANs, face recognition

NLP

Named entity recognition, question answering, summarization

Generative AI

Stable Diffusion, LLMs, prompt engineering, fine-tuning

Reinforcement Learning

Q-learning, policy gradients, actor-critic methods

MLOps

ML pipelines, experiment tracking, model versioning

Model Deployment

REST APIs, Docker, Kubernetes, cloud platforms

Monitoring

Model drift, performance monitoring, A/B testing

Scalability

Distributed training, model optimization, inference

Claude Code

Multi-file editing, codebase understanding, complex debugging

GitHub Copilot

Inline completions, chat mode, Copilot Workspace

Cursor & AI IDEs

Next-generation AI-native development environments

AI Workflows

TDD with AI, code review, refactoring, auto-documentation