AI

Programming Roadmap Python Programming & Data Roadmap (Section 2) Python Essentials (1 Week) Language Basics Data Structures Functions & OOP File I/O Exercises Algorithm challenges Class-based projects Data Manipulation (1 Week) Key Libraries NumPy: Array operations Pandas: DataFrames Matplotlib: Visualization Projects ...

Math Foundations Roadmap Mathematics Roadmap (Section 1) Linear Algebra (1 Week) Core Concepts Vectors & Matrix Operations Eigenvalues & SVD Vector Spaces Implementation Matrix inversion with NumPy PCA intuition Calculus (1 Week) Key Topics Partial Derivatives Gradient Descent Chain Rule Practice ...

Projects Roadmap Projects & Portfolio Roadmap (Section 7) Portfolio Development Showcase Projects End-to-end ML pipeline Research paper reproduction Kaggle competition solution Documentation Technical blog posts GitHub READMEs Demo videos Career Prep Checklist Build personal portfolio site Contribute to OSS projects Prepare case studies

Ethics & Deployment Roadmap Ethics & Deployment Roadmap (Section 6) Critical Components (2 Weeks) Ethics Bias detection Model explainability Privacy preservation Deployment Flask/FastAPI serving Docker containers Monitoring (Prometheus) Implementation Checklist Audit model for fairness Containerize model with Docker Implement CI/CD pipeline

Advanced Topics Roadmap Advanced Topics Roadmap (Section 5) Specializations (4-6 Weeks) Computer Vision YOLO object detection U-Net segmentation GANs implementation NLP Transformer architectures BERT fine-tuning LLM quantization Reinforcement Q-learning Policy gradients OpenAI Gym Implementation Targets Deploy vision model with ONN...

Deep Learning Roadmap Deep Learning Roadmap (Section 4) Core Concepts (4-6 Weeks) Neural Networks Backpropagation mathematics Activation functions Weight initialization Architectures CNNs (ResNet, VGG) RNNs/LSTMs Transformers Tools TensorFlow/Keras PyTorch Lightning ONNX Runtime Implementation Checklist Build neural...

AI Foundations Roadmap AI Foundations Roadmap Module 1: AI Fundamentals (3-5 Days) Core Concepts Definitions: Narrow AI vs AGI vs ASI History: From Turing Test to modern LLMs AI Paradigms: Symbolic AI vs Machine Learning Ethics: Alignment problem, bias, existential risks Key Theories Turing Machines & Church-Turing thesis Moravec's Paradox ("Hard problems are easy") Bitter Lesson (Sutton, 2019) Module 2: Problem-Solving Foundations (5-7 Days) Search & Optimization State-space representation Algorithms: BFS, DFS, A*, Minimax Constraint satisfaction problems Know...

Core ML Roadmap Core ML Roadmap Phase 1: Supervised Learning Foundations (1.5 Weeks) Concepts Ordinary Least Squares (matrix form derivation) L1/L2 regularization geometric intuition Logistic regression probabilistic interpretation Practical Work Code linear regression from scratch with NumPy Compare L1 vs L2 on synthetic data Phase 2: Tree-Based & Ensemble Methods (1.5 Weeks) Concepts Entropy vs Gini impurity in decision trees Gradient boosting in function space XGBoost histogram-based splitting Practical Work Build decision tree classifier from scratch Hyperparameter tune XGBoost o...

Complete AI/ML Roadmap Complete AI/ML Learning Roadmap 1. Mathematics Foundations (2-4 Weeks) Linear Algebra Vectors & Matrices Eigenvalues & SVD Matrix Calculus Probability Bayesian Inference Distributions MLE 2. AI Foundation (3-4 Weeks) Core Concepts Search Algorithms (A*) Knowledge Representation Agent Architectures ...