AI and machine learning roadmap

Complete AI and Machine Learning Roadmap

Follow a structured path from math foundations and classical ML to deep learning, transformer architecture, generative AI, computer vision, NLP, MLOps, and advanced AI topics.

Programming Foundations

Python, NumPy, Pandas, and data viz — the practical foundation every ML/AI engineer needs before tackling models.

  1. Python for AI 12 min - Beginner
  2. NumPy — Array Computing 12 min - Beginner
  3. Pandas — DataFrames 12 min - Beginner
  4. Data Visualization 10 min - Beginner

Math for ML

Kill the math anxiety with beautiful 3D visualizations. Every concept shown, not just told.

  1. Vectors & Scalars 8 min - Beginner
  2. Matrices & Transformations 10 min - Beginner
  3. Dot Product & Similarity 8 min - Beginner
  4. Probability Basics 10 min - Beginner
  5. Distributions 10 min - Beginner
  6. Derivatives & Slope 12 min - Intermediate
  7. Gradients 12 min - Intermediate
  8. Chain Rule 10 min - Intermediate
  9. Entropy & Information 8 min - Intermediate

Classical ML

Understand how machines find patterns. From simple lines to powerful forests of decisions.

  1. What is Machine Learning? 8 min - Beginner
  2. Data, Features & Labels 8 min - Beginner
  3. Data Preprocessing 10 min - Beginner
  4. Train / Test Split 8 min - Beginner
  5. Linear Regression 12 min - Beginner
  6. Gradient Descent 15 min - Intermediate
  7. Logistic Regression 12 min - Intermediate
  8. K-Nearest Neighbors 10 min - Beginner
  9. Support Vector Machine 15 min - Intermediate
  10. Decision Trees 12 min - Intermediate
  11. Random Forests 12 min - Intermediate
  12. Gradient Boosting & XGBoost 15 min - Advanced
  13. K-Means Clustering 12 min - Intermediate
  14. PCA — Dimensionality Reduction 15 min - Advanced
  15. Model Evaluation Metrics 12 min - Intermediate
  16. Bias vs Variance 10 min - Intermediate
  17. Regularization (L1 & L2) 12 min - Advanced

Deep Learning

Build intuition for how neural networks learn — from a single neuron to transformers.

  1. What is a Neuron? 10 min - Beginner
  2. Activation Functions 10 min - Beginner
  3. Neural Network Architecture 12 min - Intermediate
  4. Forward Propagation 12 min - Intermediate
  5. Loss Functions 10 min - Intermediate
  6. Backpropagation 18 min - Advanced
  7. Optimizers (SGD, Adam) 15 min - Advanced
  8. Overfitting & Dropout 12 min - Intermediate
  9. Batch Normalization 12 min - Advanced
  10. CNN — Convolution 15 min - Intermediate
  11. CNN — Pooling & Full Architecture 15 min - Intermediate
  12. Transfer Learning 12 min - Intermediate
  13. RNN & Sequences 15 min - Advanced
  14. LSTM & GRU 15 min - Advanced
  15. Attention Mechanism 18 min - Advanced
  16. Transformers in AI 20 min - Advanced

NLP

How AI reads, understands, and generates human language — from tokenization to GPT.

  1. Tokenization & Preprocessing 10 min - Beginner
  2. Bag of Words & TF-IDF 10 min - Intermediate
  3. Word Embeddings 15 min - Intermediate
  4. BERT — Bidirectional Encoder 18 min - Advanced
  5. GPT — Autoregressive Generation 18 min - Advanced
  6. Fine-tuning LLMs 15 min - Advanced
  7. Prompt Engineering 12 min - Intermediate
  8. RAG — Retrieval Augmented Generation 15 min - Advanced

Computer Vision

How AI processes images — from raw pixels to recognizing faces and objects.

  1. Images as Data 10 min - Beginner
  2. Object Detection 15 min - Advanced
  3. Image Segmentation 15 min - Advanced
  4. Face Recognition Pipeline 12 min - Advanced
  5. Image Augmentation 10 min - Intermediate

Generative AI

From GANs to Diffusion Models to LLMs — how AI creates images, text, and art.

  1. Autoencoders & Latent Space 15 min - Advanced
  2. VAE — Variational Autoencoders 15 min - Advanced
  3. GANs — Generator vs Discriminator 18 min - Advanced
  4. Diffusion Models 18 min - Advanced
  5. Stable Diffusion Pipeline 15 min - Advanced
  6. LLMs at Scale 12 min - Advanced
  7. RLHF — Human Feedback Training 15 min - Advanced
  8. Multimodal Models 12 min - Advanced
  9. How ChatGPT Works 15 min - Intermediate

MLOps & Deployment

Take your models from a Jupyter notebook to a live product millions can use.

  1. ML Project Lifecycle 10 min - Intermediate
  2. Experiment Tracking 12 min - Intermediate
  3. Docker for ML 12 min - Intermediate
  4. REST API with FastAPI 15 min - Intermediate
  5. Cloud Deployment 15 min - Advanced
  6. Model Monitoring & Drift 12 min - Advanced
  7. CI/CD for ML 12 min - Advanced
  8. Edge Deployment & Optimization 15 min - Advanced

AI Tools & Applications

The frameworks, APIs, and patterns to turn LLMs and generative models into shipped applications.

  1. LangChain — chain LLMs 12 min - Intermediate
  2. OpenAI API 10 min - Beginner
  3. Hugging Face Hub 10 min - Beginner
  4. Chatbots in production 12 min - Intermediate
  5. Real-World AI Apps 12 min - Intermediate

Advanced Topics

Reinforcement Learning, Graph Neural Networks, AI Ethics, frontier fine-tuning, and scaling.

  1. Reinforcement Learning Basics 15 min - Advanced
  2. Q-Learning & Deep RL 18 min - Advanced
  3. Graph Neural Networks 18 min - Advanced
  4. Federated Learning 12 min - Advanced
  5. AI Ethics & Bias 12 min - Intermediate
  6. Explainability (SHAP & LIME) 15 min - Advanced
  7. LoRA & Parameter-Efficient Fine-tuning 15 min - Advanced
  8. Mixture of Experts (MoE) 15 min - Advanced
  9. Scaling Laws & Emergence 12 min - Advanced