0

Level 0 — Solid Foundations

Build a rock-solid base in programming, mathematics, and computer science.
Python Mastery Syntax, Data Structures, OOP, Advanced Python: Decorators, Generators, Context Managers; Async & Multi-threading; Libraries: NumPy, Pandas, Matplotlib, Seaborn; Data formats: CSV, JSON, Excel.
  • Practical: Build data pipelines with Pandas, write reusable modules, practice algorithms in Python.
Mathematics for AI Linear Algebra (vectors, matrices, eigenvalues, SVD); Calculus (derivatives, gradients, multivariable calculus); Probability & Statistics (distributions, Bayes, hypothesis testing); Optimization methods.
  • Practical: Implement gradient descent, solve linear systems, probability exercises.
Computer Science Basics Algorithms & Data Structures, Complexity (Big-O), Git/GitHub, basic OS/Networking concepts.
  • Practical: Solve problems on LeetCode (easy/medium), use Git for projects.
1

Level 1 — Machine Learning

Understand core ML algorithms and evaluation; build solid predictive models.
Supervised Learning Linear Regression, Logistic Regression, Decision Trees, Random Forests, SVM, KNN.
  • Practical: Implement from scratch + scikit-learn, build classification/regression projects.
Unsupervised Learning K-Means, Hierarchical Clustering, PCA, t-SNE, UMAP.
  • Practical: Clustering user data, visualize high-dimensional embeddings.
Advanced ML Techniques Feature engineering & selection, Ensemble methods (Gradient Boosting, XGBoost, LightGBM, CatBoost), Time Series methods (ARIMA, Prophet).
  • Practical: Participate in Kaggle competitions; use cross-validation & hyperparameter tuning.
Evaluation & Tuning Metrics (Accuracy, Precision, Recall, F1, ROC-AUC), Cross-Validation, Hyperparameter Tuning, Overfitting & Regularization techniques.
2

Level 2 — Deep Learning

Design, train and debug neural networks for real problems.
Artificial Neural Networks (ANN) Neurons & layers, Activation functions, Forward & Backpropagation, Loss functions, Optimizers (SGD, Adam, AdamW).
  • Practical: Build ANN with TensorFlow or PyTorch; visualize training curves.
Convolutional Neural Networks (CNN) Convolution layers, pooling, image classification, object detection. Study ResNet, EfficientNet, transfer learning.
  • Practical: Train CNN on CIFAR/ImageNet subsets; use pre-trained models for transfer learning.
RNNs, LSTM & Seq2Seq Recurrent architectures for sequential data, Bi-LSTM, Seq2Seq with Attention.
  • Practical: Time-series forecasting, machine translation basics.
Transformers Intro Self-Attention, Multi-Head Attention, Positional Encoding. Foundation for modern NLP/LLM.
  • Practical: Implement attention mechanism and small transformer models.
Advanced DL Topics Regularization (Dropout, BatchNorm), Learning rate schedulers, Data augmentation, Advanced optimizers.
3

Level 3 — Reinforcement Learning

Train agents that learn via interaction and reward.
Core Concepts Agent, Environment, States, Actions, Rewards; Markov Decision Process (MDP); Exploration vs Exploitation.
Algorithms Q-Learning, Deep Q-Network (DQN), Policy Gradient, Actor-Critic, PPO.
  • Practical: Implement agents in OpenAI Gym, PyBullet; experiment with reward shaping.
Applications Games, Robotics simulation, Real-time decision systems.
4

Level 4 — Generative AI (GenAI)

Create models that generate images, audio, and text.
Autoencoders & VAE Representation learning and latent-space interpolation.
GANs Generator vs Discriminator, training dynamics, style transfer, image synthesis.
Diffusion Models Stable Diffusion, Imagen; modern state-of-the-art for image generation and editing.
Multimodal & Advanced GenAI Text-to-Image, Text-to-Audio, Text-to-Video; fine-tuning generative models on custom datasets.
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Level 5 — Natural Language Processing (NLP)

Process and understand human language at scale.
Text Preprocessing Tokenization, Lemmatization, Stemming, Stopwords, Cleaning pipelines.
Word Representations Word2Vec, GloVe, FastText, contextual embeddings.
Transformer Models BERT-style encoders, GPT-style decoders, fine-tuning strategies.
Vector DB & Retrieval FAISS, Milvus, semantic search, retrieval-augmented generation (RAG).
Applications Chatbots, Sentiment Analysis, Summarization, NER, Question Answering.
6

Level 6 — Large Language Models (LLMs)

Work with, fine-tune and deploy state-of-the-art LLMs.
Fine-Tuning & Prompting Supervised fine-tuning, LoRA, parameter-efficient tuning; prompt engineering and chain-of-thought prompting.
Deployment & Tooling LangChain, API integration (OpenAI/HuggingFace), RAG systems, vector search pipelines.
Efficiency & Safety Quantization, pruning, model distillation; ethics, bias detection, explainability & safety best practices.
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Level 7 — Real-World Projects

Build a portfolio of polished, deployable projects.
Project Types ML (Regression/Classification), DL (CNN/RNN/GAN), GenAI & LLM projects, RL agents.
End-to-End Pipelines Data collection → cleaning → model → evaluation → deployment → web app. CI/CD for models, monitoring.
Showcase Publish on GitHub, Dockerize projects, host demos, enter Kaggle, contribute to open-source.
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Level 8 — Expert AI Engineer

Design, scale and research production-grade AI systems.
System Design & Architecture Design scalable AI systems, microservices, model serving, observability, cost optimization.
Cloud & Scaling AWS/GCP/Azure, multi-GPU/TPU training, distributed data pipelines.
Model Ops & Optimization Quantization, pruning, mixed precision, model parallelism, inference acceleration.
Continual Learning & Research Reproduce SOTA, read ArXiv regularly, publish results, attend NeurIPS/ICML/CVPR.
✓

Enhancements & Pro Tips

Extra elements that make the roadmap dominant and career-ready.
Advanced Math & CS SVD, Eigen Decomposition, Bayesian Inference, Hypothesis Testing, Advanced algorithms & optimization techniques.
Tools & Frameworks PyTorch, TensorFlow, Hugging Face, FastAPI, Docker, Kubernetes, Colab, Kaggle.
Career Moves Kaggle, Open-source, network in AI communities, reproducible research and strong GitHub presence.
Ethics & Safety Bias detection, explainability, responsible AI practices, adversarial robustness, privacy-aware ML.