🚀 AI & Machine Learning Roadmap (Modern & Clean)

A complete, modern, industry-aligned roadmap to becoming an AI Engineer, ML Engineer, or GenAI Developer.
This roadmap includes fundamentals → advanced concepts → GenAI → AI engineering stack → industry-level projects.

---

🌟 Overview

This roadmap is divided into 6 major phases, covering everything from Python basics to Deep Learning, Generative AI, and real-world AI deployment. Each phase builds on the previous one, helping you progress from beginner to industry-ready developer.

---

🧩 1. Python & Data Foundations

Build a strong foundation in Python programming and essential data libraries.

✨ Core Python

• Variables & Data Types
• Operators
• Conditional Statements
• Loops (for, while)
• Functions & Lambda
• Modules & Packages
• Exception Handling
• File Handling
• JSON Module

✨ Python Data Structures

• List & List Comprehensions
• Tuple
• Dictionary
• Set

✨ Object-Oriented Programming (OOP)

• Classes & Objects
• Constructors
• Inheritance
• Polymorphism
• Encapsulation & Abstraction
• Magic/dunder methods

✨ Data Handling & Visualization

• Data collection
• Data preprocessing
• Exploratory Data Analysis (EDA)
• NumPy (arrays, math ops)
• Pandas (dataframes, cleaning, merging)
• Matplotlib & Seaborn (visualizations)

---

🤖 2. Machine Learning (ML)

Master ML fundamentals, math, algorithms, and evaluation techniques.

✨ Mathematics for ML

• Linear Algebra (vectors, matrices, dot product)
• Calculus (derivatives, gradients)
• Probability & Statistics
• Central Limit Theorem
• Correlation & Covariance

✨ Supervised Learning

• Regression (Linear, Polynomial)
• Classification
• Algorithms:
  • Linear Regression
  • Logistic Regression
  • Naive Bayes
  • KNN
  • Decision Trees
  • Random Forest
  • SVM

✨ Unsupervised Learning

• Clustering
• Association
• Algorithms:
  • K-means
  • PCA
  • Hierarchical clustering
  • Dimensionality reduction

✨ Reinforcement Learning (Basics)

• Agent & Environment
• Reward systems
• Q-learning fundamentals

✨ Evaluation Metrics

• Accuracy
• Precision & Recall
• F1 Score
• Confusion Matrix
• ROC & AUC
• Bias-Variance Tradeoff

✨ Tools

• Scikit-learn
• Kaggle (competitions + datasets)

---

🧠 3. Deep Learning

Dive into neural networks, architectures, and deep learning frameworks.

✨ Neural Networks Basics

• Perceptron
• Activation functions
• Loss functions
• Gradient Descent
• Forward Propagation
• Backward Propagation (Backprop)

✨ Architectures

• FNN / ANN (Feed Forward Neural Network)
• CNN (Convolutional Neural Network)
• RNN (Recurrent Neural Network)
• LSTM (Long Short-Term Memory)
• GRU (optional but useful)
• Transformers

✨ Deep Learning Tools

• TensorFlow
• Keras
• PyTorch
• Model optimization & tuning

---

🤯 4. Generative AI (GenAI)

Learn the technologies powering modern AI assistants, text generators, and multimodal models.

✨ GenAI Concepts

• What is GenAI?
• How LLMs work
• Tokenization
• Embeddings
• Attention mechanism

✨ Core Topics

• NLP (Natural Language Processing)
• LLMs & Agents
• RAG (Retrieval Augmented Generation)
• GANs (Generative Adversarial Networks)
• Prompt Engineering
• Vector Databases
  • FAISS
  • Pinecone
  • Weaviate
  • Milvus

✨ Tools & Platforms

• OpenAI API
• HuggingFace Transformers
• GitHub Copilot, Cursor AI, Claude

---

🏗️ 5. AI Engineering Stack

Learn to deploy, scale, and integrate AI models into production systems.

✨ Backend & Deployment

• Flask for AI APIs
• FastAPI (optional but recommended)
• REST API development

✨ Frontend Basics

• HTML, CSS, JS
• Integrating AI into web apps

✨ Databases

• SQL (for Data Science)
• NoSQL basics

✨ DevOps & MLOps

• Git & GitHub
• Docker (containerization)
• Kubernetes (orchestrations)
• MLflow (optional but powerful)
• Airflow (optional)

---

💼 6. Projects (Minor + Major)

Build real-world, industry-level AI applications.

✨ Machine Learning Projects

• House Price Prediction
• Credit Risk Modeling
• Student Performance Prediction
• Spam Classifier
• Fake News Detector
• Customer Segmentation

✨ Deep Learning Projects

• Image Classification
• Emotion Detection
• Object Detection (YOLO)
• Text Classification (LSTM/Transformer)

✨ GenAI Projects

• RAG-powered Chatbot
• AI Note-taking Assistant
• Code Assistant
• PDF QA Application
• AI Email Writer
• Generative Image Creator

✨ Industry-Specific Projects

• Finance: Fraud detection, stock prediction
• E-Commerce: Recommendation systems
• Healthcare: Disease prediction
• Media: Content generation, summarization

✨ Capstone Projects

• End-to-end AI system
• Full-stack AI app (MERN + AI)
• Mobile AI app (React Native + LLM)

---

🧭 Roadmap Flow (Quick Summary)

1. Python → Data Analysis
2. ML Algorithms → Math → Projects
3. Deep Learning → CNN → RNN → Transformers
4. Generative AI → RAG → Agents
5. AI Engineering → Deployment → Cloud
6. Industry-Level Projects

---

📌 Notes

This roadmap is designed to make you industry-ready, covering all skills required for modern AI roles.

If followed properly, you will be able to:

• Build ML/AI models from scratch
• Work with LLMs and GenAI agents
• Deploy AI apps using Flask, Docker, and Kubernetes
• Create professional, domain-specific AI solutions

---

⭐ Happy Learning & Building! Let AI shape your future.