Welcome to the AI / ML repository!
From Scratch to Advanced + Projects
A comprehensive, step-by-step roadmap to master Artificial Intelligence (AI) and Machine Learning (ML) — from absolute basics to advanced concepts — with hands-on projects at every stage.
- Beginners starting from zero
- Developers transitioning into AI/ML
- Students preparing for placements, research, or startups
- Professionals upskilling for AI roles
Beginner → Intermediate → Advanced → Specializations → Real-World Projects
- Basic programming mindset
- High-school level math
- Python Basics
- Data Types & Control Flow
- Functions & OOP
- NumPy & Pandas
- Data Visualization (Matplotlib, Seaborn)
- Basic Statistics
- Linear Algebra (Vectors, Matrices)
- Calculus (Derivatives – intuition)
- Python: https://docs.python.org/3/tutorial/
- NumPy: https://numpy.org/learn/
- Pandas: https://pandas.pydata.org/docs/
- Statistics: https://www.khanacademy.org/math/statistics-probability
- Calculator App
- Student Result Analyzer
- Exploratory Data Analysis (EDA)
- CSV Data Cleaner
- Simple Data Visualization Dashboard
- What is Machine Learning?
- Supervised vs Unsupervised Learning
- Regression Algorithms
- Classification Algorithms
- Model Evaluation Metrics
- Feature Engineering
- Train/Test Split
- Cross Validation
- Scikit-Learn
- ML Crash Course: https://developers.google.com/machine-learning/crash-course
- Scikit-Learn: https://scikit-learn.org/stable/
- ML Theory: https://www.coursera.org/learn/machine-learning
- House Price Prediction
- Spam Email Classifier
- Loan Approval System
- Movie Recommendation System
- Customer Segmentation
- Neural Networks
- Backpropagation
- Activation Functions
- CNNs (Computer Vision)
- RNNs & LSTMs (Sequence Models)
- Transformers (Intro)
- Model Optimization
- Overfitting & Regularization
- TensorFlow / PyTorch
- Deep Learning Book: https://www.deeplearningbook.org/
- TensorFlow: https://www.tensorflow.org/
- PyTorch: https://pytorch.org/
- Image Classifier (CNN)
- Face Recognition System
- Chatbot using NLP
- Handwritten Digit Recognition
- Stock Price Prediction (LSTM)
- Text Preprocessing
- Word Embeddings
- Transformers
- Sentiment Analysis
- Question Answering
- Image Processing
- Object Detection
- Image Segmentation
- YOLO / OpenCV
- Markov Decision Processes
- Q-Learning
- Deep Q Networks
- Game AI
- GANs
- Diffusion Models
- Large Language Models
- Prompt Engineering
- Model Deployment
- REST APIs (FastAPI / Flask)
- Docker
- MLOps
- Cloud AI (AWS, GCP, Azure)
- Model Monitoring
- Ethics & Responsible AI
- AI Resume Screener
- Medical Diagnosis System
- Autonomous Chatbot
- Fraud Detection System
- AI SaaS Application
Artificial-Intelligence-and-Machine-Learning/
│
├── 01_Python_Basics/
├── 02_Math_for_ML/
├── 03_Data_Analysis/
├── 04_Machine_Learning/
├── 05_Deep_Learning/
├── 06_NLP/
├── 07_Computer_Vision/
├── 08_Reinforcement_Learning/
├── 09_Projects/
│ ├── Beginner/
│ ├── Intermediate/
│ └── Advanced/
│
├── datasets/
├── notebooks/
├── resources/
├── requirements.txt
└── README.md
- Follow folders in order
- Read theory → run notebooks → build projects
- Modify & experiment
- Push your own improvements
- Showcase projects on GitHub & LinkedIn
✔ Strong AI/ML fundamentals ✔ Portfolio-ready projects ✔ Industry-level skills ✔ Research & startup readiness
This repository is licensed under the MIT License. You’re free to use, modify, and distribute with attribution.
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