Learn AI the real way - pawanmkr/pawanmkr GitHub Wiki

AI Learning Roadmap for Backend Developers

Phase 1: Foundation (1-2 months)

Python Fundamentals

  • Python basics review (data types, loops, functions)
    • Exercise: Write a function to find prime numbers in a range
  • File handling and error handling
    • Exercise: Create a log file parser that handles missing files gracefully
  • Object-oriented programming concepts
    • Exercise: Build a simple library management system with classes

Essential Libraries

  • NumPy: Arrays, mathematical operations
    • Exercise: Create a matrix calculator (addition, multiplication, transpose)
  • Pandas: DataFrames, data manipulation, CSV handling
    • Exercise: Clean and analyze a messy dataset (missing values, duplicates)
  • Matplotlib: Basic plotting and visualization
    • Exercise: Create 5 different chart types from same dataset
  • Jupyter Notebook: Setup and basic usage
    • Exercise: Document a complete data analysis workflow

Math Essentials

  • Linear algebra basics (vectors, matrices)
    • Exercise: Implement dot product and matrix multiplication from scratch
  • Statistics fundamentals (mean, median, standard deviation)
    • Exercise: Calculate all statistics manually, then verify with NumPy
  • Probability basics
    • Exercise: Simulate coin flips and dice rolls, compare with theory
  • Basic calculus concepts (derivatives - optional for now)
    • Exercise: Plot function and its derivative using matplotlib

Projects

  • Sales Data Analyzer: Read CSV, calculate trends, create visualizations
  • Weather Data Dashboard: API integration + data visualization
  • Student Grade Calculator: Statistical analysis with pandas
  • Stock Price Tracker: Fetch data, calculate moving averages
  • Personal Expense Analyzer: Import bank statements, categorize expenses

Phase 2: Machine Learning Basics (2-3 months)

Core ML Concepts

  • Supervised vs Unsupervised learning
    • Exercise: Classify 10 real-world problems as supervised/unsupervised
  • Training, validation, test sets
    • Exercise: Split a dataset using 60-20-20 rule, explain why
  • Overfitting and underfitting
    • Exercise: Deliberately create overfitted model, then fix it
  • Feature engineering basics
    • Exercise: Transform raw text data into numerical features
  • Model evaluation metrics
    • Exercise: Calculate precision, recall, F1 manually for confusion matrix

Scikit-learn Mastery

  • Linear regression
    • Exercise: Predict salary from experience, interpret coefficients
  • Logistic regression
    • Exercise: Binary classification with probability interpretation
  • Decision trees
    • Exercise: Visualize decision tree and explain each split
  • Random forests
    • Exercise: Compare single tree vs forest performance
  • K-means clustering
    • Exercise: Find optimal number of clusters using elbow method
  • Cross-validation
    • Exercise: Implement k-fold CV from scratch, compare with sklearn

Projects

  • House Price Predictor: Regression with multiple features
  • Email Spam Classifier: Text classification
  • Customer Segmentation: Clustering analysis for e-commerce
  • Loan Approval System: Binary classification
  • Movie Recommendation Engine: Basic collaborative filtering
  • Fraud Detection System: Anomaly detection
  • Sales Forecasting Tool: Time series prediction

Kaggle Practice

  • Complete 3 beginner competitions
  • Achieve first submission in any competition
  • Study winning solutions and notebooks

Phase 3: Deep Learning (2-3 months)

Neural Network Fundamentals

  • Perceptron and multi-layer perceptrons
    • Exercise: Implement single perceptron for AND/OR gate from scratch
  • Backpropagation algorithm
    • Exercise: Manually calculate gradients for 2-layer network
  • Activation functions
    • Exercise: Compare sigmoid, ReLU, tanh on same dataset
  • Loss functions and optimization
    • Exercise: Implement gradient descent from scratch

TensorFlow/Keras or PyTorch

  • Framework setup and basics
    • Exercise: Build "Hello World" neural network for XOR problem
  • Building neural networks
    • Exercise: Create 3-layer network with different architectures
  • Training and validation loops
    • Exercise: Plot training/validation loss curves, identify overfitting
  • Saving and loading models
    • Exercise: Train model, save it, load in new session, make predictions

Specialized Networks

  • CNN (Convolutional Neural Networks): Image processing
    • Exercise: Build CNN from scratch, visualize feature maps
  • RNN/LSTM: Sequential data processing
    • Exercise: Predict next character in text using LSTM
  • Transfer learning: Using pre-trained models
    • Exercise: Fine-tune pre-trained model on custom dataset

Projects

  • Image Classifier: Cat vs Dog classification
  • Handwritten Digit Recognition: MNIST dataset
  • Sentiment Analysis Tool: Movie reviews classification
  • Stock Price Prediction: LSTM for time series
  • Face Mask Detector: Real-time image classification
  • News Article Categorizer: Text classification with neural networks
  • Music Genre Classifier: Audio processing with CNN
  • Chatbot: Basic sequence-to-sequence model

Phase 4: Advanced Applications (1-2 months)

Computer Vision

  • OpenCV basics
    • Exercise: Load image, resize, convert to grayscale, apply filters
  • Image preprocessing techniques
    • Exercise: Implement edge detection, noise reduction on sample images
  • Object detection concepts
    • Exercise: Use pre-trained YOLO model to detect objects in video
  • Face recognition systems
    • Exercise: Build face recognition system using OpenCV + dlib

Natural Language Processing

  • Text preprocessing (tokenization, stemming)
    • Exercise: Clean Twitter dataset, remove stopwords, stem words
  • Word embeddings (Word2Vec, GloVe)
    • Exercise: Train word2vec on corpus, find similar words
  • Named Entity Recognition
    • Exercise: Extract names, locations from news articles
  • Basic transformer concepts
    • Exercise: Use pre-trained BERT for text classification

Model Deployment

  • Flask/FastAPI for ML APIs
    • Exercise: Deploy trained model as REST API with documentation
  • Docker containerization
    • Exercise: Containerize ML API, test locally
  • Model versioning
    • Exercise: Implement A/B testing between two model versions
  • Basic cloud deployment (AWS/GCP)
    • Exercise: Deploy model to cloud, create public endpoint

Projects

  • Document Scanner App: Image processing pipeline
  • Resume Parser: NLP for information extraction
  • Real-time Object Detection: Webcam integration
  • Language Translator: Sequence-to-sequence model
  • Question Answering System: Information retrieval
  • Automated Trading Bot: ML + API integration
  • Medical Image Analysis: X-ray classification

Portfolio & Career Preparation

GitHub Portfolio

  • Create dedicated AI/ML repository
  • Document each project with proper README
  • Include Jupyter notebooks with explanations
  • Add model performance metrics and visualizations

Professional Development

  • LinkedIn posts about learning journey
  • Technical blog posts about projects
  • Participate in AI/ML communities
  • Contribute to open-source ML projects

Job Preparation

  • Prepare ML interview questions
  • Practice explaining projects in simple terms
  • Build end-to-end project demonstrations
  • Network with ML professionals

Resources

Learning Platforms

  • YouTube: Krish Naik, CodeBasics, 3Blue1Brown
  • Coursera: Andrew Ng's Machine Learning Course
  • edX: MIT Introduction to Machine Learning
  • Kaggle Learn: Free micro-courses

Practice Platforms

  • Kaggle: Competitions and datasets
  • Google Colab: Free GPU for training
  • Papers with Code: Latest research implementations
  • GitHub: Explore trending ML repositories

Books (Optional)

  • "Hands-On Machine Learning" by Aurélien Géron
  • "Pattern Recognition and Machine Learning" by Christopher Bishop
  • "Deep Learning" by Ian Goodfellow

Weekly Schedule Template

Week Structure

  • Monday-Wednesday: Theory and tutorials (2-3 hours/day)
  • Thursday-Friday: Hands-on coding and projects (3-4 hours/day)
  • Saturday: Project completion and documentation
  • Sunday: Review, reflection, and planning next week

Monthly Goals

  • Complete 2-3 projects per month
  • Participate in 1 Kaggle competition
  • Write 1 technical blog post
  • Network with 5 new people in AI community

Current Progress Tracker

Start Date: _____________

Current Phase: Phase 1 - Foundation

Completed Projects: 0/25

Next Milestone: Complete Sales Data Analyzer project

Notes:

  • Focus on building strong foundations
  • Don't rush through math concepts
  • Practice coding daily, even if 30 minutes