AI Programming Learning Path

1. Foundational Programming Skills

• Master a core programming language (Python recommended)
  • Basic syntax (variables, data types, loops, conditionals)
  • Functions and modular programming
  • Object-Oriented Programming (OOP) concepts
  • File handling and data I/O
• Version control (Git & GitHub)
  • Basic commands (clone, commit, push, pull)
  • Branching and merging workflows
• Basic software engineering practices
  • Code documentation
  • Debugging techniques
  • Unit testing fundamentals

2. Mathematics for AI

• Linear Algebra
  • Vectors, matrices and operations
  • Eigenvalues and eigenvectors
  • Matrix decomposition (PCA foundation)
• Calculus
  • Differentiation and partial derivatives
  • Gradient descent fundamentals
  • Integral calculus (for probability)
• Probability and Statistics
  • Probability distributions (normal, binomial)
  • Mean, median, variance, covariance
  • Hypothesis testing and confidence intervals
• Discrete Mathematics
  • Graph theory basics
  • Combinatorics

3. AI/ML Fundamentals

• Introduction to Machine Learning
  • Supervised vs Unsupervised learning
  • Training, validation and test sets
  • Overfitting and underfitting
• Core ML Algorithms
  • Linear and Logistic Regression
  • Decision Trees and Random Forests
  • Support Vector Machines (SVM)
  • K-Nearest Neighbors (KNN)
  • Clustering (K-Means, Hierarchical)
• Feature Engineering
  • Data cleaning and preprocessing
  • Feature scaling and normalization
  • Feature selection and extraction

4. Deep Learning

• Neural Network Basics
  • Perceptrons and activation functions
  • Feedforward neural networks
  • Backpropagation algorithm
• Deep Learning Frameworks
  • TensorFlow / Keras (beginner-friendly)
  • PyTorch (flexible for research)
  • Basic model building and training
• Advanced Neural Network Architectures
  • Convolutional Neural Networks (CNNs) for computer vision
  • Recurrent Neural Networks (RNNs) for sequential data
  • Transformers and Attention mechanisms
  • Large Language Models (LLMs) basics

5. Practical AI Development

• Data Handling Libraries
  • Numpy for numerical computations
  • Pandas for data manipulation
  • Matplotlib/Seaborn for data visualization
• Project Implementation
  • End-to-end ML project pipeline
  • Model deployment (API, cloud services)
  • Model monitoring and maintenance
• Specialization (Choose One)
  • Computer Vision (CV)
  • Natural Language Processing (NLP)
  • Reinforcement Learning
  • Generative AI (GANs, Diffusion Models)

6. Continuous Learning

• Follow AI research papers (arXiv, ICML, NeurIPS)
• Participate in Kaggle competitions
• Contribute to open-source AI projects
• Stay updated with industry trends and tools

Note: This learning path is designed for beginners with basic computer literacy. Adjust the pace based on your prior knowledge and practice consistently.