AI Fundamentals

Understanding Artificial Intelligence and Machine Learning

• What you Need to Know

  • AI vs ML vs Deep Learning Concepts

    • Artificial Intelligence as the broader field of intelligent systems
    • Machine Learning as a subset focused on learning from data
    • Deep Learning as a subset using neural networks
    • Resources:
      • AI vs ML vs Deep Learning - IBM - Clear distinctions and relationships
      • Machine Learning Explained - MIT - Academic perspective on ML concepts
      • Elements of AI Course - University of Helsinki comprehensive AI introduction

  • Types of Machine Learning

    • Supervised learning with labeled training data
    • Unsupervised learning for pattern discovery
    • Reinforcement learning through trial and reward
    • Resources:
      • Machine Learning Types - Google - Google's ML crash course terminology
      • Supervised vs Unsupervised Learning - Detailed comparison with examples
      • Reinforcement Learning Introduction - OpenAI's RL fundamentals

AI Frameworks and Libraries

• What you Need to Know

  • TensorFlow and Keras Fundamentals

    • TensorFlow ecosystem and core concepts
    • Keras high-level API for neural networks
    • Model building, training, and evaluation workflows
    • Resources:
      • TensorFlow Beginner Tutorial - Official TensorFlow quickstart
      • Keras Documentation - High-level neural network API
      • TensorFlow Developer Certificate - Official certification program (free study materials)

  • PyTorch for Development and Research

    • PyTorch tensors and automatic differentiation
    • Dynamic computation graphs and debugging
    • Model development and deployment workflows
    • Resources:
      • PyTorch Tutorials - Official PyTorch learning resources
      • PyTorch for Beginners - 60-minute PyTorch introduction
      • Fast.ai Course - Practical deep learning with PyTorch

  • Scikit-learn for Traditional ML

    • Classical machine learning algorithms implementation
    • Data preprocessing and feature engineering
    • Model evaluation and selection techniques
    • Resources:
      • Scikit-learn Tutorial - Complete scikit-learn guide
      • Machine Learning with Python - IBM course using scikit-learn
      • Scikit-learn Examples - Practical implementation examples

Pre-trained Models and Model Hubs

• What you Need to Know

  • Hugging Face Transformers Library

    • Pre-trained language models and tokenizers
    • Fine-tuning and transfer learning techniques
    • Model deployment and inference optimization
    • Resources:
      • Hugging Face Course - Complete transformers library course
      • Transformers Documentation - Official library documentation
      • Hugging Face Model Hub - Thousands of pre-trained models

  • TensorFlow Hub and Model Garden

    • Pre-trained TensorFlow models for various tasks
    • Transfer learning and fine-tuning workflows
    • Model optimization and deployment strategies
    • Resources:
      • TensorFlow Hub - Repository of trained machine learning models
      • TensorFlow Model Garden - Official TensorFlow model implementations
      • Transfer Learning Tutorial - Practical transfer learning guide

  • OpenAI and API-Based Models

    • GPT models for text generation and completion
    • DALL-E for image generation from text
    • Whisper for speech recognition and transcription
    • Resources:
      • OpenAI API Documentation - Complete API reference and guides
      • OpenAI Cookbook - Practical examples and use cases
      • GPT Best Practices - Effective prompt engineering

Data Preprocessing and Feature Engineering

• What you Need to Know

  • Data Cleaning and Preparation

    • Handling missing data and outliers
    • Data type conversion and normalization
    • Exploratory data analysis techniques
    • Resources:
      • Pandas Data Cleaning - Missing data handling techniques
      • Data Preprocessing Guide - Scikit-learn preprocessing methods
      • Python Data Cleaning Cookbook - Practical data cleaning examples

  • Feature Engineering and Selection

    • Creating meaningful features from raw data
    • Feature scaling and transformation techniques
    • Dimensionality reduction and feature selection
    • Resources:
      • Feature Engineering Guide - Google's feature engineering best practices
      • Feature Selection Techniques - Scikit-learn feature selection methods
      • Feature Engineering for Machine Learning - O'Reilly book (free chapters available)

Model Training and Evaluation

• What you Need to Know

  • Training Workflows and Best Practices

    • Data splitting strategies (train/validation/test)
    • Hyperparameter tuning and optimization
    • Cross-validation and model selection
    • Resources:
      • Model Evaluation Guide - Comprehensive evaluation metrics
      • Hyperparameter Tuning - Grid search and random search techniques
      • Cross-Validation Tutorial - Model validation strategies

  • Performance Metrics and Interpretation

    • Classification metrics (accuracy, precision, recall, F1-score)
    • Regression metrics (MSE, MAE, R-squared)
    • Model interpretability and explainability techniques
    • Resources:
      • Classification Metrics - Understanding classification performance
      • Regression Metrics - Regression evaluation techniques
      • Model Interpretability - Free book on interpretable machine learning

AI Ethics and Responsible Development

• What you Need to Know

  • Bias and Fairness in AI Systems

    • Understanding algorithmic bias and discrimination
    • Fairness metrics and bias detection techniques
    • Strategies for building more equitable AI systems
    • Resources:
      • AI Ethics Course - MIT - MIT OpenCourseWare ethics course
      • Fairness in Machine Learning - Free book on fairness and accountability
      • Google AI Ethics - AI principles and ethical guidelines

  • Privacy and Security Considerations

    • Data privacy and protection techniques
    • Adversarial attacks and model robustness
    • Secure AI development practices
    • Resources:
      • Privacy-Preserving ML - OpenMined privacy techniques
      • Adversarial ML - Understanding and defending against attacks
      • AI Security Best Practices - OWASP ML security guidelines

Hands-On Practice and Projects

• What you Need to Know

  • Beginner-Friendly AI Projects

    • Image classification with pre-trained models
    • Text sentiment analysis using transformers
    • Simple recommendation systems
    • Resources:
      • TensorFlow Beginner Projects - Step-by-step project tutorials
      • Kaggle Learn - Free micro-courses with hands-on exercises
      • Google Colab Notebooks - Free GPU-enabled development environment

  • Building Your First AI Application

    • End-to-end project workflow from data to deployment
    • Creating simple web interfaces for AI models
    • Version control and project documentation
    • Resources:
      • Streamlit AI Apps - Rapid prototyping for AI applications
      • Gradio Interface Tutorial - Creating ML model interfaces
      • Flask ML API Tutorial - Building ML APIs

Development Environment and Tools

• What you Need to Know

  • Jupyter Notebooks and Development Setup

    • Jupyter notebook best practices and extensions
    • Virtual environments and dependency management
    • Code organization and project structure
    • Resources:
      • Jupyter Notebook Tutorial - Interactive development environment
      • Python Virtual Environments - Isolated development environments
      • Cookiecutter Data Science - Project template for data science

  • Version Control for AI Projects

    • Git workflows for data science and ML projects
    • Managing large datasets and model files
    • Collaborative development practices
    • Resources:
      • Git for Data Science - Version control best practices
      • DVC (Data Version Control) - Version control for data and models
      • MLflow Tracking - Experiment tracking and model management

Ready to Advance? Continue to Module 2: LLM Integration to master language model integration and prompt engineering for AI applications.