How to learn AI application development from scratch

A complete guide on learning AI and AI application development from scratch, from beginner to professional.

Developing proficiency in AI software applications requires a well-rounded understanding of various concepts and tools. Here’s a comprehensive outline for a curriculum tailored to fresh computer science graduates:

Course 1: Introduction to Artificial Intelligence

Chapter 1: Fundamentals of AI

Chapter 2: Machine Learning Basics

Chapter 3: Neural Networks and Deep Learning

Course 2: Advanced Machine Learning and Deep Learning

Chapter 4: Convolutional Neural Networks (CNNs)

  • Image classification and object detection
  • Transfer learning and pre-trained models
  • Convolutional layers and pooling

Chapter 5: Recurrent Neural Networks (RNNs) and Natural Language Processing (NLP)

  • Sequence modeling with RNNs
  • Text generation and sentiment analysis
  • Word embeddings and language models

Chapter 6: Generative Adversarial Networks (GANs)

Understanding GAN architecture

Image synthesis and style transfer

GAN applications in art and data augmentation

Course 3: AI Software Development Tools

Chapter 7: Python Programming for AI

  • Advanced Python concepts for AI development
  • Using libraries like NumPy, Pandas, and matplotlib
  • Creating virtual environments and managing dependencies

Chapter 8: TensorFlow and Keras

  • Introduction to TensorFlow and its Ecosystem
  • Building neural networks with Keras
  • Training and deploying models using TensorFlow

Chapter 9: PyTorch

  • Exploring the PyTorch framework
  • Building dynamic computational graphs
  • Leveraging PyTorch for research and production

Course 4: AI Application Development

Chapter 10: Computer Vision Applications

  • Object detection and tracking
  • Facial recognition and Biometrics
  • Medical image analysis

Chapter 11: Natural Language Processing Applications

  • Text summarization and language translation
  • Chatbots and virtual assistants
  • Sentiment analysis and named entity recognition

Chapter 12: Reinforcement Learning Applications

  • Fundamentals of reinforcement learning
  • Building agents for game-playing and control systems
  • Real-world RL applications (e.g., robotics, autonomous driving)

Course 5: AI Deployment and Ethics

Chapter 13: Model Deployment

  • Containerization with Docker
  • Deploying models on cloud platforms
  • Monitoring and scaling deployed models

Chapter 14: AI Ethics and Bias

  • Understanding bias and fairness in AI
  • Ethical considerations in AI development
  • Mitigating biases and ensuring responsible AI

Course 6: Capstone Project

  • Applying knowledge from all courses to develop a comprehensive AI software application.
  • Students can choose from various project options, such as image recognition, natural language processing, or reinforcement learning.