AI Concepts for Beginners
1. Introduction to Artificial Intelligence
- 1.1 What is AI?
- 1.2 History and Evolution of AI
- 1.3 Applications of AI in Real Life
- 1.4 Myths and Misconceptions about AI
2. Types of AI
- 2.1 Narrow AI vs General AI vs Super AI
- 2.2 Reactive Machines vs Limited Memory vs Theory of Mind
- 2.3 Strong AI vs Weak AI
3. Key Fields of AI
- 3.1 Machine Learning
- 3.2 Deep Learning
- 3.3 Natural Language Processing (NLP)
- 3.4 Computer Vision
- 3.5 Robotics
- 3.6 Expert Systems
4. Fundamentals of Machine Learning
- 4.1 What is Machine Learning?
- 4.2 Types of ML: Supervised, Unsupervised, Reinforcement Learning
- 4.3 Key Concepts: Features, Labels, Training, Testing
- 4.4 Overfitting and Underfitting
- 4.5 Model Evaluation Metrics
5. Introduction to Neural Networks
- 5.1 What is a Neural Network?
- 5.2 Architecture: Input, Hidden, and Output Layers
- 5.3 Activation Functions
- 5.4 Training with Backpropagation and Gradient Descent
6. Working with Data
- 6.1 Importance of Data in AI
- 6.2 Data Collection and Cleaning
- 6.3 Feature Engineering
- 6.4 Data Preprocessing Techniques
7. Tools and Programming Languages for AI
- 7.1 Why Python is Popular in AI
- 7.2 Key Libraries and Frameworks (NumPy, Pandas, scikit-learn, TensorFlow, PyTorch)
- 7.3 Using Jupyter Notebooks and Google Colab
8. AI Development Workflow
- 8.1 Defining the Problem
- 8.2 Preparing the Dataset
- 8.3 Selecting and Training a Model
- 8.4 Evaluating the Model
- 8.5 Hyperparameter Tuning
- 8.6 Model Deployment (Basic Overview)
9. Hands-On Beginner Projects
- 9.1 Spam Email Classifier
- 9.2 Handwritten Digit Recognition (MNIST)
- 9.3 Sentiment Analysis of Movie Reviews
- 9.4 Basic Chatbot using NLP
- 9.5 Image Classifier with CNNs
- 9.6 House Price Predictor
- 9.7 Stock Price Trend Classifier
- 9.8 Rock, Paper, Scissors Game with Computer Vision
- 9.9 Fake News Detector
- 9.10 Music Genre Classifier
- 9.11 Language Detection App
- 9.12 Number Plate Reader (OCR)
- 9.13 Personal Voice Assistant (Basic)