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Artificial Intelligence

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1. Introduction to Artificial Intelligence

Lesson 1: What is AI?

  • Defining artificial intelligence
  • Historical context and milestones
  • AI applications across industries

Lesson 2: Types of AI

  • Narrow (Weak) AI vs. General (Strong) AI
  • Exploring applied AI, cognitive AI, and beyond

Lesson 3: AI Problem Solving

  • Algorithms and heuristics
  • Problem-solving using AI techniques

2. Machine Learning Foundations

Lesson 1: Introduction to Machine Learning

  • Understanding the basics of machine learning
  • Supervised, unsupervised, and reinforcement learning

Lesson 2: Data and Preprocessing

  • Data collection and preprocessing techniques
  • Feature engineering and data augmentation

Lesson 3: Model Training and Evaluation

  • Selecting and training machine learning models
  • Cross-validation, overfitting, and model evaluation metrics

3. Deep Learning Principles

Lesson 1: Neural Networks

  • Building blocks of neural networks
  • Activation functions, layers, and architectures

Lesson 2: Training Deep Neural Networks

  • Backpropagation and gradient descent
  • Optimizers, loss functions, and regularization techniques

Lesson 3: Convolutional Neural Networks (CNNs)

  • Understanding CNNs for image analysis
  • Image classification and object detection using CNNs

4. Natural Language Processing

Lesson 1: Introduction to NLP

  • Basics of natural language processing
  • Text preprocessing and tokenization

Lesson 2: Text Representation

  • Word embeddings and vectorization
  • TF-IDF and word2vec techniques

Lesson 3: NLP Applications

  • Sentiment analysis, named entity recognition
  • Machine translation and chatbots

5. Computer Vision

Lesson 1: Introduction to Computer Vision

  • Fundamentals of computer vision
  • Image processing and feature extraction

Lesson 2: Image Classification and Object Detection

  • Image classification techniques
  • Object detection using region-based and YOLO algorithms

Lesson 3: Image Segmentation and CNNs in Vision

  • Semantic and instance segmentation
  • Applying CNNs to solve complex vision tasks

6. AI Ethics and Future Trends

Lesson 1: AI Ethics and Bias

  • Ethical considerations in AI development
  • Addressing bias and fairness issues

Lesson 2: AI in Society and Work

  • Impact of AI on industries and jobs
  • Collaborating with AI systems

Lesson 3: Future Trends in AI

  • Exploring the future of AI technology
  • AI advancements and their potential implications

7. Final Exam

  • A comprehensive exam covering all course topics
  • Multiple-choice, true/false, and short answer questions
  • Prepare thoroughly for your AI knowledge assessment!

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