🤖 Introduction to Machine Learning & AI

Discover the fundamentals of artificial intelligence and machine learning algorithms

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ML & AI Curriculum

12
Core Units
~85
AI/ML Concepts
20+
Algorithms
40+
Practical Projects
1

Introduction to AI and ML

Understand the fundamentals, history, and applications of artificial intelligence and machine learning.

  • What is AI and ML?
  • History and evolution
  • Types of AI systems
  • ML paradigms
  • Applications and use cases
  • Ethics and bias
  • Current limitations
  • Future directions
2

Data Preprocessing

Learn essential techniques for cleaning, transforming, and preparing data for machine learning.

  • Data collection and sources
  • Data cleaning techniques
  • Handling missing values
  • Outlier detection
  • Feature scaling
  • Encoding categorical data
  • Feature engineering
  • Data validation
3

Supervised Learning

Master algorithms that learn from labeled data to make predictions on new examples.

  • Linear regression
  • Logistic regression
  • Decision trees
  • Random forests
  • Support vector machines
  • k-Nearest neighbors
  • Naive Bayes
  • Ensemble methods
4

Unsupervised Learning

Explore algorithms that find patterns and structure in data without labeled examples.

  • K-means clustering
  • Hierarchical clustering
  • DBSCAN
  • Principal Component Analysis
  • t-SNE
  • Association rules
  • Anomaly detection
  • Dimensionality reduction
5

Model Evaluation and Validation

Learn how to assess model performance and avoid overfitting through proper validation techniques.

  • Train/validation/test splits
  • Cross-validation
  • Performance metrics
  • Confusion matrices
  • ROC curves and AUC
  • Overfitting and underfitting
  • Regularization techniques
  • Model selection
6

Neural Networks Basics

Introduction to artificial neural networks and the foundations of deep learning.

  • Perceptron model
  • Multi-layer perceptrons
  • Activation functions
  • Backpropagation
  • Gradient descent
  • Loss functions
  • Network architectures
  • Training strategies
7

Deep Learning Fundamentals

Explore advanced neural network architectures and deep learning techniques.

  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • LSTM and GRU
  • Autoencoders
  • Transfer learning
  • Regularization in deep learning
  • Optimization algorithms
  • Popular frameworks
8

Reinforcement Learning

Learn how agents can learn optimal behavior through interaction with environments.

  • Markov Decision Processes
  • Q-learning
  • Policy gradients
  • Value functions
  • Exploration vs exploitation
  • Deep Q-Networks
  • Actor-Critic methods
  • Multi-agent systems
9

Natural Language Processing

Discover techniques for processing and understanding human language with AI.

  • Text preprocessing
  • Bag of words
  • TF-IDF
  • Word embeddings
  • Named entity recognition
  • Sentiment analysis
  • Language models
  • Transformer architectures
10

Computer Vision

Learn how machines can interpret and understand visual information from images and videos.

  • Image processing basics
  • Feature extraction
  • Object detection
  • Image classification
  • Convolutional layers
  • Image segmentation
  • Face recognition
  • Video analysis
11

ML Engineering and Deployment

Learn practical skills for deploying machine learning models in production systems.

  • Model deployment strategies
  • API development
  • Model monitoring
  • A/B testing
  • Scalability considerations
  • MLOps practices
  • Version control for ML
  • Performance optimization
12

Ethics and Future of AI

Explore the ethical implications of AI and emerging trends in artificial intelligence.

  • AI bias and fairness
  • Interpretability and explainability
  • Privacy and security
  • Responsible AI development
  • Societal impact
  • Emerging technologies
  • AGI considerations
  • Career paths in AI

Unit 1: Introduction to AI and ML

Understand the fundamentals, history, and applications of artificial intelligence and machine learning.

What is AI and ML?

Learn the fundamental definitions and distinctions between artificial intelligence and machine learning.

AI Definition ML Concepts Intelligence
Artificial Intelligence (AI) is the simulation of human intelligence in machines. Machine Learning (ML) is a subset of AI that enables systems to learn and improve from experience without being explicitly programmed.
# AI vs ML Definitions
ai_ml_concepts = {
  "artificial_intelligence": {
    "definition": "Simulation of human intelligence in machines",
    "goal": "Create systems that can perform tasks requiring human-like intelligence",
    "includes": ["Reasoning", "Learning", "Perception", "Language understanding"],
    "types": ["Narrow AI", "General AI", "Superintelligence"]
  },
  "machine_learning": {
    "definition": "Subset of AI that learns from data",
    "approach": "Statistical methods to improve performance on tasks",
    "key_idea": "Learn patterns from data without explicit programming",
    "types": ["Supervised", "Unsupervised", "Reinforcement"]
  },
  "relationship": "ML ⊂ AI ⊂ Computer Science"
}

History and Evolution

Explore the key milestones and breakthroughs in the development of AI and machine learning.

Key Historical Milestones:
• 1950: Alan Turing proposes the Turing Test
• 1956: Dartmouth Conference coins "Artificial Intelligence"
• 1980s: Expert systems gain popularity
• 1990s: Statistical learning methods emerge
• 2010s: Deep learning revolution begins
AI Winters and Springs:
AI has experienced periods of high expectations followed by disappointment and reduced funding ("AI winters"), typically caused by overpromising and technical limitations of the time.
# AI Evolution Timeline
ai_timeline = {
  "1950s": {
    "key_figures": ["Alan Turing", "John McCarthy", "Marvin Minsky"],
    "achievements": ["Turing Test", "Logic Theorist", "Dartmouth Conference"],
    "focus": "Symbolic AI and logic-based reasoning"
  },
  "1980s": {
    "breakthrough": "Expert systems boom",
    "examples": ["MYCIN", "DENDRAL", "XCON"],
    "limitation": "Knowledge acquisition bottleneck"
  },
  "2010s": {
    "revolution": "Deep learning breakthrough",
    "catalysts": ["Big data", "GPU computing", "Improved algorithms"],
    "achievements": ["ImageNet victory", "AlphaGo", "GPT models"]
  }
}

Types of AI Systems

Understand different categories of AI systems based on capabilities and approach.

AI Classification by Capability:
• Narrow AI (ANI): Designed for specific tasks (current state)
• General AI (AGI): Human-level intelligence across all domains
• Superintelligence: Exceeds human intelligence in all areas
AI Classification by Approach:
• Symbolic AI: Logic and knowledge representation
• Connectionist AI: Neural networks and learning
• Evolutionary AI: Genetic algorithms and evolution
• Hybrid AI: Combines multiple approaches
# AI System Types
ai_types = {
  "by_capability": {
    "narrow_ai": {
      "description": "Specialized for specific tasks",
      "examples": ["Chess engines", "Image recognition", "Voice assistants"],
      "current_state": "Dominant form of AI today"
    },
    "general_ai": {
      "description": "Human-level general intelligence",
      "capabilities": ["Transfer learning", "Common sense reasoning", "Creativity"],
      "timeline": "Predictions range from 2030s to never"
    }
  },
  "by_learning": {
    "reactive": "Responds to current situation",
    "limited_memory": "Uses past data for decisions",
    "theory_of_mind": "Understands others' mental states",
    "self_aware": "Has consciousness and self-awareness"
  }
}