Introduction to AI & Machine Learning
What is Artificial Intelligence (AI)?
Artificial Intelligence refers to the simulation of human intelligence in machines that are designed to think, learn, reason, and make decisions.
What is Machine Learning (ML)?
Machine Learning is a subset of AI that enables systems to learn from data and improve performance without being explicitly programmed.
Types of Artificial Intelligence
- Narrow AI: Designed for specific tasks (e.g., recommendation systems)
- General AI: Human-level intelligence across tasks (theoretical)
- Superintelligent AI: Intelligence surpassing human capabilities (hypothetical)
Types of Machine Learning
- Supervised learning
- Unsupervised learning
- Semi-supervised learning
- Reinforcement learning
Mathematical Foundations for AI & ML
Linear Algebra
- Vectors and matrices
- Matrix operations
- Tensors and multidimensional data
Probability and Statistics
- Probability distributions
- Bayes’ theorem
- Mean, variance, and standard deviation
Calculus
- Derivatives and gradients
- Optimization techniques
- Gradient descent
Algorithms and Complexity
- Time and space complexity
- Algorithm efficiency
Data Collection and Preprocessing
Data Types and Sources
- Structured, semi-structured, and unstructured data
- Databases, APIs, sensors, and public datasets
Data Cleaning
- Handling missing values
- Outlier detection and treatment
Feature Engineering
- Feature scaling and normalization
- Encoding categorical variables
- Feature selection
Data Splitting
- Training set
- Validation set
- Test set
Supervised Learning
Overview of Supervised Learning
Learning from labeled datasets to predict outcomes.
Regression Algorithms
- Linear regression
- Polynomial regression
Classification Algorithms
- Logistic regression
- Decision trees
- Support Vector Machines (SVM)
- k-Nearest Neighbors (k-NN)
Model Evaluation Metrics
- Accuracy
- Precision
- Recall
- F1 score
- ROC-AUC
Unsupervised Learning
Overview of Unsupervised Learning
Finding patterns in unlabeled data.
Clustering Algorithms
- K-means clustering
- Hierarchical clustering
- DBSCAN
Dimensionality Reduction
- Principal Component Analysis (PCA)
- t-SNE
Anomaly Detection
- Identifying rare or abnormal patterns
Neural Networks and Deep Learning
Neural Network Fundamentals
- Perceptron and multilayer networks
- Activation functions
- Loss functions
Deep Learning Concepts
- Backpropagation
- Optimization algorithms
Reinforcement Learning
Fundamentals of Reinforcement Learning
Learning through interaction with an environment.
Key Concepts
- Agents
- Environments
- Rewards
- Policies
Reinforcement Learning Algorithms
- Q-learning
- Deep Q-Networks (DQN)
Applications
- Game playing
- Robotics
- Autonomous systems
Natural Language Processing (NLP)
NLP Basics
- Tokenization
- Stemming
- Lemmatization
Text Representation
- Bag of Words
- TF-IDF
- Word embeddings
NLP Models
- Recurrent Neural Networks (RNNs)
- LSTMs
- Transformers
NLP Applications
- Sentiment analysis
- Machine translation
- Chatbots
AI & ML in Practice
Model Selection and Optimization
- Choosing the right algorithm
- Hyperparameter tuning
Evaluation Techniques
- Cross-validation
- Bias-variance tradeoff
Model Deployment
- Cloud deployment
- Edge computing
Tools and Frameworks
- TensorFlow
- PyTorch
- Scikit-learn
Ethics and Bias in AI & ML
AI Bias and Fairness
- Sources of bias in data and models
- Fairness-aware learning
Ethical Considerations
- Responsible AI development
- Societal impact
Transparency and Explainability
- Interpretable models
- Explainable AI (XAI) techniques
Regulations and Guidelines
- Ethical AI frameworks
- Regulatory compliance
Advanced Topics in AI & ML
Explainable AI
- Model interpretability techniques
Privacy-Preserving Machine Learning
- Federated learning
- Secure multi-party computation
AI Automation and the Future of Work
- AI-driven automation
- Workforce transformation
Emerging Trends
- Generative AI
- Multimodal models
- Ongoing AI research