Artificial Intelligence (AI) Tutorial

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines which allows them to think and act like humans. It involves creating algorithms and systems that can perform tasks that require human abilities such as visual perception, speech recognition, decision-making and language translation.

Basics

This section introduces the fundamental concepts of Artificial Intelligence and its core classifications.

• Introduction
• Types of AI Based on Capabilities
• Types of AI Based on Functionalities
• AI vs. ML vs. DL

Python for AI

Python is widely used in AI for implementing algorithms, processing data, building models and working with AI and machine learning libraries.

• Introduction
• Variables
• Data Types
• Operators
• Loops
• Functions
• Data Structures
• NumPy
• Pandas

Data Preprocessing

Data preprocessing prepares raw data for analysis and model training by cleaning, transforming and organizing it into a suitable format.

• Introduction
• Data Cleaning
• Handling Missing Data
• Feature Engineering
• Splitting Data into Training and Testing Sets

Problem Solving

It is a fundamental aspect of AI which involves the design and application of algorithms to solve complex problems systematically.

Search Algorithms

Search algorithms navigate through problem spaces to find solutions.

• State Space Search
• Breadth-First Search (BFS)
• Depth-First Search (DFS)
• Uniform Cost Search (UCS)
• Bidirectional search
• Greedy Best-First Search
• A* Search Algorithm

Local Search Algorithms

It operates on a single current state and attempt to improve it incrementally by exploring neighboring states.

• Introduction
• Hill-Climbing Search
• Simulated Annealing
• Local Beam Search

Adversarial Search

It deal with competitive environments where multiple agents are in direct competition with one another.

• Introduction
• Minimax Algorithm
• Alpha-Beta Pruning

Constraint Satisfaction Problems

It is a problem-solving framework where variables have possible values and constraints restrict valid combinations of those values.

• Introduction
• Backtracking Search
• Constraint Propagation

Knowledge, Reasoning & Planning

It refers to the way information, knowledge and data are structured, stored and used by AI systems to reason, learn and make decisions.

• Introduction
• Frames
• Ontologies
• Propositional Logic
• First Order Logic (FOL)

Reasoning & Planning

Reasoning is the process by which AI systems draw conclusions, make decisions or infer new knowledge from existing information. And Planning generates a sequence of actions an intelligent agent follows to achieve specific goals.

• Reasoning
• Deductive Reasoning
• Inductive Reasoning
• Abductive Reasoning
• Planning
• Forward State Space Search

Uncertain Knowledge & Reasoning

It deals with handling incomplete or ambiguous information using methods designed to manage uncertainty.

• Introduction
• Probabilistic Reasoning
• Fuzzy Logic

Machine Learning

Machine Learning is a subset of AI that enables systems to learn patterns from data and make predictions or decisions without being explicitly programmed for every task.

Supervised Learning

In this model are trained on labeled dataset to learn the mapping from inputs to outputs.

• Introduction
• Linear Regression
• Logistic Regression
• Decision Trees
• Support Vector Machines (SVM)
• Random Forests

Semi-supervised learning

Here model uses both labeled and unlabeled data to improve learning accuracy.

• Semi-supervised learning

Unsupervised Learning

Here the model is trained on unlabeled dataset to discover patterns or structures.

• Introduction
• K-Means Clustering
• Principal Component Analysis (PCA)
• Hierarchical Clustering
• DBSCAN

Reinforcement Learning

Here the agent learns through interactions with an environment using feedbacks.

• Introduction
• Markov decision processes (MDPs)
• Bellman equation
• Q-Learning
• Deep Q-Networks (DQN)

Deep Learning

It focuses on using neural networks with many layers to model and understand complex patterns and representations in large datasets.

• Introduction
• Artificial Neural Networks
• Multi-Layer Perceptron
• Convolutional Neural Networks
• Recurrent Neural Networks
• Long Short-Term Memory networks
• Gated Recurrent Units Networks

Transformers

Transformers are neural network architectures based on attention mechanisms that model relationships between data elements and power many modern language and multimodal AI models.

• Introduction
• Attention Mechanism
• Self-Attention
• Multi-Head Attention
• Positional Encoding
• Encoder and Decoder Architecture
• BERT
• GPT

Generative AI

Generative AI focuses on creating new data examples that resemble real data, effectively learning the distribution of data to generate similar but distinct outputs.

• Introduction
• Large Language Models
• Diffusion Models
• Prompt Engineering
• RAG

AI Agent

An AI agent is a software or hardware entity that performs actions autonomously with the goal of achieving specific objectives.

• Introduction
• LLM Agents
• Tool Calling
• Agent Memory
• Multi-Agent Systems

Applications

This section highlights the real-world areas where AI techniques are used to solve language and vision-based problems.

Natural Language Processing (NLP)

• Introduction
• Text Classification
• Natural Language Generation
• Machine Translation
• Chatbots
• Speech Recognition

Computer Vision

• Introduction
• Image Recognition
• Object Detection
• Image Segmentation
• Optical Character Recognition