Introduction To Artificial intelligence Course

Lesson 1 • Core Branches of AI

Maps the major subfields—machine learning, NLP, computer vision, robotics—and their relationships. Helps learners navigate the AI ecosystem before diving into technical details.

Lesson 2 • History and Evolution of AI

Traces AI from early symbolic systems through modern deep learning, highlighting pivotal breakthroughs. Provides historical context that explains current capabilities and limitations.

Lesson 3 • How AI Systems Learn

Introduces the concept of learning from data as the engine behind modern AI, contrasting it with rule-based programming. Sets up the machine learning chapters that follow.

Lesson 4 • AI in Society and Industry

Surveys how AI is deployed across healthcare, finance, manufacturing, and other sectors. Motivates learners by connecting abstract concepts to tangible professional applications.

Lesson 1 • Probability and Statistics Basics

Introduces probability distributions, Bayes' theorem, and descriptive statistics essential for model evaluation. Provides the statistical reasoning needed to interpret AI outputs.

Lesson 2 • Exploratory Data Analysis

Teaches visualization and summary techniques to uncover patterns before modeling. Reinforces statistical concepts by applying them to real datasets.

Lesson 3 • Data Types and Data Quality

Distinguishes structured, unstructured, and semi-structured data and addresses quality issues like missing values and bias. Prepares learners to assess dataset suitability before model training.

Lesson 4 • Linear Algebra for AI

Covers vectors, matrices, and tensor operations that underpin neural networks and data transformations. Directly enables understanding of model weight computations covered later.

Lesson 1 • Unsupervised Learning Techniques

Covers clustering, dimensionality reduction, and anomaly detection without labeled data. Expands learners' toolkit for exploratory and pattern-discovery tasks.

Lesson 2 • Bias, Variance, and Regularization

Explains the bias-variance tradeoff and introduces regularization techniques to control overfitting. Directly applies the overfitting concept introduced in Chapter 1.

Lesson 3 • Supervised Learning Principles

Defines supervised learning, labeled data, and the training loop for classification and regression tasks. Establishes the paradigm most commonly encountered in professional AI projects.

Lesson 4 • Model Training and Evaluation

Teaches train-test splits, cross-validation, and key metrics such as accuracy, precision, recall, and F1. Equips learners to rigorously assess model performance before deployment.

Lesson 1 • Convolutional Neural Networks

Introduces convolution, pooling, and feature maps as the basis for image recognition models. Connects computer vision applications from Chapter 1 to concrete architectural choices.

Lesson 2 • Perceptrons and Activation Functions

Traces the perceptron as the atomic unit of neural networks and explains how activation functions introduce nonlinearity. Grounds deep learning in the ML principles from Chapter 3.

Lesson 3 • Feedforward Network Architecture

Describes layers, neurons, and the forward pass through a multilayer perceptron. Establishes the structural vocabulary used throughout the deep learning sections.

Lesson 4 • Backpropagation and Gradient Descent

Explains how errors propagate backward through a network to update weights using calculus from Chapter 2. Learners understand why and how neural networks improve during training.

Lesson 1 • Word Embeddings and Semantic Vectors

Explains dense vector representations that capture semantic relationships between words. Bridges classical NLP from the previous section to neural language models.

Lesson 2 • Transformer Architecture

Unpacks self-attention, multi-head attention, and positional encoding as the core of modern NLP. Builds directly on the sequence modeling concepts from Chapter 4.

Lesson 3 • Large Language Models and Fine-Tuning

Surveys pre-trained LLMs, prompt engineering, and fine-tuning strategies for domain-specific tasks. Equips learners to leverage existing models rather than train from scratch.

Lesson 4 • Text Preprocessing and Representation

Covers tokenization, stemming, stop-word removal, and classical text vectorization methods. Establishes the data preparation pipeline that feeds all NLP models.

Lesson 1 • Generative Models for Images

Explains GANs, VAEs, and diffusion models as tools for image synthesis and data augmentation. Introduces generative AI concepts that recur in advanced and supplementary chapters.

Lesson 2 • Video Understanding

Covers temporal modeling, optical flow, and action recognition for video data. Extends spatial vision skills to the time dimension using sequence modeling from Chapter 4.

Lesson 3 • Image Data and Preprocessing

Covers pixel representation, color spaces, normalization, and augmentation strategies for vision datasets. Prepares image data for the CNN and vision model architectures covered next.

Lesson 4 • Multimodal AI Systems

Examines models that fuse text, image, and audio inputs, such as vision-language models. Synthesizes NLP and vision knowledge into unified cross-modal architectures.

Lesson 1 • Model Deployment Strategies

Explains REST APIs, batch inference, edge deployment, and containerization for serving AI models. Connects model training outcomes to real-world consumption by applications and users.

Lesson 2 • Experiment Tracking and Reproducibility

Introduces experiment logging, version control for data and models, and reproducible training environments. Establishes professional engineering discipline for iterative AI development.

Lesson 3 • Problem Framing and Data Strategy

Guides learners from a business problem to a well-defined ML task with a clear data acquisition plan. Ensures models are built to solve the right problem before any code is written.

Lesson 4 • Monitoring and Model Drift

Teaches data drift, concept drift, and performance degradation detection in live AI systems. Ensures learners can maintain model quality after deployment over time.

Lesson 1 • Regulatory Landscape and Compliance

Surveys emerging AI governance frameworks, transparency requirements, and accountability obligations across industries. Enables learners to align AI projects with evolving compliance expectations.

Lesson 2 • Privacy and Data Governance

Examines data minimization, consent, anonymization, and federated learning as privacy-preserving practices. Connects data strategy from Chapter 7 to ethical obligations around personal data.

Lesson 3 • Bias Sources and Fairness Metrics

Identifies how bias enters AI through data, labels, and model design, and introduces quantitative fairness metrics. Builds on data quality concepts from Chapter 2 with an ethical lens.

Lesson 4 • Explainability and Interpretability

Covers model-agnostic and model-specific explanation methods that make AI decisions transparent to stakeholders. Addresses the black-box challenge inherent in deep learning models from Chapters 4 and 5.