Artificial Intelligence Engineering Course

Lesson 1 • Core Mathematical Prerequisites

Covers linear algebra, calculus, probability, and statistics essential for AI. Connects each concept to its role in model training and inference.

Lesson 2 • History and Scope of AI

Traces AI from symbolic reasoning to modern deep learning. Establishes context for understanding why current engineering approaches emerged.

Lesson 3 • Python for AI Engineering

Establishes Python proficiency for data manipulation and model prototyping. Provides the programming foundation used throughout all subsequent chapters.

Lesson 1 • Data Cleaning and Preprocessing

Addresses missing values, duplicates, encoding, and scaling. Ensures model inputs are consistent and numerically appropriate.

Lesson 2 • Building Reproducible Data Pipelines

Designs automated, testable pipelines using workflow orchestration tools. Ensures data transformations are consistent across training and production environments.

Lesson 3 • Data Acquisition and Storage

Covers sourcing structured and unstructured data from APIs, databases, and file systems. Establishes data ingestion patterns used in all AI pipelines.

Lesson 4 • Exploratory Data Analysis

Applies statistical summaries and visualizations to understand data distributions and anomalies. Informs all downstream preprocessing and modeling decisions.

Lesson 1 • Ensemble Methods

Applies bagging, boosting, and stacking to improve predictive performance. Demonstrates how combining weak learners yields robust models.

Lesson 2 • Unsupervised Learning Methods

Covers k-means, hierarchical clustering, DBSCAN, and dimensionality reduction. Enables pattern discovery in unlabeled datasets.

Lesson 3 • Classification Algorithms

Implements logistic regression, decision trees, SVMs, and k-NN for categorical targets. Connects decision boundaries to real-world classification tasks.

Lesson 4 • Regression Algorithms

Covers linear, polynomial, and regularized regression for continuous target prediction. Builds intuition for loss functions and optimization used in all ML models.

Lesson 1 • Regularization and Generalization

Applies dropout, batch normalization, and early stopping to prevent overfitting. Ensures models generalize beyond training data.

Lesson 2 • Backpropagation and Optimization

Derives backpropagation and applies gradient-based optimizers to minimize loss. Connects mathematical theory to practical training loops.

Lesson 3 • Convolutional Neural Networks

Builds CNNs for image classification using convolution, pooling, and fully connected layers. Introduces spatial feature extraction as a core deep learning technique.

Lesson 4 • Deep Learning Frameworks in Practice

Implements models in PyTorch and TensorFlow, covering data loaders, training loops, and GPU acceleration. Bridges theory to production-grade code.

Lesson 1 • NLP Application Development

Builds end-to-end pipelines for classification, NER, summarization, and question answering. Connects NLP components to deployable product features.

Lesson 2 • Sequence Models and Attention

Implements RNNs, LSTMs, and the attention mechanism for sequential data. Provides the architectural lineage leading to transformer models.

Lesson 3 • Large Language Model Engineering

Covers pretraining objectives, fine-tuning strategies, and prompt engineering for LLMs. Equips engineers to adapt foundation models to domain-specific tasks.

Lesson 4 • Transformer Architecture

Dissects multi-head self-attention, positional encoding, and feed-forward sublayers. Enables engineers to understand and modify transformer-based models.

Lesson 1 • Vision Transformers and Modern Backbones

Applies ViT, Swin Transformer, and EfficientNet as feature extractors. Connects transformer concepts from NLP to state-of-the-art vision models.

Lesson 2 • Generative Vision Models

Builds GANs and diffusion models for image synthesis and editing. Introduces generative modeling as a distinct engineering discipline within vision.

Lesson 3 • Image Data Handling and Augmentation

Covers image loading, color spaces, resizing, and augmentation strategies. Ensures vision models receive diverse, well-formatted training data.

Lesson 4 • Object Detection Architectures

Implements anchor-based and anchor-free detectors for localizing objects in images. Extends CNN knowledge to multi-task prediction problems.

Lesson 1 • Model Packaging and Serving

Packages models as REST APIs and containerized services for inference. Enables consistent deployment across development and production environments.

Lesson 2 • CI/CD Pipelines for ML

Automates testing, validation, and deployment of model updates using CI/CD principles. Reduces manual errors and accelerates the model release cycle.

Lesson 3 • Experiment Tracking and Reproducibility

Uses experiment tracking tools to log hyperparameters, metrics, and artifacts. Establishes reproducibility as a core engineering discipline.

Lesson 4 • Scalable Infrastructure for AI

Designs cloud-native architectures using managed ML platforms and auto-scaling. Prepares engineers to handle high-throughput production AI workloads.

Lesson 1 • System Reliability and Fault Tolerance

Applies redundancy, fallback logic, and graceful degradation to AI systems. Ensures production systems meet uptime and correctness requirements.

Lesson 2 • AI System Architecture Patterns

Evaluates monolithic vs. microservice AI architectures and event-driven designs. Equips engineers to make informed architectural trade-offs at scale.

Lesson 3 • Retrieval-Augmented Generation Systems

Builds RAG pipelines combining vector search with LLM generation for knowledge-grounded responses. Addresses LLM hallucination and knowledge staleness.

Lesson 4 • Multimodal AI Systems

Integrates text, image, and audio modalities into unified model pipelines. Extends single-modality skills to cross-modal understanding and generation.