AI Engineering Course

Lesson 1 • AI Landscape and Key Terminology

Defines AI, ML, and deep learning and maps their relationships. Establishes shared vocabulary used throughout the entire course.

Lesson 2 • The AI Development Lifecycle

Outlines the end-to-end process from problem framing to deployment. Sets expectations for the engineering workflow covered in later chapters.

Lesson 3 • Data Fundamentals for AI

Explains data types, collection strategies, and quality assessment. Connects raw data realities to model performance outcomes.

Lesson 1 • Data Visualization for AI Workflows

Covers plotting libraries and visualization best practices for communicating model insights. Visualization skills support evaluation and stakeholder reporting.

Lesson 2 • Cloud and Compute Environments

Explains cloud platforms, GPU access, and notebook environments used in production AI work. Prepares students to run large-scale training jobs efficiently.

Lesson 3 • Python Essentials for AI

Reviews Python syntax, data structures, and functional patterns relevant to AI workflows. Ensures all students share a common coding baseline.

Lesson 4 • Numerical Computing with NumPy and Pandas

Teaches array operations, dataframe manipulation, and vectorized computation. These skills underpin every data preprocessing step in the course.

Lesson 1 • Hyperparameter Tuning and Optimization

Teaches grid search, random search, and Bayesian optimization for model tuning. Connects algorithmic understanding to practical performance improvement.

Lesson 2 • Feature Engineering and Selection

Explains how to create, transform, and select features to maximize model performance. Directly impacts model accuracy and generalization covered in evaluation sections.

Lesson 3 • Supervised Learning Algorithms

Covers regression, decision trees, SVMs, and ensemble methods with implementation focus. Builds the algorithmic toolkit used in most production ML systems.

Lesson 4 • Unsupervised Learning Techniques

Teaches clustering, dimensionality reduction, and anomaly detection algorithms. Expands the student's ability to extract structure from unlabeled data.

Lesson 1 • Neural Network Fundamentals

Explains perceptrons, activation functions, forward propagation, and backpropagation. Establishes the theoretical core that all deep learning architectures extend.

Lesson 2 • Transformer Architecture Deep Dive

Explains self-attention, multi-head attention, and positional encoding in transformers. Prepares students for large language model engineering in later chapters.

Lesson 3 • Recurrent and Sequence Models

Covers RNNs, LSTMs, and GRUs for sequential data modeling. Provides the sequence modeling background needed before studying transformers.

Lesson 4 • Training Deep Networks Effectively

Covers optimizers, learning rate schedules, batch normalization, and dropout. Addresses the practical challenges of training stable, high-performing networks.

Lesson 1 • LLM Architecture and Pretraining

Explains tokenization, pretraining objectives, and the scale factors that define modern LLMs. Grounds students in how LLMs acquire general language capabilities.

Lesson 2 • Prompt Engineering and In-Context Learning

Teaches zero-shot, few-shot, chain-of-thought, and structured prompting strategies. Provides immediate practical leverage over LLM behavior without model retraining.

Lesson 3 • Retrieval-Augmented Generation

Explains RAG pipelines combining vector search with LLM generation for grounded responses. Addresses hallucination and knowledge currency limitations of base LLMs.

Lesson 4 • Evaluating Generative AI Systems

Covers automated metrics, human evaluation frameworks, and red-teaming for LLM quality. Connects model assessment to deployment readiness decisions.

Lesson 1 • Data Pipelines and Feature Stores

Covers batch and streaming data pipelines, feature stores, and data contracts. Ensures consistent, high-quality feature delivery to models in production.

Lesson 2 • AI System Design Principles

Introduces reliability, scalability, latency, and maintainability trade-offs in AI system design. Frames engineering decisions that recur throughout production AI work.

Lesson 3 • Model Serving and Inference Optimization

Teaches REST and gRPC serving patterns, batching, quantization, and hardware acceleration. Directly impacts the cost and responsiveness of deployed AI services.

Lesson 4 • System Reliability and Fault Tolerance

Covers circuit breakers, fallback strategies, graceful degradation, and SLA management. Ensures AI systems remain robust under real-world failure conditions.

Lesson 1 • CI/CD Pipelines for Machine Learning

Covers automated testing, model validation gates, and continuous training pipelines. Enables rapid, safe iteration on models in production environments.

Lesson 2 • MLOps Principles and Maturity Levels

Defines MLOps, its maturity model, and the gap between research and production AI. Establishes the operational mindset required for the rest of the chapter.

Lesson 3 • Scaling and Cost Optimization in MLOps

Covers auto-scaling, spot compute, model caching, and infrastructure cost controls. Balances performance requirements against operational budget constraints.

Lesson 4 • Model Registry and Governance

Explains model versioning, metadata management, lineage tracking, and approval workflows. Provides the audit trail required for regulated and enterprise AI deployments.

Lesson 1 • Secure and Privacy-Preserving AI

Covers adversarial robustness, data minimization, differential privacy, and federated learning. Addresses security and privacy requirements for enterprise AI deployments.

Lesson 2 • AI Governance and Regulatory Alignment

Explains risk-based AI governance frameworks, accountability structures, and compliance processes. Prepares students to operate within emerging AI regulatory environments.

Lesson 3 • AI Risk and Harm Taxonomy

Categorizes AI harms including bias, privacy violations, misinformation, and safety failures. Provides a shared risk vocabulary for all subsequent responsible AI work.

Lesson 4 • Explainability and Interpretability

Teaches SHAP, LIME, attention visualization, and model cards for transparent AI. Supports stakeholder trust and regulatory explainability requirements.