AI Developer Course

Lesson 1 • Data Fundamentals for AI

Explains data types, structures, and quality requirements that drive model performance. Prepares students to evaluate datasets before modeling.

Lesson 2 • The AI Development Lifecycle

Maps the end-to-end process from problem framing to deployment and monitoring. Gives students a repeatable framework applied in all subsequent chapters.

Lesson 3 • AI Landscape and Key Terminology

Defines AI, ML, and deep learning with precise distinctions. Establishes shared vocabulary used throughout the entire course.

Lesson 1 • Data Manipulation with Pandas

Teaches DataFrame operations for loading, cleaning, and transforming tabular data. Prepares students to deliver analysis-ready datasets to ML pipelines.

Lesson 2 • Python Development Best Practices

Establishes coding standards, virtual environments, and version control habits for AI projects. Ensures reproducible, maintainable codebases from the start.

Lesson 3 • Scientific Computing with NumPy

Introduces array operations, broadcasting, and vectorized computation using NumPy. Directly supports tensor manipulation needed in deep learning frameworks.

Lesson 4 • Data Visualization for AI Insights

Applies Matplotlib and Seaborn to explore distributions, correlations, and model outputs. Visualization skills support feature engineering and result communication.

Lesson 1 • Model Evaluation and Selection

Defines metrics for classification, regression, and clustering tasks. Students apply cross-validation and statistical tests to compare models rigorously.

Lesson 2 • Tree-Based and Ensemble Methods

Covers decision trees, random forests, and gradient boosting for tabular data. Students learn when ensemble methods outperform linear baselines.

Lesson 3 • Supervised Learning Fundamentals

Introduces regression and classification tasks with linear models as the baseline. Connects loss functions and optimization to the math covered in Chapter 1.

Lesson 4 • Unsupervised Learning Techniques

Teaches clustering, dimensionality reduction, and anomaly detection without labeled data. Prepares students to extract structure from unlabeled datasets.

Lesson 1 • Training Deep Networks in Practice

Covers GPU setup, mixed-precision training, and experiment tracking. Students can run reproducible training runs on real hardware or cloud instances.

Lesson 2 • Backpropagation and Optimization

Derives backpropagation and connects it to gradient descent variants. Students understand how weights update during training.

Lesson 3 • Regularization and Generalization

Addresses overfitting with dropout, batch normalization, and weight decay. Connects bias-variance concepts from Chapter 3 to deep learning practice.

Lesson 4 • Neural Network Architecture Basics

Explains neurons, layers, activation functions, and forward propagation. Grounds deep learning mechanics in the linear algebra from Chapter 1.

Lesson 1 • Fine-Tuning Language Models

Covers supervised fine-tuning, parameter-efficient methods, and dataset preparation for domain adaptation. Builds on deep learning training skills from Chapter 4.

Lesson 2 • Working with Pretrained Language Models

Demonstrates loading, prompting, and evaluating pretrained LLMs via APIs and local inference. Students can integrate LLMs into applications immediately.

Lesson 3 • NLP Application Development

Applies NLP models to classification, summarization, translation, and question answering. Students deliver end-to-end NLP features within a larger system.

Lesson 4 • Text Preprocessing and Representation

Covers tokenization, embeddings, and vectorization methods for raw text. Establishes the input pipeline required by all NLP models in this chapter.

Lesson 1 • Retrieval-Augmented Generation Systems

Builds RAG pipelines combining vector search with LLM generation for knowledge-grounded responses. Extends LLM skills from Chapter 5 into full application stacks.

Lesson 2 • Frontend Integration for AI Features

Connects AI backends to web and mobile frontends with streaming, feedback loops, and UX patterns. Ensures AI outputs are presented clearly to end users.

Lesson 3 • Model Serving and API Design

Covers REST and gRPC APIs, request batching, and model server frameworks. Students expose trained models as reliable, low-latency services.

Lesson 4 • AI System Architecture Patterns

Introduces monolithic, microservice, and event-driven patterns for AI systems. Students choose architectures that match latency, scale, and reliability needs.

Lesson 1 • Model Registry and Versioning

Tracks model artifacts, metadata, and lineage across experiments and deployments. Enables reproducibility and auditability required by production teams.

Lesson 2 • CI/CD Pipelines for ML Projects

Automates testing, model validation, and deployment with continuous integration workflows. Students reduce manual errors and accelerate release cycles.

Lesson 3 • Containerization and Environment Management

Packages AI applications with Docker and manages dependencies for reproducible deployments. Prerequisite for all cloud and orchestration topics in this chapter.

Lesson 4 • Production Monitoring and Observability

Detects data drift, model degradation, and system anomalies in live deployments. Students set up dashboards and alerts that trigger retraining workflows.

Lesson 1 • AI Safety and Robustness

Covers adversarial attacks, out-of-distribution detection, and red-teaming for LLMs. Students harden models against real-world failure modes.

Lesson 2 • Explainability and Interpretability

Applies SHAP, LIME, and attention visualization to explain model decisions to stakeholders. Supports trust-building and regulatory transparency requirements.

Lesson 3 • AI Strategy and Organizational Alignment

Frames AI investment decisions, build-vs-buy trade-offs, and cross-functional collaboration. Prepares students to lead AI initiatives beyond individual model development.

Lesson 4 • AI Fairness and Bias Mitigation

Identifies sources of algorithmic bias and applies pre-, in-, and post-processing mitigation techniques. Connects dataset bias concepts from Chapter 1 to production impact.