AI Development Course

Lesson 1 • Setting Up the Development Environment

Configures Python, virtual environments, and key ML libraries. Hands-on setup ensures every student can execute code from chapter two onward.

Lesson 2 • History and Landscape of AI

Traces AI from symbolic systems to modern neural networks. Establishes historical context that frames every subsequent technical concept in the course.

Lesson 3 • Core AI and ML Terminology

Defines supervised, unsupervised, and reinforcement learning with precise vocabulary. Shared terminology prevents ambiguity throughout all later chapters.

Lesson 1 • Data Versioning and Pipeline Automation

Introduces data versioning tools and reproducible pipeline construction. Reproducibility established here underpins MLOps practices introduced in later chapters.

Lesson 2 • Exploratory Data Analysis Techniques

Uses statistical summaries and visualizations to reveal distributions and relationships. EDA findings directly inform feature engineering decisions in the next section.

Lesson 3 • Data Sources and Acquisition Strategies

Surveys structured databases, APIs, web scraping, and synthetic generation. Connects data origin to downstream quality and bias considerations.

Lesson 4 • Feature Engineering and Selection

Transforms raw variables into informative features and removes redundant ones. Strong features are the single largest driver of model accuracy.

Lesson 1 • Regression Models and Evaluation

Covers linear, polynomial, and regularized regression with full evaluation workflows. Regression forms the baseline against which complex models are compared.

Lesson 2 • Classification Algorithms

Implements logistic regression, decision trees, SVMs, and k-NN classifiers. Classification is the most common ML task and anchors model selection discussions.

Lesson 3 • Ensemble Methods

Builds bagging, boosting, and stacking ensembles to exceed single-model performance. Ensemble techniques are the dominant approach in structured-data competitions.

Lesson 4 • Unsupervised Learning Methods

Applies clustering and dimensionality reduction to unlabeled data. These methods enable pattern discovery when labeled data is unavailable.

Lesson 1 • Backpropagation and Optimization

Derives backpropagation and implements gradient-based optimizers. Mastery here enables students to diagnose and fix training failures in any network.

Lesson 2 • Regularization and Generalization

Applies dropout, batch normalization, and early stopping to prevent overfitting. Generalization techniques are essential before deploying any deep model.

Lesson 3 • Training Deep Networks at Scale

Covers mixed-precision training, distributed strategies, and hardware utilization. Scalable training is necessary for the large models covered in later chapters.

Lesson 4 • Convolutional Neural Networks

Builds CNNs for image classification using convolution, pooling, and skip connections. CNNs introduce spatial inductive biases used in vision and beyond.

Lesson 1 • Text Preprocessing and Representation

Covers tokenization, stemming, TF-IDF, and word embeddings. Text representation quality directly determines downstream NLP model performance.

Lesson 2 • Fine-Tuning and Prompt Engineering

Applies full fine-tuning, parameter-efficient methods, and prompt design to pretrained LLMs. These skills enable task-specific adaptation without training from scratch.

Lesson 3 • Retrieval-Augmented Generation Systems

Combines vector databases with LLMs to ground responses in external knowledge. RAG is the dominant architecture for enterprise knowledge applications.

Lesson 4 • Transformer Architecture Deep Dive

Dissects encoder, decoder, and positional encoding in the transformer. Full architectural understanding is required to fine-tune and extend LLMs effectively.

Lesson 1 • Generative Vision Models

Builds GANs and diffusion models for image synthesis and editing. Generative models underpin modern creative AI tools and data augmentation pipelines.

Lesson 2 • Object Detection Architectures

Implements one-stage and two-stage detectors with anchor and anchor-free designs. Detection is the most commercially deployed computer vision task.

Lesson 3 • Image Segmentation Techniques

Covers semantic, instance, and panoptic segmentation with encoder-decoder networks. Segmentation enables pixel-level understanding required in medical and autonomous systems.

Lesson 1 • Serving Architectures and APIs

Builds REST and gRPC inference endpoints with batching and autoscaling. Efficient serving directly determines user-facing latency and infrastructure cost.

Lesson 2 • CI/CD Pipelines for ML

Automates testing, retraining triggers, and deployment gates in ML pipelines. CI/CD reduces manual errors and accelerates safe model updates in production.

Lesson 3 • Model Packaging and Containerization

Packages trained models into reproducible containers using Docker and model registries. Containerization is the prerequisite for any cloud or on-premise deployment.

Lesson 4 • Production Monitoring and Drift Detection

Tracks data drift, concept drift, and model degradation with alerting systems. Monitoring closes the feedback loop between deployment and retraining decisions.

Lesson 1 • Model Explainability and Interpretability

Applies SHAP, LIME, and attention visualization to explain model decisions. Explainability is required for stakeholder trust and regulatory compliance.

Lesson 2 • Responsible AI Frameworks and Auditing

Structures AI audits using impact assessments, documentation standards, and governance boards. Systematic auditing translates ethical principles into operational practice.

Lesson 3 • Bias, Fairness, and Representation

Diagnoses sources of bias in data and models using quantitative fairness metrics. Fairness analysis is now a standard deliverable in regulated and consumer AI.

Lesson 4 • AI Safety and Alignment Principles

Covers reward hacking, specification gaming, and alignment research fundamentals. Safety awareness shapes responsible design choices throughout the development lifecycle.