AI Basics Course

Lesson 1 • Defining Artificial Intelligence

Clarifies what AI is, what it is not, and how definitions have evolved. Grounds the chapter in precise vocabulary students will use throughout the course.

Lesson 2 • Core Branches of AI

Maps the major subfields—machine learning, NLP, computer vision, and more. Students gain a taxonomy for categorizing AI tools they encounter at work.

Lesson 3 • Myths and Realistic Expectations

Addresses hype, fear, and inflated claims surrounding AI. Students leave with calibrated expectations that support sound professional judgment.

Lesson 1 • The Model Lifecycle

Traces a model from data collection through deployment and retirement. Prepares students to participate in cross-functional AI project discussions.

Lesson 2 • Data as the Foundation of AI

Shows why data quality and quantity determine model performance. Establishes data literacy as a prerequisite for understanding every subsequent ML concept.

Lesson 3 • Supervised Learning Explained

Covers training models on labeled examples to predict outcomes. Students understand classification and regression as the backbone of most business AI applications.

Lesson 4 • Unsupervised and Reinforcement Learning

Introduces pattern discovery without labels and reward-based learning. Broadens students' awareness of AI approaches beyond supervised methods.

Lesson 1 • Transfer Learning and Pretrained Models

Shows how pretrained models reduce training cost and data requirements. Students understand why most practical AI projects reuse existing model weights.

Lesson 2 • Training Neural Networks

Explains backpropagation and gradient descent in plain language. Students understand how networks improve iteratively without needing calculus expertise.

Lesson 3 • Recurrent Networks and Sequence Data

Introduces RNNs and LSTMs for processing time-series and text. Bridges to natural language processing topics covered in the next chapter.

Lesson 4 • Convolutional Neural Networks for Images

Covers how CNNs detect patterns in visual data through filters and pooling. Links directly to computer vision applications students will encounter in practice.

Lesson 1 • Transformers and Attention Mechanisms

Introduces the transformer architecture that underpins modern language models. Prepares students to understand GPT, BERT, and similar systems at a conceptual level.

Lesson 2 • Text Preprocessing Fundamentals

Covers tokenization, stemming, stop-word removal, and normalization. These steps are the entry point for every NLP pipeline students will work with.

Lesson 3 • Representing Words as Numbers

Explains bag-of-words, TF-IDF, and word embeddings like Word2Vec. Students understand why numerical representation is essential for machine comprehension.

Lesson 4 • Evaluating NLP Model Quality

Teaches BLEU, ROUGE, and human evaluation methods for language tasks. Students can assess vendor claims and internal model outputs with appropriate skepticism.

Lesson 1 • Retrieval-Augmented Generation

Explains how external knowledge bases are combined with LLMs to reduce hallucination. Students understand RAG as the standard approach for enterprise AI accuracy.

Lesson 2 • Generative Models Beyond Text

Covers image generation via diffusion models and multimodal AI systems. Expands students' awareness of generative AI beyond text-based applications.

Lesson 3 • Fine-Tuning and Customizing LLMs

Covers instruction tuning, RLHF, and parameter-efficient fine-tuning methods. Students understand when and why organizations adapt base models for specific domains.

Lesson 4 • How Large Language Models Work

Describes pretraining on massive corpora and next-token prediction at scale. Students gain an accurate mental model of what LLMs can and cannot do.

Lesson 1 • Responsible AI Frameworks and Governance

Surveys industry and regulatory frameworks for accountable AI deployment. Students can map their organization's practices against established responsible AI standards.

Lesson 2 • Transparency and Explainability

Covers interpretable models, SHAP values, and explainability techniques. Students can demand and evaluate explanations from AI vendors and internal teams.

Lesson 3 • Sources and Types of AI Bias

Traces bias from data collection through model outputs and societal impact. Students recognize bias as a systemic issue, not an isolated technical glitch.

Lesson 4 • Fairness Definitions and Trade-offs

Presents competing mathematical definitions of fairness and their incompatibilities. Students understand that fairness requires explicit value choices, not just better algorithms.

Lesson 1 • AI Project Planning and Scoping

Covers problem framing, success metrics, and stakeholder alignment for AI projects. Students produce a project brief that guides cross-functional teams effectively.

Lesson 2 • MLOps and Production Infrastructure

Introduces model versioning, CI/CD pipelines, and monitoring in production. Students understand the operational backbone that keeps AI systems reliable over time.

Lesson 3 • Measuring AI ROI and Business Impact

Provides frameworks for quantifying AI value beyond technical metrics. Students can present a compelling business case to executives and budget holders.

Lesson 4 • Change Management for AI Adoption

Addresses workforce concerns, training needs, and cultural resistance to AI. Students apply change management tactics that increase adoption and reduce friction.

Lesson 1 • Emerging AI Trends to Watch

Surveys agentic AI, foundation models, neuromorphic computing, and quantum AI. Students develop a habit of structured horizon scanning to stay professionally current.

Lesson 2 • Sustaining a Learning Organization

Embeds continuous learning, experimentation culture, and knowledge sharing into AI strategy. Students leave with a personal and organizational plan for ongoing AI readiness.

Lesson 3 • Competitive Dynamics and AI Risk

Analyzes first-mover advantages, commoditization risks, and strategic AI threats. Students anticipate competitive shifts and position their organizations proactively.

Lesson 4 • AI Talent and Partnership Models

Covers hiring, contracting, and ecosystem partnerships for AI capability. Students design a talent strategy that balances speed, cost, and long-term knowledge retention.