AI vs ML vs Deep Learning vs Generative AI
Understand the differences between artificial intelligence, machine learning, deep learning, and generative AI with practical examples.
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Every curriculum topic has a complete article with visual diagrams, practical examples, general AI context, and an AWS AI Practitioner exam connection where relevant.
11 learning modules70 complete articles2 ways to learn: watch or read
Module 1 · Episodes 1–6
AI Foundations
Build the vocabulary and decision-making foundation needed for every later AI topic.
The Complete AI Lifecycle Explained
Learn the AI lifecycle from business problem and data preparation through training, deployment, monitoring, and improvement.
Supervised vs Unsupervised vs Reinforcement Learning
Compare supervised, unsupervised, and reinforcement learning and learn when each machine learning approach is appropriate.
Responsible AI Principles Explained
Learn fairness, transparency, privacy, safety, accountability, and other principles used to build trustworthy AI systems.
AI Ethics, Bias, and Explainability
Explore common sources of AI bias, ethical risks, and methods for explaining model predictions to technical and business audiences.
Neural Networks Explained for Beginners
Learn how artificial neurons, layers, weights, activation functions, and training work together in a neural network.
Module 2 · Episodes 7–11
Deep Learning Foundations
Understand how neural networks learn, activate, train, infer, and recognize visual patterns.
What Is a Perceptron?
Understand the perceptron as a basic artificial neuron and see how inputs, weights, bias, and activation produce a prediction.
Weights and Biases in Neural Networks
Learn what weights and biases mean, how they influence predictions, and why training updates them.
Activation Functions: ReLU, Sigmoid, and Tanh
Compare common neural-network activation functions and understand why nonlinearity is necessary for deep learning.
Machine Learning Training vs Inference
Understand the difference between learning model parameters during training and using the trained model for predictions during inference.
Convolutional Neural Networks (CNNs) Explained
Learn how convolution, filters, feature maps, and pooling help CNNs recognize patterns in images.
Module 3 · Episodes 12–15
Modern AI and Deep Learning
Move from sequence models to transformers and modern natural-language processing.
Recurrent Neural Networks (RNNs) Explained
Learn how recurrent neural networks process sequences and why their hidden state provides short-term memory.
Long Short-Term Memory (LSTM) Networks Explained
Understand how LSTM gates help neural networks preserve important information across longer sequences.
Transformers and Self-Attention Explained
Learn how transformer architecture and self-attention capture relationships between tokens and power modern language models.
Natural Language Processing (NLP) Explained
Learn how NLP enables computers to process, understand, classify, summarize, and generate human language.
Module 4 · Episodes 16–23
NLP and LLM Foundations
Learn how language becomes tokens, vectors, searchable meaning, and generated responses.
Tokenization in Large Language Models
Understand how language models split text into tokens and why token counts affect context windows, latency, and cost.
Tokens and Context Windows
Context windows define how much tokenized information a model can consider in one request and influence coherence, cost, and truncation.
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Embeddings Explained: How AI Represents Meaning
Learn how embeddings convert text and other data into vectors that capture semantic relationships and similarity.
Vector Search and Vector Databases Explained
Understand vector search, similarity measures, vector databases, and their role in semantic search and RAG applications.
Semantic Search Explained
Semantic search retrieves information by meaning rather than relying only on exact keyword matches.
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Attention Mechanisms Explained
Attention lets a model assign different importance to parts of its input when building a contextual representation.
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Foundation Models Explained
Foundation models are trained broadly and then adapted through prompting, retrieval, fine-tuning, or task-specific systems.
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Large Language Models (LLMs) Explained
Learn what LLMs are, how they are trained, what they can do, and where limitations such as hallucinations arise.
What Happens After You Send a ChatGPT Prompt?
Follow the path from prompt tokenization and model inference to token generation, safety checks, and the final response.
Module 5 · Episodes 24–30
Generative AI Fundamentals
Use generative AI effectively while understanding prompting, limitations, and hallucinations.
Prompt Engineering Techniques for Better AI Results
Learn practical zero-shot, few-shot, role, context, constraint, and step-based prompting techniques for generative AI.
Zero-Shot and Few-Shot Prompting
Zero-shot prompts rely on instructions, while few-shot prompts add examples that demonstrate the expected pattern.
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Temperature, Top-P, and Generation Controls
Generation controls shape how a model selects output tokens and balance predictability with variety.
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Hallucinations in Large Language Models Explained
Understand why language models can produce confident but unsupported answers and how grounding, retrieval, verification, and responsible design improve reliability.
Generative AI Capabilities and Limitations
Generative models are strong at transformation and synthesis but can be inaccurate, inconsistent, or unsuitable for unsupported high-impact decisions.
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Generative AI Business Use Cases
Useful GenAI projects connect a clear business workflow to measurable outcomes instead of deploying a model without a defined problem.
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Evaluating Generative AI Systems
Evaluation combines task-specific quality measures, human review, safety testing, groundedness, latency, and cost.
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Module 6 · Episodes 31–36
RAG and Enterprise AI
Connect models to trusted private knowledge, retrieval, and enterprise controls.
Retrieval-Augmented Generation (RAG) Explained
RAG retrieves relevant trusted information and supplies it to a generative model as context for an answer.
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The Complete RAG Pipeline
A RAG pipeline ingests documents, creates searchable representations, retrieves evidence, builds prompts, and evaluates answers.
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Document Chunking Strategies
Chunking divides source material into retrievable units while preserving enough context to answer accurately.
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Vector Databases for AI Applications
Vector databases store and index embeddings so applications can retrieve items with similar meaning efficiently.
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Enterprise Knowledge Bases
Enterprise knowledge bases organize governed business information for search, RAG, support, and decision assistance.
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RAG Security and Governance
RAG systems must enforce source permissions, protect sensitive data, resist prompt injection, and preserve auditability.
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Module 7 · Episodes 37–42
AI Agents
Understand planning, tools, memory, orchestration, and agent safety.
AI Agents Explained
An AI agent combines a model with goals, tools, state, and a control loop that selects and evaluates actions.
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Tools and Function Calling for AI Agents
Tools let agents interact with controlled external capabilities such as search, databases, calendars, or business APIs.
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Memory in AI Agents
Agent memory can retain conversation state, task history, user preferences, or retrieved facts with different lifetimes and controls.
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Planning and Reasoning in AI Agents
Planning decomposes a goal into manageable actions and revises the path when observations change.
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AI Agent Guardrails and Human Oversight
Agentic systems need bounded permissions, validation, monitoring, and human approval for consequential actions.
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Building Agents with Amazon Bedrock AgentCore
Amazon Bedrock AgentCore provides managed capabilities for building, connecting, securing, and operating AI agents.
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Module 8 · Episodes 43–54
AWS AI Services
Match business needs to Amazon Bedrock, SageMaker, and purpose-built AWS AI services.
Amazon Bedrock Explained
Amazon Bedrock is a managed service for building generative AI applications with foundation models and related capabilities.
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Amazon SageMaker AI Explained
Amazon SageMaker AI supports building, training, tuning, deploying, and governing machine-learning models.
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Amazon Q Explained
Amazon Q provides generative AI assistance for workplace knowledge and software-development workflows.
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Amazon Comprehend Explained
Amazon Comprehend applies natural-language processing to extract insights such as entities, sentiment, and key phrases.
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Amazon Rekognition Explained
Amazon Rekognition analyzes images and video for objects, text, moderation signals, and other visual information.
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Amazon Textract Explained
Amazon Textract extracts printed text, handwriting, forms, and tables from documents into structured results.
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Amazon Transcribe Explained
Amazon Transcribe converts speech audio into text and supports transcription workflows.
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Amazon Polly Explained
Amazon Polly converts text into lifelike synthesized speech for voice-enabled applications.
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Amazon Lex Explained
Amazon Lex helps build conversational interfaces that recognize user intent and collect required information.
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Choosing the Right AWS AI Service
Service selection starts with the workload: custom ML, foundation-model applications, or a purpose-built API for text, speech, vision, or documents.
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Security for AWS AI Workloads
AI workload security combines identity, encryption, network controls, data protection, logging, and least privilege.
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Cost and Performance for AWS AI
Architecture choices balance model capability, token or compute usage, latency, throughput, availability, and operational effort.
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Module 9 · Episodes 55–59
Real-World GenAI Projects
Apply the concepts through document Q&A, search, chatbots, and automation projects.
Project Guide: Build an AI Chatbot
A useful chatbot combines a defined audience, conversation design, trusted context, safety controls, and measurable success criteria.
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Project Guide: Document Question Answering
Document Q&A retrieves evidence from approved files and generates answers that users can trace back to sources.
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Project Guide: Semantic Search
Semantic search projects use embeddings and similarity retrieval to find relevant information despite vocabulary differences.
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Project Guide: AI Workflow Automation
AI automation combines model judgment with deterministic workflow steps, approvals, and recovery paths.
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Project Guide: Evaluation and Monitoring
Production AI projects need repeatable evaluations, operational telemetry, feedback, and change management.
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Module 10 · Episodes 60–64
AI for Software Testing
Use AI to strengthen test design, automation, analysis, and quality workflows.
AI in Software Testing
AI can support test ideation, prioritization, analysis, and maintenance while testers retain responsibility for risk and evidence.
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AI-Assisted Test Case Generation
Models can propose test scenarios from requirements, but coverage and correctness require review against product risks.
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AI with Playwright Test Automation
AI can help draft and maintain Playwright tests when locators, assertions, data, and failure handling remain explicit and reviewable.
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AI for Defect and Failure Analysis
AI can summarize logs, cluster failures, and suggest hypotheses while engineers validate the actual root cause.
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Responsible AI for Testing Teams
Testing AI-enabled products includes quality, fairness, privacy, robustness, explainability, and misuse considerations.
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Module 11 · Episodes 65–69
AWS AI Practitioner Exam Prep
Review exam domains, scenarios, services, responsible AI, and study strategy.
AWS AI Practitioner AIF-C01 Exam Overview
The exam evaluates foundational AI, generative AI, foundation-model applications, responsible AI, and security and governance concepts.
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AIF-C01 Domain-by-Domain Review
A domain review connects each official objective to concepts, business scenarios, AWS services, and responsible-use decisions.
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How to Solve AWS AI Scenario Questions
Scenario questions become easier when you isolate the requirement, constraint, distractors, and best-fit AWS capability.
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AWS AI Practitioner Practice Strategy
Effective practice mixes recall, scenario reasoning, timed sets, explanation review, and targeted revision.
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AWS AI Practitioner Final Study Plan
A final study plan prioritizes the official guide, high-weight domains, service use cases, responsible AI, and calm exam execution.
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