AI Ecosystem for the Absolute Beginners - Hands-On

Discover how artificial intelligence transforms industries, from real-time translation to autonomous vehicles, and learn AI's evolution and key business benefits like efficiency, data-driven decisions, and risk management.

Explore the AI ecosystem's four layers—hardware, programming languages and platforms, models, and applications—highlighting chips, tools like TensorFlow and Python, and real-world deployments.

Explore real-world AI use cases across health care, finance, education, automotive, customer service, social media, and e-commerce, including radiomics, algorithmic trading, personalized learning, ADAS, chatbots, and recommendations.

Explore the five phases of the AI model lifecycle—from data preparation and model building to training, deployment, and management—emphasizing quality, optimization, scalability, continuous improvement, and risk management.

Learn AWS fundamentals for absolute beginners, covering regions, availability zones, local zones, and IaaS, PaaS, and SaaS, with a hands-on EC2 demo and security basics.

Explore Amazon ai services landscape and see how machine learning, deep learning, and generative ai align with Amazon Recognition, Amazon Transcribe, Amazon Comprehend, Amazon Lex, Amazon Polly, and Amazon Bedrock.

Discover how AWS Rekognition analyzes images and videos with facial analysis, object and text detection. Explore practical use cases in retail and marketing, including real-time detection and demographics insights.

Explore label detection and facial analysis with Amazon Rekognition through a hands-on demo that builds a capstone project using S3, Lambda, and Rekognition, including uploading images and reviewing JSON responses.

Explore machine learning basics as a subset of AI, and compare supervised, unsupervised, and reinforcement learning, with practical examples and data-driven model evaluation and preprocessing techniques.

Develop a high-quality dataset through data cleaning, integration, reduction, and aggregation, then train and evaluate machine learning models with attention to hyperparameters, avoiding overfitting and underfitting during validation and testing.

Explore real world uses of machine learning across customer feedback analysis, predictive maintenance, image recognition for quality assurance, demand forecasting, transportation, and finance, driving efficiency across industries.

Explore Amazon Forecast, a managed time series forecasting service with AutoML and built-in algorithms. Import data, train predictors, and generate forecasts for retail demand and supply chain planning.

Explore how to use Amazon Forecast for time series forecasting by uploading data to S3, training a predictor, generating forecasts, and exporting results back to S3.

Explore deep learning, a brain-inspired subset of AI and machine learning, and see artificial neural networks—the input, hidden, and output layers—learn from vast data to make predictions.

Explore real world use cases of deep learning and neural networks, from object detection and facial recognition to self-driving cars, translation, medical imaging, and personalized medicine.

Explore Amazon Comprehend, a cloud-based natural language processing service that uses pre-trained deep learning models to extract entities, key phrases, sentiment, topics, and language from text.

Learn how to use Amazon Comprehend for sentiment analysis with a hands-on demo in AWS, including setting up an IAM user, Boto3, and Jupyter notebook to analyze reviews.

Explore generative AI, its foundation models, and how it enables efficiency, scalability, creativity, innovation, and tailored content generation for diverse tasks.

Explore core generative ai algorithms and models, including generative adversarial networks, variational autoencoders, foundation models, and large language models, and workflow from data collection to evaluation with retrieval augmented generation.

Explore how generative AI spans image generation and manipulation, NLP, healthcare, art, content creation, and virtual simulations, with future trends like interdisciplinary collaboration, ethical AI, continuous learning, and open access.

Explore the GenAI toolset and future prospects, from chat and code assistants to image and text generators, including ChatGPT, Copilot, DALL-E 2, Adobe Sensei, Alphacode, Vertex AI, and Bedrock.

Explore Amazon Bedrock service and its foundation models via a unified API, then fine-tune and deploy chatbots, content, and automated reports with generative ai.

Demonstrate Amazon Bedrock's text, chat, and image generation via playgrounds, using Titan models and managing model access with IAM, while exploring base and custom models and watermark detection.

Explore how prompts drive generative AI and master prompt engineering with clarity, specificity, conciseness, and context. Preview tools like ChatGPT, Gemini, Copilot, and Sora to see practical prompt-driven interactions.

Learn practical prompt engineering with ChatGPT, using zero-shot and few-shot contexts to shape outputs. Explore use cases from summarizing documents and generating code to travel planning and poetry.

Explore AI risks from fake content and misinformation to privacy, security, accountability, bias, and copyright ownership, and apply fairness, transparency, and data protection in practice.

Build a Flask-based capstone app using AWS Bedrock to enable text-to-text and text-to-image generation, powered by the Anthropic model and stable diffusion x1 v1.

Build an AI chatbot using Python libraries (TensorFlow, Tflearn, NLTK, NumPy) and Lancaster stemmer, train on a JSON intents dataset with bag-of-words in a Jupyter notebook.