Intro to AI: A Beginner's Guide to Artificial Intelligence

Compare structured data, organized in rows and columns, with unstructured data such as text, images, and videos. AI turns unstructured data into valuable insights, opening opportunities for businesses.

Compare labeled and unlabeled data for AI modeling, showing that labeled data yields high quality sets and reliable models, but unlabeled data enables learning from unstructured data.

Learn how machine learning uses abundant training data and trial-and-error learning to build predictive models, guided by data scientists; see a real estate app predicting home prices from past transactions.

Explore deep learning as a multi-layer neural network approach to pattern recognition, from input to hidden to output layers, using weights and biases to learn from data.

Explore how robotics blends mechanical design, electronics, and AI to create intelligent machines, from autonomous systems to medical and service robots, and examine multi-model architectures for perception, navigation, and interaction.

OpenAI released ChatGPT 3.5, a dialogue-focused model that produces text, driving rapid user growth as the fastest growing consumer application in history.

Explain how language models predict the next word using context, contrast masked and autoregressive models, and why large language models trained on vast data enable multilingual and generative, scalable capabilities.

Compare supervised learning's labeling costs and scalability limits with unsupervised and self-supervised approaches. Understand how self-supervised learning and transformers enable large language models like ChatGPT.

Explains the challenge of future language models running out of training data as public sources dry up, AI-generated content rises, and data-scraping bans increase costs, prompting licensing deals.

Explore why no-code tools help start with AI, but mastering Python is essential to use AI APIs, tailor behavior, and build reliable AI apps with numpy, pandas, and matplotlib.

Hugging Face drives open source ai by sharing pre-trained models, datasets, and applications, earning the GitHub of machine learning. The Transformers Python library enables easy access, fine-tuning, and deploying demos.

Explore Lang Chain, an open source orchestration environment in Python and JavaScript. Use its modular components to integrate any foundation model and external data sources, reducing code complexity.

Explore how to evaluate AI-powered interview tools using the AI as a judge approach, balancing prompt engineering, open-ended and coding questions, and the need for human oversight.

Explore the AI strategist role, its alignment with business strategy, and how to select use cases, deploy models, and evangelize AI across the organization.

Discover how AI developers build foundation models, choose architectures, and prepare training data for pre-training. Explore the foundation-model landscape, citing OpenAI's GPT, Google's Gemini, and Bert Meta's Llama as examples.