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.

Explore how mNIST dataset pixel values and binary encoding enable machine learning to recognize handwritten digits, and examine data collection methods from sensors, video, audio, texts, social media.

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.

Explore how metadata describes data by detailing asset type, author, creation date, file size, and usage, helping manage unstructured and large datasets.

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 the three core machine learning types—supervised, unsupervised, and reinforcement learning—plus their intuition and real-world uses, setting the stage for deep learning with neural networks.

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.

Explore how computer vision uses machine learning and neural networks to derive meaningful insights from images and videos. Learn CNNs, transformers, GANs, U-Net, EfficientNet, and their roles in spatial hierarchies.

Explore how traditional machine learning drives real-world business value through fraud detection, mortgage repayment risk, insurance pricing, demand forecasting, pricing optimization, and product recommendations.

Explore generative AI, the technology behind ChatGPT and DALL-E, including large language models, diffusion models, GANs, neural radiance fields, and hybrid approaches, and learn their impact on the corporate world.

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

Trace natural language processing from rule-based systems to statistical NLP in the 1990s, and learn context-driven noun versus verb disambiguation, plus advances via vector embeddings, machine learning, and deep learning.

Explore how statistical analysis boosted NLP, using high-dimensional vector embeddings for semantic similarity to power neural networks, transformers, and large language models like GPT and Gemini.

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.

Trace the evolution of language models from n-grams to transformers, highlighting unigram, bigram, trigram limits, rnn and lstm advances, and the attention mechanism shaping llms like ChatGPT.

Explore the phases of building large language models from model design and dataset engineering to pre-training, post-training, fine tuning, and final evaluation, with attention to overlaps, data quality, and ethics.

Clarify prompt engineering, rag, and fine tuning as three distinct methods that enhance AI system response accuracy by targeting different aspects of AI functionality, illustrated with an interview simulator example.

Foundation models shift from single-task systems to multimodal capabilities, allowing large language models to generate text, code, images, and video, with fine-tuning and prompt engineering expanding applications.

Choose between buying and making foundation models, noting the cost and scale of private models, and leverage OpenAI GPT access with prompt engineering and fine tuning.

Spot and reduce hallucinations and inconsistencies in ai outputs by fact-checking, applying prompt engineering to require certainty, and understanding model limitations and training data influences.

Balance data set quality and model size to boost AI performance while managing training costs. Budget beforehand by allocating resources to data acquisition and computing power, considering trade-offs.

Explore latency in customer facing ai apps and how autoregressive models slow outputs due to sequential word generation. Learn parallel computing and model size optimization to speed responses.

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.

Explore how APIs connect clients and servers to enable data exchange, showcase request and response flows, and learn how to integrate OpenAI's API to use foundation models in applications.

Explore how vector databases store and index vector embeddings to enable fast similarity search and meaning-based retrieval, enabling long-term memory for LLMs with examples like Pinecone, Milvus, Chroma, and Elasticsearch.

Accelerate innovation with open source models through community collaboration and lower hardware requirements, enabling domain-specific fine-tuning, while closed systems offer easier integration, optimized APIs, and stronger data security.

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.

An AI engineer builds apps by integrating foundation models into products such as websites, mobile apps, and IoT devices, optimizing with fine tuning and prompt engineering.

Explore AI ethics as moral principles that maximize benefits for the common good and minimize harm, clarifying human accountability in AI development, data privacy, misinformation, and job displacement within regulation.

Explore how the future of AI hinges on electricity, data, and computing power as it accelerates growth, reshapes energy demand, and sparks regulatory and ethical debates.