AI, ML and Generative AI - for Managers and Business Leaders

Explain how artificial intelligence, from early programs to Deep Blue's 1997 win, simulates human thinking in machines programmed to learn, with weather forecasts and personalized recommendations.

Explore the three AI types by capability—narrow (weak) AI, general (strong) AI, and super AI—along with examples like facial recognition and language translation.

Learn how machine learning, a subset of artificial intelligence, enables computers to learn from data without explicit programming by training models on large datasets, recognizing patterns and making predictions.

Understand how a model emerges from data, algorithm, and training, backed by hardware, then deploys as a software-like program that processes input to deliver an output.

Explore the role of data and datasets in machine learning, understand why a massive amount of data and good data matter, and learn how to group similar data for training.

Differentiate labeled data from unlabeled data by tagging and classifying objects, with examples like an apple image and spam versus non-spam emails.

Differentiate structured data, such as spreadsheets and database tables, from unstructured data; explain why labeled and unlabeled data and data format matter for machine learning input.

Acquire core understanding of the three fundamental machine learning types—supervised, unsupervised, and reinforcement learning—with notes on semi-supervised and self-supervised approaches.

Understand supervised learning through labeled data, where input and output align as objects and labels. Distinguish classification and regression with examples like spam filters, dog images, and house price prediction.

Explore classification learning, a supervised learning approach that assigns inputs to predefined categories, such as cat or not cat. Apply to spam detection, sentiment, and document or quality control tasks.

Learn regression learning in supervised learning by predicting numerical outputs, such as house prices, from features like size, bedrooms, and location, and contrast with classification.

Understand unsupervised learning by using on-label data to uncover hidden patterns and relationships without labeled examples. Explore clustering, association, and dimensity reduction to organize data and reveal structure.

Explore clustering as a key unsupervised learning method that groups inputs by a defining feature, enabling customer segmentation, image segmentation, labeling data for supervised learning, and anomaly detection.

Explore association type unsupervised learning, which finds relationships among input variables to form related item segments, enabling product recommendations and medical risk insights.

Learn how dimensionality reduction, a type of unsupervised learning, trims away unnecessary features to reduce randomness, noise, and cost in data for applications like MRI, financial data, and customer segmentation.

Learn how reinforcement learning uses rewards and penalties to train agents in real time, maximizing rewards while avoiding dead ends through a continuous feedback loop.

Discover how data quality drives machine learning performance, why diverse, accurate datasets matter, and how overfitting and underfitting arise during model training.

Discover how overfitting makes a model excel on training data but falter in real life, driven by biased data and evaluation on a subset, and ways to address it.

Underfitting happens when data is too simple for the model to learn relationships, causing reliance on a single feature like size; adjust features and hyperparameters to capture complexity.

Data biasness occurs when training data fails to represent real-world diversity, leading to skewed predictions. Ensure wide, diverse, and accurate data to improve future results and counter one-sided history.

Explore generative AI, a form of artificial intelligence that understands and creates new data from existing patterns. Learn how it generates text, images, audio, video, and code for everyday use.

Discover how generative AI handles unstructured input and generates human-like content across text, images, audio, and code, while exploring natural language processing, neural networks, tokens, embeddings, and prompting.

Explore natural language processing, a branch of AI that enables machines to understand, interpret, and generate human language, moving from scripted IVR to flexible gen-AI content generation.

Understand artificial neural networks and how input, hidden, and output layers use nodes to extract features, enabling better content creation and generative AI models.

Discover how deep learning drives natural language processing, image and speech tasks, using multiple hidden layers of artificial neural networks and GPU-enabled processing.

Compare cnn for image tasks and rnn for text and speech, including next-word prediction; explain how transformer enables parallel training with gpus, accelerating deep learning and reshaping generative ai.

Explore foundation models that process, understand, and generate human-like content using transformer architecture, GPUs, and vast datasets, and contrast them with traditional AI designed for single tasks.

Explore large language models (LLMs) that input text and output natural language, built on transformer architecture, trained on billions of parameters, and capable of generating articles, poems, translations, or code.

Explore diffusion models that convert text or images into new images, enabling quick marketing visuals, product image editing, and even video generation.

Multimodal models process text, audio, image, and video inputs to generate diverse outputs, offering greater capability but requiring more training, data, and cost.

Assess whether you can build your own AI model by evaluating data, infrastructure, private data, and cloud options for training a traditional or generative model, and plan fine-tuning.

Learn how to access a model beyond the user interface, using API, SDK, notebooks, and IDE plugins for programmatic integration, including translation APIs.

Learn the fundamental concepts of token and embedding that power generative AI. See how LLMs generate text word by word without true meaning, using tokens to predict next words.

Understand how tokens arise from words or parts via tokenization, then become vectors through embedding in a vector space that encodes word relationships learned during training.

learn how fine-tuning adapts a foundation model to your private data and internal documents, enabling bots in a call center to understand company-specific language and questions.

Describe how LLMs generate the next word by tokenizing input, embedding into vectors, using context and probability scores to choose, with randomness controlled by temperature and top-k/top-p.

Tokens drive costs in llm usage, since input and output tokens count toward charges, with limits to save costs, and enterprise models ChatGPT and Gemini may process tokens.

Learn how the context window limits an LLM's memory by total input and output tokens, and compare model token capacities to choose the right one for tasks like summarization.

Learn how a model's knowledge cutoff depends on training data, using GPT-3.5 with data up to September 2021 as a four-year gap, causing accuracy and relevance issues and hallucination.

Explain hallucination in AI models with examples, showing how data quality, training design, prompts, and overfitting produce plausible yet false content and erode trust in AI.

Grounding connects a language model to an external real-world data system to produce accurate, contextually relevant outputs from the latest data and reduce hallucination.

Learn how retrieval augmented generation uses external data sources from a vector database and semantic search to create modified prompts that deliver contextually relevant, latest AI outputs with reduced hallucination.

Learn how prompt engineering guides large language models through crafting effective input, zero-shot and one-shot prompting, to produce accurate, relevant outputs with controlled style and increased trust.

Explore zero-shot prompting, the most common prompting technique, defined as direct instruction to a model with no examples, used for straightforward answers such as asking for the capital of France.

Learn one-shot prompting by providing a single example and a predefined format to guide model responses, using the answer is phrasing to produce consistently formatted outputs.

Learn qshort prompting, an extension of one-shot prompting that uses multiple examples to guide the model’s output formats. It demonstrates how sentiment outputs—positive, negative, or neutral—are derived from several examples.

Explore role play prompting by asking the model to act as a role-specific expert, such as a historian or chef, to shape tone, format, and output quality.

Understand chain-of-thought prompting and learn to prompt a model to think step-by-step, revealing how an LLM derives outputs with a clear, explainable process.

Explore react prompting, an advanced prompting engineering approach where the LLM thinks, acts, and observes in cycles, connecting to external systems to gather data before delivering results.

Master meta prompting by using an llm to generate prompts, enabling prompt engineering where the model acts as a prompt designer for complex tasks like a 10-day italy itinerary.

Understand how temperature, a numerical value on a 0-1 (or 0-2) scale, governs randomness and creativity in AI outputs, with low values being deterministic and high values more diverse.

Learn how top k and top p, combined with temperature, control randomness and output quality in LLMs, and how cumulative probabilities guide word selection within prompts and prompt engineering.