Google Cloud Generative AI Leader Cert Complete Course 2026

Explore artificial intelligence as the simulation of human intelligence in machines that think and learn, from weather forecasts and email autocomplete to personalized recommendations and self-driving cars.

The lecture explains narrow AI, general AI, and super AI with examples like facial recognition, voice assistants, and language translation, including ChatGPT and Google Gemini. It notes that only narrow AI exists today, while strong and super AI remain hypothetical.

Explore how machine learning enables computers to learn from data, train models using datasets, algorithms, and hardware, and apply to tasks like house pricing, product recommendations, and spam detection.

Define and demystify a model as the trained program that takes input and outputs results, independent of hardware, and explain its training, deployment, and use in ai services.

Explore how data and datasets underpin machine learning, revealing that diverse data must be grouped by type to train reliable models.

Learn how labeled data is created by tagging and classifying images and emails, and contrast it with unlabeled data to understand machine learning training.

Distinguish structured data from unstructured data using CSV, tables, and media folders, and learn how data format guides machine learning algorithms.

Explore the three fundamental types of learning: supervised, unsupervised, and reinforcement learning, distinguished by labeled versus unlabeled data, with future coverage of semi-supervised and self-supervised approaches.

Supervised learning uses labeled data to map inputs to outputs, with input and label guiding the model. Distinguish classification and regression, with predefined categories and numerical outputs like house prices.

Explore classification, a supervised learning task that assigns inputs to predefined categories like cat or not cat, trained on millions of labeled examples to predict new inputs.

Explore how regression learning, a form of supervised learning, uses labeled data and features to predict numerical prices, such as house prices, enabling sales, weather, and stock forecasting.

Explore unsupervised learning using unlabeled data to reveal hidden patterns and relationships, enabling clustering, association, and dimension reduction without manual labeling.

Explore clustering, a type of unsupervised learning that groups inputs by a defining feature, enabling customer and image segmentation, label data for supervised learning, and anomaly detection.

Explore association type of unsupervised learning that discovers relationships between input variables to form related-item segments. Apply to real-world uses like e-commerce product associations and medical diagnosis patterns.

Dimensionality reduction, an unsupervised learning method, reduces unnecessary features to cut training cost and noise, using key attributes like bedrooms and garden size.

Explore reinforcement learning where agents learn to maximize rewards and minimize penalties in real time through trial, error, and feedback from the environment.

Understand how data quality drives model performance: good data yields proper results, while bad data risks not meeting expectations; data diversity affects outcomes, and overfitting and underfitting remain common challenges.

Learn how overfitting occurs when a model performs well on training data but fails on real-life data, with resumes biased toward male candidates illustrating data diversity issues.

Recognize underfitting when data is too simple for the model to learn. Demonstrate how size drives price while other features are ignored, and improve with richer data and feature weighting.

Identify data biasness as a misrepresentation of real world data, and ensure diverse, representative training data to improve accuracy and reduce bias from one-sided historical data.

Explore generative AI, a branch of artificial intelligence that understands language and creates data such as text, images, audio, video, and code by learning from data patterns.

Explore how generative ai differs from traditional models, handling unstructured input and output across text, images, audio, and code. Learn core concepts like tokens, embeddings, prompting, and foundation models.

Explore how natural language processing enables machines to understand, interpret, and generate human language, tracing the shift from IVR to gen AI applications.

Explore how artificial neural networks mimic the brain with input, hidden, and output layers; nodes in hidden layers extract features, recognize patterns, and transform inputs for content creation.

Explore how deep learning, with multiple hidden layers, drives natural language processing, image and speech recognition, and enables powerful generative ai through gpu-enabled processing.

Explore foundation models that can process, understand, and generate human-like content, powered by transformer architectures, GPUs, and vast datasets to handle language, images, and code.

Explore transformer-based language models that take text as input and output, trained on billions of parameters, with use cases like translation, text generation, code generation, and chatbots.

Explore diffusion models that generate images from text or image inputs, extend to video, and enable rapid marketing image creation, editing, medical image report summarization, and personalized content generation.

Explore multimodal models that process and generate text, image, audio, and video with a single model; compare with single-modal models and understand training complexity, cost, and use-case trade-offs.

Explore when you can build your own generative ai model, weighing use cases, data and infrastructure needs, costs with cloud providers, and the benefits of private data and fine-tuning.

Explore how fine tuning a foundation model with your private data creates a domain specific model for internal documents and customer care.

Explore how to access generative AI models beyond the user interface by using APIs, SDKs, notebook environments, and IDE plugins for seamless programmatic integration.

Explore at a high level how tokens and embeddings fuel generative ai, why language models generate text word by word, and how temperature and context window relate.

Understand how tokens convert words into numerical values, how tokenization feeds embedding, and how embedding maps tokens to vectors that capture word relationships in a vector space.

Explain how large language models generate the next word by tokenizing text, forming embeddings in vector space, and selecting contextually probable outputs with temperature, top p, and top k.

Explore how input and output tokens drive costs in industrial use, how to cap tokens per request for savings, and how context windows influence token processing in large language models.

Understand how the context window limits a model's memory through input and output tokens; compare 16,384 tokens on ChatGPT 3.5 turbo with 1 million on Gemini 1.5 Pro.

Understand knowledge cutoff as the last training data date that defines a model's knowledge, using GPT-3.5's September 2021 data as an example; explore how cutoff affects accuracy and relevance.

Explore how AI hallucination occurs, illustrated by a 2017 translation error and a 2023 fabricated case, and identify data quality, prompts, and overfitting as sources of mistrust.

Grounding connects generative artificial intelligence with external real-world data systems to validate outputs, reduce hallucination, and boost accuracy, trust, and contextual awareness.

Explore retrieval-augmented generation by connecting user queries to an external data source, creating a modified prompt with relevant information, and using vector database for semantic search to produce contextual outputs.

Master zero-shot prompting, the direct instruction to an LLM without examples. Use it for straightforward answers, like what is the capital of France.

Learn to use one-shot prompting by providing a single example to define the output format, guiding the model to respond with the phrase the answer is followed by the result.

Explore few-shot prompting, an extension of one-shot prompting that uses multiple examples (1–5) to guide the model toward a specific sentiment output, learning from varied cases.

Role play prompting asks the model to act as a specific expert, shaping tone and output quality. It uses roles like historian or legal advisor to tailor responses.

Understand chain-of-thought prompting, which asks a model to explain its step-by-step reasoning behind an answer, and see how the thinking process leads to a solution.

Discover react prompting, where a language model cycles through thought, action, and observation to refine answers. It autonomously calls external systems to gather data and then outputs the result.

Explore meta prompting and prompt engineering using an LLM to craft prompts. Illustrate with a ten-day Italy family itinerary for two teenagers—historical sites, food, daily activities, meals, and budget.

Discover how temperature controls AI output by shaping determinism and creativity, from near zero to higher values, altering token probabilities and the range of possible completions.

Explore advanced generation controls with top k and top p, and how they, together with temperature, shape output randomness by constraining candidate words and cumulative probability.

See how Google positions itself as an AI first company by embedding AI in products and operations, and by offering AI features like autocomplete and translation.

Explore Google's foundation models, including Gemini’s multi-modal capabilities, image-to-image and text-to-image, coda for code, gamma open source, view for text-to-video, and chip for speech-to-text.

Access foundation models like Gemini through the web interface and explore alternative options such as Google AI Studio, Vertex AI Studio, and API integrations for background code.

Explore the Gemini web interface to chat with the model, upload files, and switch between free and pro preview models; learn basic prompt engineering and personal data summarization.

Access Google AI Studio, create a Gemini model API key through a Google Cloud project, and generate ready-made code in multiple languages with billable usage.

Explore Google AI Studio settings to select models, adjust temperature, enable grounding with Google search, manage safety blocks, and tailor output length and token pricing for developers.

Explore Google AI Studio options, including history, load, and streaming interactions, share your screen to validate PowerPoint content, and experiment with Gemini multimodal, Imagen, View, and text to video models.

Explore Vertex AI Studio on Google Cloud, a complete platform for prompts, model selection (Gemini or third-party), data grounding, and training or deploying your own models.

Explore Vertex AI platform, including Model Garden, Pipeline, Prompt Gallery, and Vertex AI Studio, and learn end-to-end ML workflow from data import to training and deployment with zero coding.

Navigate Google Cloud Vertex AI's model garden to access Google and third-party models. Filter by task, view pricing, test in a notebook, and learn to integrate and fine-tune in code.

Learn how Vertex AI AutoML trains models on your data without coding, using image, tabular, or text data for classification, regression, or clustering, deploy and monitor in model registry.

Explore Vertex AI model registry to deploy and manage live models in production, maintain lifecycle and versioning, and upgrade, fine-tune, and retrain with new data.