Course Overview

Get an overview of this course's content, what will be covered, the tools and technologies to be used, and the intended audience.

We'll cover the following

In today’s rapidly evolving generative AIGenAI, short for generative AI, creates new data like text, images, or code by learning patterns from existing information. landscape, learning never stops, and there is no better time than the present to explore what Google’s Gemini has to offer. This course will introduce you to Google Gemini, explain what it brings to the table, and how you can get started with using it. By the end of this course, you will be ready to start using Gemini in your own applications.

Gemini: A multimodal model

Google Gemini belongs to the family of powerful AI models called large language models (LLMs). These are a class of deep learning models specifically designed to generate or create new data. This can include text, images, voice, video, and even code. While traditional LLMs were only focused on generating textual data, Gemini is a multimodal LLM that can work across a range of modalities—it can create original outputs from text, images, video, and voice.

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A multimodal model like Gemini can process and reason about various modalities of data
A multimodal model like Gemini can process and reason about various modalities of data

Let’s ask Gemini some questions about itself.

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# The Gemini 1.5 models are versatile and work with multimodal prompts
model = genai.GenerativeModel(model_name="models/gemini-1.5-flash")
# Your prompt
prompt = "Hey Gemini, introduce yourself!"
# Generate text with a prompt
response = model.generate_content(prompt)
# Print the generated content
print(response.text)

Note: We will explore the code and similar examples in depth in this interactive course.

Why choose Google Gemini?

The field of LLMs is experiencing explosive growth, with advancements in AI technology enabling more sophisticated and capable models. Google’s Gemini, OpenAI’s GPT-4, Anthropic’s Claude, and Meta’s Llama, just to name a few. Each new iteration of these models sets higher benchmarks, continually pushing the boundaries of what’s possible. The cost of using LLMs is also gradually decreasing, making them more accessible to a wider audience. It can be tricky to pick the right model or service to use. Let’s list down a few reasons as to why you might want to choose Gemini.

  • Multimodal capabilities: Multimodal AI models such as Gemini can process different types of inputs, such as text, images, and videos. This allows the model to work with almost any type of input. Furthermore, Gemini can also generate a wide range of outputs.

  • Generous free offerings: Unlike a few other services, Gemini offers its multimodal capabilities to users for free. The free tier API also provides decent rate limitsRate limits determine the number of requests you can make to a service within a certain timeframe., which can be enhanced by upgrading to the paid version.

  • Variety of options: Gemini has several different variants: Ultra, Pro, Flash, and Nano. Ultra provides the maximum power for complex tasks, Pro offers a balance of capability and efficiency for everyday use, Flash provides a cost-efficient yet smart model, and Nano offers a more lightweight option for tasks on devices with lower resources.

Notes: For the informed learner, this course will cover the use of both Google's Gemini AI model and Google’s Gemini chatbot (formally known as Bard). Since the chatbot is based on a model from the Gemini AI family, the learnings from this course will be helpful for both use cases.

What will we cover in this course?

This course makes Google Gemini accessible to everyone. We’ll break down its features in simple terms, guide you through the setup process, and have you create amazing things with Gemini before you know it! This course is divided into four sections. Here’s a brief overview of each.

  • Getting Started: We will see why the Gemini chatbot is one of the most capable chatbots at the moment and how we can use smart prompts to unleash its potential. We’ll get hands-on with the Google AI Python SDK using our API key.

  • Capabilities of Gemini: We will use Python to test out some key use cases with Gemini. We will generate text, code, and captions for images and videos. We’ll also use our newly gained knowledge to create a fun AI-powered application.

  • Gemini on Vertex AI: Google’s Vertex AI is a unified platform for building, deploying, and managing machine learning models. Vertex AI acts as a one-stop shop for accessing, deploying, and interacting with Gemini models. It also offers access to other pretrained models for various tasks, such as image classification and speech recognition.

  • Conclusion: By the end of this course, you will be well-equipped to take on more complex challenges. We will discuss a few avenues that you might want to explore using Gemini.

An overview of the course structure

Target audience

This beginner-level course is designed for a wide audience with or without prior knowledge of deep learning and language models. It can be particularly useful for those who might fall into any of the following categories:

  • Individuals with a foundational understanding of programming, particularly in Python, who are looking to extend their skills into the domain of GenAI.

  • Those who have basic exposure to machine learning concepts and are interested in exploring newer models like Google Gemini.

  • Learners who have used models such as ChatGPT and explored some of its capabilities and would like to get hands-on with Gemini.

  • Data scientists and analysts who want to leverage generative models to enhance data insights and predictive capabilities.

  • Software developers looking to integrate Gemini-powered AI functionalities into their applications.

Prerequisites

To get the most out of this course, it is recommended that you are familiar with Python and have a basic understanding of APIs. A grasp on prompting and how generative AI works would definitely be helpful. However, as with any good course, it is self-contained and will provide the necessary context for the topics being covered.