Learn to use Google's Gemini API for building AI-powered applications. Plus you'll put your skills into action by building three projects using the Gemini API.
Building AI Apps with the Gemini API
3h 43m 41s
English
Paid
Take your first step into the world of AI application development by diving deep into a fundamental technology: Google's Gemini API. You'll learn everything there is about utilizing the API to power your AI applications with Google's leading Large Language Models.
About the Author: Zero To Mastery
Zero To Mastery (ZTM) is a Toronto-based online coding academy founded by Andrei Neagoie, originally a senior developer at large Canadian tech firms before turning to teaching full-time. The academy's signature is the cohort-based bootcamp track combined with a deep self-paced course library, all aimed at career-changers and self-taught developers preparing to land software-engineering roles at top companies.
The instructor roster has grown well beyond Andrei to include other senior practitioners: Daniel Bourke (machine learning), Aleksa Tešić (DevOps), Jacinto Wong, and others. Courses cover the full software-engineering career path: web development with React and Next.js, Python, machine learning and deep learning, DevOps and cloud, system design, mobile, and the algorithm / data-structure interview prep that gates engineering jobs.
The CourseFlix listing under this source carries over 120 ZTM courses spanning that full range. Material is paid; ZTM itself runs on a monthly / annual membership model. The teaching style favours long-form, project-based courses where students build complete portfolio-quality applications rather than disconnected feature tutorials.
Watch Online 39 lessons
0:00
/ #1: Introduction
All Course Lessons (39)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 1 | Introduction Demo | 03:26 | |
| 2 | What We're Using | 00:36 | |
| 3 | Jupyter Notebook | 14:11 | |
| 4 | Google Colab | 08:08 | |
| 5 | Getting a Gemini API Key | 04:21 | |
| 6 | Installing the Python SDK for Gemini API and Authenticating to Gemini | 09:52 | |
| 7 | Gemini Multimodal Models: Nano, Pro, and Ultra | 05:15 | |
| 8 | Google AI Studio: Freeform Prompts With Gemini Pro Vision | 06:12 | |
| 9 | Google AI Studio: Using Variables and Parameters in the Prompt | 03:05 | |
| 10 | Generating Text From Text Inputs: Gemini Pro | 04:23 | |
| 11 | Streaming Model Responses | 03:36 | |
| 12 | Generating Text From Image and Text Inputs: Gemini Pro Vision | 05:13 | |
| 13 | Gemini API Generation Parameters: Controlling How the Model Generates Responses | 06:12 | |
| 14 | Gemini API Generation Parameters Explained | 10:14 | |
| 15 | Building Chat Conversations | 07:54 | |
| 16 | Project: Building a Conversational Agent Using Gemini Pro | 07:19 | |
| 17 | Introduction to Gemini 1.5 Pro | 04:11 | |
| 18 | System Instructions | 05:43 | |
| 19 | The File API Prompting with Media Files | 06:09 | |
| 20 | Tokens | 06:42 | |
| 21 | Prompting with Audio | 04:21 | |
| 22 | Project Requirements | 05:54 | |
| 23 | Building the Application | 05:23 | |
| 24 | Testing the Application | 01:49 | |
| 25 | Streamlit: Transform Your Jupyter Notebooks into Interactive Web Apps | 02:49 | |
| 26 | Creating the Web App Layout With Streamlit | 11:20 | |
| 27 | Saving and Displaying the History Using the Streamlit Session State | 05:20 | |
| 28 | Project Introduction | 00:57 | |
| 29 | Getting Images Using a Generator | 06:18 | |
| 30 | Renaming Images Using Gemini Pro Vision | 09:35 | |
| 31 | Intro to Prompt Engineering the Gemini API | 03:13 | |
| 32 | Tactic #1 - Position Instructions Clearly With Delimiters | 05:02 | |
| 33 | Tactic #2 - Provide Detailed Instructions for the Context, Outcome, or Length | 06:11 | |
| 34 | Tactic #3 - Specify the Response Format | 06:14 | |
| 35 | Tactic #4 - Few-Shot Prompting | 06:56 | |
| 36 | Tactic #5 - Specify the Steps Required to Complete a Task | 06:29 | |
| 37 | Tactic #6 - Give Models Time to "Think" | 04:34 | |
| 38 | Other Tactics for Better Prompting and Avoiding Hallucinations | 06:21 | |
| 39 | Prompt Engineering Summary | 02:13 |
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Frequently asked questions
What prerequisites are needed before taking this course?
Before enrolling in the course, students should have a basic understanding of Python programming. Familiarity with Jupyter Notebook and Google Colab is beneficial, as these tools are used extensively in the lessons. Prior experience with API usage or AI models is not required but can be helpful.
What projects will I build during the course?
The course includes three hands-on projects where students use the Gemini API. One of the projects involves building a conversational agent using Gemini Pro. Another project focuses on transforming Jupyter Notebooks into interactive web apps using Streamlit. These projects allow students to apply their learning in practical scenarios, utilizing the Gemini API's capabilities.
Who is the target audience for this course?
This course is designed for developers and data scientists interested in building AI-powered applications using Google's Gemini API. It's suitable for those who want to learn how to implement AI models in their projects and for individuals seeking to enhance their skills in AI application development.
What specific tools or platforms will I learn to use?
Students will learn to use several tools and platforms, including Jupyter Notebook, Google Colab, and Streamlit. They will also gain experience with Google AI Studio for creating freeform prompts and using variables and parameters in the Gemini API. The course provides a comprehensive guide to obtaining and authenticating a Gemini API Key and using the Python SDK.
What topics are not covered in this course?
The course does not cover traditional machine learning model training or data preprocessing techniques. It focuses specifically on using the Gemini API for AI application development and does not delve into the underlying machine learning algorithms or data science methodologies that power the API.
How much time should I expect to commit to this course?
The course consists of 39 lessons. While the total runtime is not specified, students should allocate time for both the video lessons and practical exercises associated with each project. The time commitment will vary based on individual pace and prior experience with the tools and concepts taught.
How can the skills learned in this course benefit my career?
Skills acquired in this course can enhance a career in AI development by providing practical experience with the Gemini API. Understanding how to build AI-powered applications and create interactive web apps can be valuable in roles such as AI developer, data scientist, or software engineer. The knowledge of prompt engineering tactics, such as few-shot prompting and specifying response formats, can also be applicable in other AI-related projects.