Master Google'S Gemini Pro Vision Api With Python

Harness the Power of Google's Gemini LLM: Build Cutting-edge LLM Apps with Prompt Engineering Expertise. Project-based

What you'll learn

Gain a deep understanding of the Google Gemini API with Python

Install Python SDK for Gemini API and authenticate to Gemini

Create freeform prompts with Gemini Pro Vision in Google AI Studio

Use variables and parameters in Gemini prompts in Google AI Studio

Generate text from text inputs using Gemini Pro API and Python

Stream model responses

Generate text from image and text inputs using Gemini Pro Vision API and Python

Control how the model generates responses using Gemini API generation parameters: temperature, top_k, top_p, stop sequences and more

Build custom chat conversational agents

Master prompt-engineering techniques for LLMs

You'll learn how to create web interfaces (front-ends) for you LLM apps using Streamlit

Streamlit: main concepts, widgets, session state, callbacks

Learn how to use Jupyter AI efficiently

Requirements

Basic Python programming experience is required.

The Gemini API is only available in certain regions worldwide. Before enrolling, please verify that Gemini supports your region.

Description

Welcome to the Gemini Era. Embrace the Gemini Pro Vision API with Python and Become a Pioneer in Multimodal AIPrepare to master Google's Gemini Pro Vision API with Python and unleash the power of Google's most capable AI family into your applications.By the end of this journey, you'll master the Gemini Pro Vision API and become a pro in LLM prompt engineering, equipped to create groundbreaking and intelligent Python applications using the Gemini API.Get ready to join the forefront of multimodal AI innovation as we constantly update this course with the latest advancements, equipping you with the skills to thrive in the future.This course on Google's Gemini Pro Vision API with Python covers everything you need to know about the Gemini family of models and about effective prompt engineering for LLMs.Become a pioneer shaping the technological landscape and reap the benefits of being an early adopter.In today's world, AI is the key to unlock unprecedented productivity.Embrace the Gemini Pro Vision API with Python, Google AI Studio, and advanced prompting tactics to stay ahead of the curve.In this course, you'll learn by doing, with practical projects that will guide you in applying what you learn.You'll also discover the best practices and tips for effective prompting for LLMs, such as using few examples, finding relevant context information, and exploring different prompt engineering techniques.By the end of this course, you'll be able to:Learn how to use Google's Gemini Pro [Vision] API with Python, the most advanced and versatile AI tool from GoogleCreate freeform and dynamic prompts with Gemini Pro Vision in Google AI StudioGenerate text from text inputs using Gemini Pro API and PythonStream model responsesGenerate text from image and text inputs using Gemini Pro Vision API and PythonControl how the model generates responses using Gemini API generation parameters: temperature, top_k, top_p, stop sequences and moreBuild custom chat conversational agentsMaster the art of prompt engineering for LLMs and create effective and natural language queries for any taskYou'll learn how to create web interfaces (front-ends) for your LLM apps using StreamlitLearn how to use Jupyter AI efficientlyThis course is suitable for anyone who wants to learn how to use the Gemini Pro Vision API and Google AI Studio, and how to leverage the power of multimodal AI for various applications.If you are ready to take your skills to the next level and master one of the most cutting-edge technologies in AI, enroll in this course today and start your journey to multimodal AI mastery!

Overview

Section 1: Getting Started

Lecture 1 How to Get the Most Out of This Course

Lecture 2 Setting Up the Environment: Jupyter Notebook

Lecture 3 Setting Up the Environment: Google Colab

Lecture 4 Course Resources

Section 2: Deep Dive into Google Gemini Pro API

Lecture 5 Getting a Gemini API Key

Lecture 6 Installing the Python SDK for Gemini Pro API and Authenticating to Gemini

Lecture 7 Gemini Multimodal Models: Nano, Pro and Ultra

Lecture 8 Google AI Studio: Freeform Prompts With Gemini Pro Vision

Lecture 9 Google AI Studio: Using Variables and Parameters in the Prompt

Lecture 10 Generating Text From Text Inputs: Gemini Pro

Lecture 11 Streaming Model Responses

Lecture 12 Generating Text From Image and Text Inputs: Gemini Pro Vision

Lecture 13 Gemini API Generation Parameters: Controlling How the Model Generates Responses

Lecture 14 Gemini API Generation Parameters Explained

Lecture 15 Building Chat Conversation

Lecture 16 Project: Building a Conversational Agent Using Gemini Pro

Section 3: [Appendix] Jupyter AI

Lecture 17 Jupyter AI

Lecture 18 Introduction to Jupyter AI and Other Coding Companions

Lecture 19 Installing Jupyter AI

Lecture 20 Using Jupyter AI in JupyterLab

Lecture 21 Setting Up Jupyter AI in Jupyter Notebook

Lecture 22 Using Jupyter AI in Jupyter Notebook

Lecture 23 Using Interpolation for More Advanced Use Cases

Lecture 24 Using Jupyter AI with Other Providers and Models

Section 4: Project: Talking With an Image

Lecture 25 Project Requirements

Lecture 26 Building the Application

Lecture 27 Testing the Application

Lecture 28 Streamlit: Transform Your Jupyter Notebooks into Interactive Web Apps

Lecture 29 Creating the Web App Layout With Streamlit

Lecture 30 Saving and Displaying the History Using the Streamlit Session State

Section 5: Prompt Engineering for Gemini API

Lecture 31 Intro to Prompt Engineering

Lecture 32 Tactic #1 - Position Instructions Clearly With Delimiters

Lecture 33 Tactic #2 - Provide Detailed Instructions for the Context, Outcome, or Length

Lecture 34 Tactic #3 - Specify the Response Format

Lecture 35 Tactic #4 - Few-Shot Prompting

Lecture 36 Tactic #5 - Specify the Steps Required to Complete a Task

Lecture 37 Tactic #6 - Give Models Time to "Think"

Lecture 38 Other Tactics for Better Prompting and Avoiding Hallucinations

Lecture 39 Prompt Engineering Summary

Section 6: [Appendix] Python Programming

Lecture 40 README

Lecture 41 While and continue Statements

Lecture 42 While and break Statements

Lecture 43 List Slicing and Iteration

Lecture 44 List Comprehension - Part 1

Lecture 45 List Comprehension - Part 2

Lecture 46 Working with Dictionaries

Lecture 47 JSON Data Serialization

Lecture 48 JSON Data Deserialization

Lecture 49 Assignment: JSON and Requests/REST API

Lecture 50 Assignment Answer: JSON and Requests/REST API

Section 7: [Appendix] Building Front-ends for AI Apps With Streamlit

Lecture 51 Introduction to Streamlit

Lecture 52 Streamlit Main Concepts

Lecture 53 Displaying Data on the Screen: st.write() and Magic

Lecture 54 Widgets, Part 1: text_input, number_input, button

Lecture 55 Widgets, Part 2: checkbox, radio, select

Lecture 56 Widgets, Part 3: slider, file_uploader, camera_input, image

Lecture 57 Layout: Sidebar

Lecture 58 Layout: Columns

Lecture 59 Layout: Expander

Lecture 60 Displaying a Progress Bar

Lecture 61 Session State

Lecture 62 Callbacks

Section 8: BONUS SECTION

Lecture 63 Congratulations

Lecture 64 BONUS: THANK YOU GIFT!

Python programmers who want to integrate the Google's Gemini models into their applications.,Programmers looking to develop AI apps with cutting-edge AI (Google's Gemini) for free.,Any technical person interested in the most disruptive technology of this decade.