Learn Google Gemini and Anthropic Claude API with Python

Learn Googles Gemini and Anthropics Claude API with Python, available at $54.99, has an average rating of 4.58, with 83 lectures, 6 quizzes, based on 81 reviews, and has 1182 subscribers.

You will learn about Gain a deep understanding of the Googles Gemini and Anthropics Claude 3 API with Python Install Python SDK for Gemini and Claude 3 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 from Gemini and Claude Generate text from image and text inputs using Gemini Pro Vision and Claude 3 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 Youll 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 This course is ideal for individuals who are Python programmers who want to integrate the Googles Gemini and Anthropics Claude models into their applications. or Programmers looking to develop AI apps with cutting-edge AI (Googles Gemini and Anthropics Claude 3) for free. or Any technical person interested in the most disruptive technology of this decade. It is particularly useful for Python programmers who want to integrate the Googles Gemini and Anthropics Claude models into their applications. or Programmers looking to develop AI apps with cutting-edge AI (Googles Gemini and Anthropics Claude 3) for free. or Any technical person interested in the most disruptive technology of this decade.

Enroll now: Learn Googles Gemini and Anthropics Claude API with Python

Summary

Title: Learn Googles Gemini and Anthropics Claude API with Python

Price: $54.99

Average Rating: 4.58

Number of Lectures: 83

Number of Quizzes: 6

Number of Published Lectures: 83

Number of Published Quizzes: 6

Number of Curriculum Items: 89

Number of Published Curriculum Objects: 89

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Gain a deep understanding of the Googles Gemini and Anthropics Claude 3 API with Python

Install Python SDK for Gemini and Claude 3 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 from Gemini and Claude

Generate text from image and text inputs using Gemini Pro Vision and Claude 3 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

Youll 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

Who Should Attend

Python programmers who want to integrate the Googles Gemini and Anthropics Claude models into their applications.

Programmers looking to develop AI apps with cutting-edge AI (Googles Gemini and Anthropics Claude 3) for free.

Any technical person interested in the most disruptive technology of this decade.

Target Audiences

Python programmers who want to integrate the Googles Gemini and Anthropics Claude models into their applications.

Programmers looking to develop AI apps with cutting-edge AI (Googles Gemini and Anthropics Claude 3) for free.

Any technical person interested in the most disruptive technology of this decade.

In this course, you’ll learn about both Google’s Gemini and Anthropic’s Claude 3 API with Python.

**Fully updated for Gemini 1.5 Pro API!**

Welcome to the Gemini Era.Embrace the Gemini Pro Vision API with Python and Become a Pioneer in Multimodal AI

Prepare 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 API (1.5 included) 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.

You’ll also learn how to use the Python API for the Anthropic’s Claude 3 family of models: Opus, Sonnet and Haiku.

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 Google

Create freeform and dynamic prompts with Gemini Pro Vision in Google AI Studio

Unlock the Power of Gemini 1.5 Pro API

Use the File API for prompting with media files (audio, video and more)

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 the art of prompt engineering for LLMs and create effective and natural language queries for any task

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

Learn how to use Anthropic’s Claude 3 API with Python: API setup, generating text, streaming, Claude 3 vision capabilities, and more

Learn how to use Jupyter AI efficiently

This course is suitable for anyone who wants to learn how to use the Gemini Pro Vision API,  Google AI Studio, Claude 3 API, 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!

Course Curriculum

Chapter 1: Getting Started

Lecture 1: How to Get the Most Out of This Course

Lecture 2: Python IDEs For This Course

Lecture 3: Setting Up the Environment: Jupyter Notebook

Lecture 4: Setting Up the Environment: Google Colab

Lecture 5: Join Our Online Community!

Lecture 6: Course Resources

Chapter 2: Deep Dive into Google Gemini Pro API

Lecture 1: Getting a Gemini API Key

Lecture 2: Installing the Python SDK for Gemini Pro API and Authenticating to Gemini

Lecture 3: Gemini Multimodal Models: Nano, Pro and Ultra

Lecture 4: Google AI Studio: Freeform Prompts With Gemini Pro Vision

Lecture 5: Google AI Studio: Using Variables and Parameters in the Prompt

Lecture 6: Generating Text From Text Inputs: Gemini Pro

Lecture 7: Streaming Model Responses

Lecture 8: Generating Text From Image and Text Inputs: Gemini Pro Vision

Lecture 9: Gemini API Generation Parameters: Controlling How the Model Generates Responses

Lecture 10: Gemini API Generation Parameters Explained

Lecture 11: Building a Chat Conversation

Lecture 12: Project: Building a Conversational Agent Using Gemini Pro

Chapter 3: Unlocking the Power of Gemini 1.5 Pro API

Lecture 1: Introduction to Gemini 1.5 Pro

Lecture 2: System Instructions

Lecture 3: The File API: Prompting with Media Files

Lecture 4: Tokens in Gemini API

Lecture 5: The File API: Prompting with Audio

Chapter 4: Jupyter AI

Lecture 1: Jupyter AI

Lecture 2: Python Version

Lecture 3: Introduction to Jupyter AI and Other Coding Companions

Lecture 4: Installing Jupyter AI

Lecture 5: Using Jupyter AI in JupyterLab

Lecture 6: Setting Up Jupyter AI in Jupyter Notebook

Lecture 7: Using Jupyter AI in Jupyter Notebook

Lecture 8: Using Interpolation for More Advanced Use Cases

Lecture 9: Using Jupyter AI with Other Providers and Models

Chapter 5: Project: Talking With an Image

Lecture 1: Project Requirements

Lecture 2: Building the Application

Lecture 3: Testing the Application

Lecture 4: Streamlit: Transform Your Jupyter Notebooks into Interactive Web Apps

Lecture 5: Creating the Web App Layout With Streamlit

Lecture 6: Saving and Displaying the History Using the Streamlit Session State

Chapter 6: Prompt Engineering for Gemini API

Lecture 1: Intro to Prompt Engineering

Lecture 2: Tactic #1 – Position Instructions Clearly With Delimiters

Lecture 3: Tactic #2 – Provide Detailed Instructions for the Context, Outcome, or Length

Lecture 4: Tactic #3 – Specify the Response Format

Lecture 5: Tactic #4 – Few-Shot Prompting

Lecture 6: Tactic #5 – Specify the Steps Required to Complete a Task

Lecture 7: Tactic #6 – Give Models Time to Think

Lecture 8: Other Tactics for Better Prompting and Avoiding Hallucinations

Lecture 9: Prompt Engineering Summary

Chapter 7: Deep Dive into Anthropics Claude 3 API

Lecture 1: Claude 3 Models: Haiku, Sonnet, Opus

Lecture 2: Anthropic API Setup

Lecture 3: Generating Text from Text Prompts

Lecture 4: Understanding the Assistant Role

Lecture 5: The System Prompt

Lecture 6: Streaming Responses from Claude

Lecture 7: Exploring Multimodal AI: Vision Capabilities

Lecture 8: Using Online Images as Input

Lecture 9: Vision Testing: Handwriting, Charts, and Visual Prompting

Lecture 10: Combining Multiple Images

Chapter 8: [Appendix] Python Programming

Lecture 1: README

Lecture 2: While and continue Statements

Lecture 3: While and break Statements

Lecture 4: List Slicing and Iteration

Lecture 5: List Comprehension – Part 1

Lecture 6: List Comprehension – Part 2

Lecture 7: Working with Dictionaries

Lecture 8: JSON Data Serialization

Lecture 9: JSON Data Deserialization

Lecture 10: Assignment: JSON and Requests/REST API

Lecture 11: Assignment Answer: JSON and Requests/REST API

Chapter 9: [Appendix] Building Front-ends for AI Apps With Streamlit

Lecture 1: Introduction to Streamlit

Lecture 2: Streamlit Main Concepts

Lecture 3: Displaying Data on the Screen: st.write() and Magic

Lecture 4: Widgets, Part 1: text_input, number_input, button

Lecture 5: Widgets, Part 2: checkbox, radio, select

Lecture 6: Widgets, Part 3: slider, file_uploader, camera_input, image

Lecture 7: Layout: Sidebar

Lecture 8: Layout: Columns

Lecture 9: Layout: Expander

Lecture 10: Displaying a Progress Bar

Lecture 11: Session State

Lecture 12: Callbacks

Chapter 10: Where to Go From Here?

Lecture 1: Whats Next?

Chapter 11: BONUS SECTION

Lecture 1: Congratulations

Lecture 2: BONUS: THANK YOU GIFT!

Instructors

Andrei Dumitrescu
Software Engineer and Lead Instructor

Crystal Mind Academy
Teaching Cutting-Edge Technologies

Rating Distribution

1 stars: 1 votes

2 stars: 2 votes

3 stars: 5 votes

4 stars: 27 votes

5 stars: 46 votes

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