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Learn Google's Gemini and Anthropic's Claude API with Python

Andrei Dumitrescu and Crystal Mind Academy

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.

Read more

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.

Enroll now

What's inside

Learning objectives

  • Gain a deep understanding of the google's gemini and anthropic's 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
  • 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
  • Show more
  • Show less

Syllabus

Getting Started
How to Get the Most Out of This Course
Python IDEs For This Course
Setting Up the Environment: Jupyter Notebook
Read more
Setting Up the Environment: Google Colab
Join Our Online Community!
Course Resources
Deep Dive into Google Gemini Pro API
Getting a Gemini API Key
Quiz for Getting a Gemini API Key
Installing the Python SDK for Gemini Pro API and Authenticating to Gemini
Quiz for Installing the Python SDK
Gemini Multimodal Models: Nano, Pro and Ultra
Quiz for Gemini Models
Google AI Studio: Freeform Prompts With Gemini Pro Vision
Google AI Studio: Using Variables and Parameters in the Prompt
Generating Text From Text Inputs: Gemini Pro
Streaming Model Responses
Quiz for Generating Text from Text Inputs
Generating Text From Image and Text Inputs: Gemini Pro Vision
Gemini API Generation Parameters: Controlling How the Model Generates Responses
Gemini API Generation Parameters Explained
Quiz for Gemini API Generation Parameters
Building a Chat Conversation
Quiz for Building a Chat Conversation
Project: Building a Conversational Agent Using Gemini Pro
Unlocking the Power of Gemini 1.5 Pro API
Introduction to Gemini 1.5 Pro
System Instructions
The File API: Prompting with Media Files
Tokens in Gemini API
The File API: Prompting with Audio
Jupyter AI
Python Version
Introduction to Jupyter AI and Other Coding Companions
Installing Jupyter AI
Using Jupyter AI in JupyterLab
Setting Up Jupyter AI in Jupyter Notebook
Using Jupyter AI in Jupyter Notebook
Using Interpolation for More Advanced Use Cases
Using Jupyter AI with Other Providers and Models
Project: Talking With an Image
Project Requirements
Building the Application
Testing the Application
Streamlit: Transform Your Jupyter Notebooks into Interactive Web Apps
Creating the Web App Layout With Streamlit
Saving and Displaying the History Using the Streamlit Session State
Prompt Engineering for Gemini API
Intro to Prompt Engineering
Tactic #1 - Position Instructions Clearly With Delimiters
Tactic #2 - Provide Detailed Instructions for the Context, Outcome, or Length
Tactic #3 - Specify the Response Format
Tactic #4 - Few-Shot Prompting
Tactic #5 - Specify the Steps Required to Complete a Task
Tactic #6 - Give Models Time to "Think"
Other Tactics for Better Prompting and Avoiding Hallucinations
Prompt Engineering Summary
Deep Dive into Anthropic's Claude 3 API
Claude 3 Models: Haiku, Sonnet, Opus
Anthropic API Setup
Generating Text from Text Prompts
Understanding the Assistant Role
The System Prompt
Streaming Responses from Claude
Exploring Multimodal AI: Vision Capabilities
Using Online Images as Input
Vision Testing: Handwriting, Charts, and Visual Prompting
Combining Multiple Images
[Appendix] Python Programming
README
While and continue Statements
While and break Statements
List Slicing and Iteration
List Comprehension - Part 1
List Comprehension - Part 2
Working with Dictionaries
JSON Data Serialization
JSON Data Deserialization
Assignment: JSON and Requests/REST API
Assignment Answer: JSON and Requests/REST API
[Appendix] Building Front-ends for AI Apps With Streamlit
Introduction to Streamlit
Streamlit Main Concepts
Displaying Data on the Screen: st.write() and Magic
Widgets, Part 1: text_input, number_input, button
Widgets, Part 2: checkbox, radio, select
Widgets, Part 3: slider, file_uploader, camera_input, image
Layout: Sidebar
Layout: Columns
Layout: Expander
Displaying a Progress Bar
Session State
Callbacks
Where to Go From Here?
What's Next?
BONUS SECTION
Congratulations
BONUS: THANK YOU GIFT!

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Covers Gemini 1.5 Pro API, which is Google's most advanced AI model, giving learners access to cutting-edge technology
Includes instruction on Anthropic's Claude 3 API, providing exposure to multiple state-of-the-art LLMs
Teaches Streamlit, which allows learners to create web interfaces for their LLM applications
Explores prompt engineering techniques, which are essential for effectively using LLMs and getting desired results
Includes a section on Jupyter AI, which can streamline the process of working with AI models in a notebook environment
Requires learners to obtain API keys, which may involve a signup process and potential costs depending on usage

Save this course

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Learn Google's Gemini and Anthropic's Claude API with Python with these activities:
Review Python Fundamentals
Strengthen your Python foundation to better understand the API interactions and code examples used throughout the course.
Browse courses on Python Basics
Show steps
  • Review basic data types and operators.
  • Practice writing simple functions and loops.
  • Familiarize yourself with list comprehensions.
Read 'Effective Python' by Brett Slatkin
Improve your Python coding skills to better implement and customize the Gemini and Claude API integrations.
View Effective Python: 125 Specific Ways to Write... on Amazon
Show steps
  • Read the first few chapters covering basic Python idioms.
  • Focus on sections related to functions and classes.
  • Try implementing some of the examples in the book.
Practice Prompt Engineering
Refine your prompt engineering skills to get the most out of the Gemini and Claude APIs.
Show steps
  • Experiment with different prompting techniques.
  • Analyze the responses and iterate on your prompts.
  • Try few-shot prompting with different examples.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Document Your Prompt Engineering Experiments
Solidify your understanding of prompt engineering by documenting your experiments and findings.
Show steps
  • Create a document to record your prompts and results.
  • Analyze the effectiveness of different prompts.
  • Share your findings with other students.
Build a Simple Q&A App with Gemini or Claude
Apply your knowledge by building a practical application that uses the Gemini or Claude API to answer questions.
Show steps
  • Choose either Gemini or Claude API for your project.
  • Design the user interface for your Q&A app.
  • Implement the API calls to get answers from the model.
  • Test and refine your application.
Create a Streamlit Web App for your LLM project
Enhance your project by creating a web interface using Streamlit, allowing others to easily interact with your LLM application.
Show steps
  • Design the layout of your Streamlit app.
  • Implement widgets for user input and output.
  • Integrate your LLM project with the Streamlit interface.
  • Deploy your Streamlit app for others to use.
Read 'Generative AI with Python and TensorFlow 2'
Gain a deeper understanding of the underlying principles of generative AI to better utilize the Gemini and Claude APIs.
View Generative AI with Python and TensorFlow 2:... on Amazon
Show steps
  • Read the chapters on generative models and techniques.
  • Focus on the sections related to language models.
  • Experiment with the code examples provided in the book.

Career center

Learners who complete Learn Google's Gemini and Anthropic's Claude API with Python will develop knowledge and skills that may be useful to these careers:
Prompt Engineer
A Prompt Engineer crafts effective prompts for large language models (LLMs). This course is ideally suited for those looking to become a prompt engineer as it teaches how to effectively use Google's Gemini API as well as Anthropic's Claude API. You will learn techniques on how to design prompts that generate desirable responses, including using few examples, finding relevant context, and exploring different strategies. This practical, hands-on experience is essential to a prompt engineer. Additionally, the course covers the use of parameters, text generation, and creating chat agents, all of which are critical tasks.
See salaries and explore the career path for Prompt Engineer
Chatbot Developer
A Chatbot Developer creates conversational AI systems, which are a key focus of this course, that teaches how to use the Gemini and Claude APIs to build custom chat conversational agents. This job requires skills in prompt engineering, which is also covered in detail. This includes how to generate text, stream model responses, and control generation parameters. The practical projects in the course give a Chatbot Developer hands-on experience in building and testing conversational AI. Learning Streamlit for web interfaces is also valuable for deploying functional chatbots.
See salaries and explore the career path for Chatbot Developer
AI Application Developer
An AI Application Developer designs and builds applications that use AI. This course in Gemini and Claude APIs will help build a foundation for this role. The course teaches how to use the Gemini Pro Vision API with Python to manage text and multimodal inputs, and how to build custom chat agents. This is directly relevant to building AI-enabled applications. The course also teaches how to create web interfaces using Streamlit, which is a crucial skill for deploying AI-powered applications. The lessons on prompt engineering enable an AI application developer to fine-tune their application for optimal performance.
See salaries and explore the career path for AI Application Developer
Artificial Intelligence Engineer
An Artificial Intelligence Engineer develops and implements AI models and applications. This role involves using tools like Google's Gemini and Anthropic's Claude APIs, with skills in prompt engineering. This course will help you learn how to integrate large language models (LLMs) into various applications by teaching the core concepts of using Gemini and Claude APIs with Python. You'll also learn how to build custom chat agents and web interfaces for AI applications. Understanding how to use the Gemini Pro Vision API with Python, as covered in this course, is directly applicable to building multimodal AI applications. By mastering prompt engineering, an AI Engineer can tailor their models to perform optimally.
See salaries and explore the career path for Artificial Intelligence Engineer
Machine Learning Engineer
A Machine Learning Engineer builds, trains, and deploys machine learning models. This role requires familiarity with a variety of models such as those offered by Google's Gemini and Anthropic's Claude. This course teaches how to utilize the Gemini and Claude APIs via Python, along with techniques for prompt engineering. This is invaluable to a Machine Learning Engineer. The course's focus on practical projects would enable one to build experience integrating LLMs into a range of applications. The exploration of the Gemini Pro Vision API and multimodal AI is directly applicable to a Machine Learning Engineer who needs to work with complex data types.
See salaries and explore the career path for Machine Learning Engineer
Natural Language Processing Engineer
A Natural Language Processing Engineer designs systems that allow machines to understand and process human language. This course provides a solid foundation through its exploration of Google's Gemini API as well as Anthropic's Claude API. These tools are critical for many NLP tasks. This role requires a strong understanding of prompt engineering, which this course covers in detail. Moreover, the ability to generate text, stream model responses, and build conversational agents is highly relevant. Learning to use the Gemini Pro Vision API with Python is useful for any NLP Engineer who works with multimodal inputs.
See salaries and explore the career path for Natural Language Processing Engineer
Computational Linguist
A Computational Linguist develops computational models of language. This role benefits from a strong understanding of natural language processing, which this course helps with by teaching the Gemini and Claude APIs. These APIs are useful in exploring how large language models process and generate text. This course provides practical instruction on how to use these tools with Python, which directly supports the needs of a computational linguist. By learning prompt engineering techniques, you'll gain experience in controlling the models' behavior, which is invaluable in the field.
See salaries and explore the career path for Computational Linguist
Computer Vision Engineer
A Computer Vision Engineer develops systems that enable computers to 'see' and interpret images and videos. This role is closely related to the technologies explored in this course. The Gemini Pro Vision API provides tools for managing multimodal data, including image and video. The course teaches how to use this API via Python while also providing practical experience with prompt engineering. This is critical to extracting accurate information. Also, the course focus on media handling could be valuable to a Computer Vision Engineer.
See salaries and explore the career path for Computer Vision Engineer
Robotics Engineer
A Robotics Engineer designs, builds, and programs robots. This role may involve integrating computer vision, natural language processing, and other forms of AI. This course may be useful for a robotics engineer. It provides a foundational understanding of building multimodal AI systems using the Gemini Pro Vision API with Python. This is in addition to learning how to manage text, audio, and image data through effective prompt engineering. Knowledge of Gemini and Claude APIs for text generation is also beneficial when developing AI components.
See salaries and explore the career path for Robotics Engineer
Research Scientist
A Research Scientist conducts studies and experiments to advance knowledge. This role often requires a high degree of proficiency with advanced technologies such as large language models. The course will be helpful as it provides an understanding of both the Gemini and Claude APIs. It also teaches the implementation of these models via Python. The skill of prompt engineering is particularly useful for experimenting with different model inputs and use cases. This course could help a research scientist stay current with the most recent innovations in AI.
See salaries and explore the career path for Research Scientist
Data Scientist
A Data Scientist analyzes large datasets to extract meaningful insights. While this role is broad, the skills developed in this course on Google's Gemini API and Anthropic's Claude API can be very beneficial, especially to those working with unstructured data. This course provides the context and technical know-how to perform AI-driven data analysis, using techniques for prompt engineering, generating text, and the processing of multimodal inputs. The knowledge of using APIs and Python for model interaction is helpful to any data scientist interested in integrating generative AI features into their work.
See salaries and explore the career path for Data Scientist
Data Analyst
A Data Analyst examines data to identify trends and insights. This course may be helpful because it teaches how to manage text, and other media types via the Gemini Pro Vision API and Claude APIs in Python. This could provide a data analyst with more tools for exploring complicated data sets. Furthermore, a data analyst may find the ability to create custom chat agents and learn prompt engineering techniques to be a way to efficiently explore unstructured data sets. The use of Streamlit to display data could also be valuable for a Data Analyst.
See salaries and explore the career path for Data Analyst
Software Developer
A Software Developer designs and implements software applications. This course may be useful to a Software Developer, especially one who is working with or plans to integrate AI into software. Learning how to use Google's Gemini API and Anthropic's Claude API can allow a developer to experiment with a variety of AI models. You can use Python to integrate these models into software. The course also touches on how to use Streamlit, useful for building web-based front-ends. Understanding advanced techniques like prompt engineering are helpful in any context.
See salaries and explore the career path for Software Developer
Technical Writer
A Technical Writer creates documentation for technical products and services. This course may be useful for Technical Writers who need better understanding of AI models. The course provides practical techniques using the Gemini and Claude APIs, including generating text and handling multimodal inputs. This knowledge may help a technical writer create documentation about prompt engineering best practices, or to better explain the use of Gemini and Claude AI models. The course’s focus on generating text and using APIs is directly applicable to AI technical writing.
See salaries and explore the career path for Technical Writer
AI Consultant
An AI Consultant advises organizations on how to use AI technologies to meet business goals. Understanding the technical aspects of AI is crucial for this role. This course may be useful by introducing the Gemini and Claude APIs using Python. Prompt engineering, text generation, and building chat agents is practical experience that could help inform your consultations. The course also touches on Streamlit for web interfaces, which helps showcase AI applications. Knowledge of the Gemini Pro Vision API could be useful in recommending AI solutions.
See salaries and explore the career path for AI Consultant

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Learn Google's Gemini and Anthropic's Claude API with Python.
Cover image
Effective Python
Save
Provides practical advice on writing clean, efficient, and maintainable Python code. It covers a wide range of topics, including best practices for using Python's built-in features, writing concurrent code, and debugging. While not directly focused on AI APIs, it will improve your overall Python skills, making it easier to work with the Gemini and Claude APIs. It is valuable as additional reading.
Effective Python: 125 Specific Ways to Write Better...
Kindle Edition
$$
Cover image
Generative AI with Python and TensorFlow 2
Save
Provides a comprehensive guide to generative AI techniques using Python and TensorFlow 2. While the course focuses on Gemini and Claude, understanding the underlying principles of generative AI can enhance your ability to effectively use these APIs. This book is more valuable as additional reading than as a current reference. It can help you understand the broader context of LLMs.
Generative AI with Python and TensorFlow 2: Create...
Paperback
$$
Generative AI with Python and TensorFlow 2: Create...
Kindle Edition
$$

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Effort
7 total hours
Via
Udemy
Instructors
Andrei Dumitrescu
Crystal Mind Academy
Language
English
Good to know
Covers Gemini 1.5 Pro API, which is Google's most advanced AI model, giving learners access to cutting-edge technology
Includes instruction on Anthropic's Claude 3 API, providing exposure to multiple state-of-the-art LLMs
Teaches Streamlit, which allows learners to create web interfaces for their LLM applications
Explores prompt engineering techniques, which are essential for effectively using LLMs and getting desired results
Includes a section on Jupyter AI, which can streamline the process of working with AI models in a notebook environment
Requires learners to obtain API keys, which may involve a signup process and potential costs depending on usage
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