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Krish Naik and KRISHAI Technologies Private Limited

Unlock the power of generative AI with our comprehensive course on building applications using Google Gemini Models. Whether you're a beginner or an experienced AI enthusiast, this course is designed to take you through the fundamentals and advanced techniques of using Google Gemini, Gemini Pro, Gemini Flash, and Gemini Pro 1.5 models.

What You Will Learn:

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Unlock the power of generative AI with our comprehensive course on building applications using Google Gemini Models. Whether you're a beginner or an experienced AI enthusiast, this course is designed to take you through the fundamentals and advanced techniques of using Google Gemini, Gemini Pro, Gemini Flash, and Gemini Pro 1.5 models.

What You Will Learn:

  • Introduction to Google Gemini Models:

    • Dive deep into the architecture, features, and capabilities of Google Gemini models.

    • Understand the unique strengths of Gemini Pro, Gemini Flash, and Gemini Pro 1.5.

  • Model Training and Fine-Tuning:

    • Gain hands-on experience with training and fine-tuning these powerful models.

    • Learn techniques to optimize performance for various generative AI tasks.

  • Application Development:

    • Develop practical skills to build diverse generative AI applications such as text generation, image synthesis, and language translation.

    • Explore real-world examples and projects to solidify your understanding.

  • Integration and Deployment:

    • Learn how to seamlessly integrate Google Gemini models into your existing systems.

    • Master the deployment of AI applications in cloud-based and on-premise environments.

  • Ethics and Best Practices:

    • Understand the ethical considerations and best practices in generative AI development.

    • Implement responsible AI principles to ensure fairness, transparency, and accountability.

Who Should Enroll:

  • AI Enthusiasts: Looking to explore the exciting world of generative AI.

  • Developers: Seeking to build and deploy cutting-edge AI applications.

  • Data Scientists: Aiming to enhance their skills with advanced generative models.

  • Tech Professionals: Wanting to integrate AI into their business solutions.

Course Highlights:

  • Expert-led video tutorials with step-by-step instructions.

  • Hands-on projects and real-world examples.

  • Access to exclusive resources and datasets.

  • Community support and peer collaboration.

  • Quizzes and assignments to test your knowledge.

Join us on this journey to master generative AI with Google Gemini Models and unlock endless possibilities in the world of artificial intelligence. Enroll now and start building the future of AI today.

Enroll now

What's inside

Learning objectives

  • Develop practical skills in building and deploying generative ai applications using google gemini models.
  • Gain hands-on experience in training and fine-tuning google gemini models using various datasets.
  • Create diverse applications leveraging the power of these gemini models.
  • End to end projects using google gemini models

Syllabus

Getting Started
Welcome to the Course
Course Materials
Python Programming Language
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Getting Started With Vs Code
Python Basics-Syntax And Semantics
Variables In Python
Basics Data Types In Python
Operators In Python
Python Control Flow
Conditional Statements(if,elif,else)
Loops In Python
Inbuilt Data Structure In Python
Lista And List Comprhrension
Practical Examples Of List
Tuples In Python
Dictionaries In Python
Functions In Python
Getting Started With Functions
More Examples For Functions
Lambda Function
Map Function In Python
Filter Function In Python
Importing Creating Modules And Packages
Import Modules And Packages In Python
Standard Library Overview
File Handling With Python
File Operation With Python
Working With File Paths
Exception Handling
Exception Handling With Try Except Finally else blocks
OOPS Concepts With Classes And Objects
Classes And Objects In Python
Inheritance In OOPS
Polymorphism In OOPS
Encapsulation In OOPS
Abstraction In OOPS
Magic Methods In Python
Operator Overloading In Python
Custom Exception Handling
Introduction To Generative AI
Introduction To Google Gemini Models And Its Variants
Creating Free API Keys For Google Gemini Models
Exploring All Google Gemini Models Variants With Practical Demo
What is Generative AI
End To End LLM And LIM Projects Using Google Gemini
Building End to End LLM and Large image model application using Gemini pro
Step by Step Tutorial to Create Conversational Q&A Chatbot using Gemini Pro
End to End Multi Language Invoice Extractor Project using Gemini Pro LLM Model
Chat With multiple Pdf Documents with Langchain and Google Gemini Pro
End To End Document Q&A Using Google Gemma,Groq API
End to End Text to SQL LLM App along with Quering SQL database using Gemini Pro
End To End Resume Application Tracking System(ATS) Using Google Gemini Pro Visio
Modified End To End Resume ATS Tracking LLM Project With Google Gemini Pro
End to End Nutritionist Generative AI Doctor Using Google Gemini Pro Vision
End To End Youtube Video Transcribe Summarizer LLM App With Google Gemini Pro
CrewAI-Create Multi AI Agents With LLM Models
Building YT Videos To Blog Creator AI Agents Using CrewAI And OpenAI
News Reporter AI Agent Using CrewAI With Google Gemini Pro Models
Fine Tuning With Google Open Source Models
What is Quantization-Theoretical Intuition
LORA And QLORA Indepth Intuition
Finetuning With Custom Data Using Google Gemma Models
What To Do Going Ahead
Keep On Exploring

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Provides hands-on experience with Google Gemini models, which are cutting-edge tools in the rapidly evolving field of generative AI
Covers building end-to-end LLM and large image model applications, which are highly sought-after skills in the AI job market
Includes practical projects like building a conversational Q&A chatbot and a multi-language invoice extractor, demonstrating real-world applications of Gemini Pro
Explores fine-tuning with Google open-source models, offering insights into customizing and optimizing AI models for specific tasks
Requires creating free API keys for Google Gemini models, which may involve some initial setup and potential limitations depending on usage tiers
Assumes a foundational understanding of Python programming, which may require additional learning for individuals new to coding

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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 Building Gen AI App 12+ Hands-on Projects with Gemini Pro with these activities:
Review Python Fundamentals
Strengthen your Python foundation to better understand the code examples and project implementations in the course.
Browse courses on Python Basics
Show steps
  • Review basic syntax and data structures.
  • Practice writing simple Python scripts.
  • Complete online Python tutorials.
Read 'Generative AI with Python and TensorFlow 2'
Gain a broader understanding of generative AI principles to better contextualize the use of Gemini models.
Show steps
  • Read the chapters on generative models.
  • Experiment with the code examples provided in the book.
Read 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow'
Gain a broader understanding of machine learning principles to better contextualize the use of Gemini models.
Show steps
  • Read the chapters on neural networks and deep learning.
  • Experiment with the code examples provided in the book.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Experiment with Different Gemini Pro Prompts
Improve your prompt engineering skills by experimenting with different prompts and analyzing the outputs from Gemini Pro.
Show steps
  • Create a list of diverse prompts for Gemini Pro.
  • Run each prompt and analyze the generated output.
  • Refine the prompts based on the analysis.
  • Document your findings and best practices.
Build a Simple Text Summarization App
Apply your knowledge of Gemini Pro to create a practical application that summarizes text from articles or documents.
Show steps
  • Choose a text summarization dataset.
  • Implement the summarization logic using Gemini Pro.
  • Create a user interface for the app.
  • Test and refine the summarization results.
Write a Blog Post on Gemini Pro Use Cases
Solidify your understanding of Gemini Pro by researching and writing about its various applications and potential use cases.
Show steps
  • Research different applications of Gemini Pro.
  • Outline the structure of your blog post.
  • Write the blog post with clear examples and explanations.
  • Publish the blog post on a platform like Medium or your personal website.
Contribute to a Gemini Pro Open Source Project
Deepen your understanding of Gemini Pro by contributing to an open-source project that utilizes the model.
Show steps
  • Find an open-source project using Gemini Pro.
  • Identify a bug or feature to work on.
  • Implement the fix or feature and submit a pull request.
  • Respond to feedback and iterate on your contribution.

Career center

Learners who complete Building Gen AI App 12+ Hands-on Projects with Gemini Pro will develop knowledge and skills that may be useful to these careers:
Generative AI Developer
A Generative AI Developer specializes in creating applications that leverage generative AI models. This course is directly aligned with this role, as it delves into the use of Google Gemini models, including Gemini Pro, Flash, and 1.5. The course's hands-on projects allow a Generative AI Developer to build real-world applications. By understanding model integration and deployment, the course prepares the developer to work with these tools effectively.
Artificial Intelligence Engineer
An Artificial Intelligence Engineer designs, develops, and implements AI solutions. This role involves working with models like Google Gemini, which this course specifically covers with practical, hands-on projects. The course's emphasis on model training, fine-tuning, and application development helps the AI Engineer to effectively build and deploy AI systems. A deep understanding of the Gemini models, gained from this course, is crucial for anyone wishing to excel as an Artificial Intelligence Engineer.
Machine Learning Specialist
A Machine Learning Specialist works on designing, building and deploying machine learning systems. This course is directly relevant, as it provides hands-on experience with Gemini models. The course's emphasis on training, fine-tuning and deploying these models will help the Machine Learning Specialist, as they work on real-world problems. The course gives practical experience with diverse generative AI applications using Gemini.
Machine Learning Engineer
A Machine Learning Engineer focuses on building and deploying machine learning models. This course builds experience with training and fine-tuning Google Gemini models, skills that are critical for any Machine Learning Engineer. The course gives practical experience with diverse generative AI applications, such as text generation and language translation. This course may be useful for those who seek to become a Machine Learning Engineer, as practical examples are provided.
Applications Developer
An Applications Developer is responsible for designing, developing, and implementing software applications. This course on building Gen AI applications with Google Gemini is directly relevant to the role of an Applications Developer, especially for those wanting to incorporate AI. The course teaches practical skills for building diverse AI applications, which aligns directly with the responsibilities of this role. The course's focus on hands-on projects will help one to work effectively.
AI Solutions Architect
An AI Solutions Architect designs and oversees the implementation of AI solutions within an organization. This course provides a solid understanding of Google Gemini models, model training, and application development, which is useful for an AI Solutions Architect. The knowledge of integrating these models into existing systems and the deployment of AI applications is essential for this role. This course may be helpful for those who wish to become an AI Solutions Architect.
Data Scientist
A Data Scientist analyzes and interprets complex data to drive business decisions. This course may be useful, as the course provides experience with advanced generative models. The integration and deployment of AI applications will help the Data Scientist enhance the skills needed to implement these models in real-world situations. The course's hands-on component also provides practical experience with model training and fine-tuning.
Natural Language Processing Engineer
A Natural Language Processing Engineer develops systems that allow computers to understand and process human language. This course may be useful, as the course focuses on Google Gemini models and their capabilities in text generation and language translation. This learning helps the Natural Language Processing Engineer to build effective NLP solutions. The hands-on projects in the course will provide the practical experience needed to apply these models to real-world problems.
AI Consultant
An AI Consultant advises organizations on how to use AI to achieve their goals. The course provides an understanding of generative AI models, such as Google Gemini. The consultant can learn about ethical considerations and best practices in AI development, which is important when guiding organizations. This course may be useful for those who wish to become AI Consultants.
AI Product Manager
An AI Product Manager oversees the development and launch of AI-driven products. This course, while not directly focused on product management, may be useful for an AI Product Manager as it provides a solid understanding of the capabilities of Google Gemini models. The course's focus on practical applications and hands-on experience are valuable for product managers to understand the technical aspects of the AI products they manage. The course helps Product Managers communicate with their engineering team.
Computer Vision Engineer
A Computer Vision Engineer focuses on enabling computers to 'see' and interpret images. The course provides practical examples that may be useful for the Computer Vision Engineer. With a strong foundation in generative AI models, this course also gives opportunities to build skills with text generation and language translation, all of which may be relevant. Training and fine-tuning models is a key part of the role.
AI Research Scientist
An AI Research Scientist conducts advanced research in artificial intelligence, often requiring a master's degree or a PhD. This course, while more focused on practical applications, may be useful for someone who is also seeking fundamental knowledge. This course provides the hands-on experience with Google Gemini models, which is relevant to the broader field of AI research. The understanding of model training and fine-tuning helps to build a foundation for more advanced work.
Software Developer
A Software Developer creates and maintains software applications. This course on building Gen AI applications with Google Gemini Models may be useful for a software developer who wishes to incorporate generative AI into their projects. The course teaches how to integrate AI models into existing systems and deploy applications, skills that are useful for any Software Developer. The course focuses on hands-on projects to build diverse AI applications.
Data Analyst
A Data Analyst collects, processes, and performs statistical analyses on data. This course may be useful for a Data Analyst interested in leveraging generative AI. While the course focuses primarily on AI models, the understanding of model training and fine-tuning can be valuable for any data professional. This course provides practical skills in building and deploying AI applications, which can enhance a Data Analyst's workflow.
Robotics Engineer
A Robotics Engineer designs, builds, and maintains robots and robotic systems. This course provides exposure to generative AI models, which can help them integrate AI into robotics systems. The course provides practical skills for integrating and deploying AI applications. This course may be useful for Robotics Engineers who want to enhance their capabilities with AI.

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 Building Gen AI App 12+ Hands-on Projects with Gemini Pro.
Provides a comprehensive guide to generative AI techniques using Python and TensorFlow 2. While the course focuses on Google Gemini, this book will help you understand the broader landscape of generative AI and the underlying principles. It's a valuable resource for understanding the different types of generative models and how to implement them. This book is helpful in providing background and prerequisite knowledge.
Provides a comprehensive introduction to machine learning concepts and tools, including TensorFlow and Keras. While the course focuses on Gemini models, understanding the broader landscape of machine learning will enhance your ability to apply and adapt these models effectively. It's a valuable resource for understanding the underlying principles and best practices in the field. This book is commonly used as a textbook at academic institutions.

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