
Discover how to use AI to code machine learning apps, save time, organize complex code, and catch errors, with Python basics and libraries, Jupyter notebooks, and Colab GPU access.
Explore how large language models like Bard, ChatGPT, Claude, and open-source options enable code generation for machine learning apps, compare strengths, and demonstrate a simple five-layer neural network example.
Explore building a naive Bayes classifier in Python using Google Bard to generate code, run in Google Colab with transformers, and adapt it to CSV data from Google Drive.
learn how to run a diffusion model locally with the version model in google colab, install diffusers, transformers, and accelerate, troubleshoot runtime errors, and expose results via a gradio interface.
Learn to build and run a sentiment analysis model in Google Colab using AI prompts, install transformers, and debug a BERT-based classifier with Bard and ChatGPT.
Are you ready to learn how to use AI to code machine learning apps? If yes, then this is the course for you!
Hello and welcome to the course “Using AI To Code Machine Learning Apps”. My name is Bing, and I will be your instructor for this course.
In this course, you will learn how to use AI to code machine learning apps in a fast and easy way. You will learn how to use various AI tools and platforms that can help you generate, edit, and deploy machine learning code without writing a single line of code yourself.
This course is designed for anyone who is interested in learning more about AI and machine learning, or who wants to create their own machine learning apps without coding. Whether you are a beginner or an expert, this course will help you understand how to use AI to code machine learning apps.
To take this course, you should have a basic understanding of what AI and machine learning are and some of the common applications of AI and machine learning in different domains. You should also have a curious and open mind, and a willingness to learn new things.
The course consists of four sections, each covering a different topic related to using AI to code machine learning apps. Each section has several lectures, quizzes, and assignments to help you learn and practice the concepts. At the end of the course, you will have a final project where you will apply what you have learned to create your own machine learning app using AI.
The main topics that we will cover in this course are:
Section 1: How to use AI to generate machine learning code
Section 2: How to use AI to edit and optimize machine learning code
Section 3: How to use AI to deploy and test machine learning code
Section 4: How to use AI to improve and maintain machine learning code
By the end of this course, you will be able to:
Explain the benefits and challenges of using AI to code machine learning apps
Identify and select the best AI tools and platforms for your use case and budget
Use AI to generate machine learning code for various tasks and functions, such as data preprocessing, model building, model training, etc.
Use AI to edit and optimize machine learning code for various aspects and criteria, such as performance, accuracy, efficiency, etc.
Use AI to deploy and test machine learning code on various platforms and devices, such as web, mobile, cloud, etc.
Use AI to improve and maintain machine learning code by adding new features, fixing bugs, updating data, etc.
I hope you are excited to join me on this journey of learning how to use AI to code machine learning apps. I look forward to seeing you in the next lecture. Thank you for choosing this course.