Coding with Generative AI from Coursera

This microlearning course (approximately 3 hours) provides an introductory exploration of the fundamental concepts of Generative AI with a specific focus on its application for coders. You will gain an understanding of the underlying principles of generative AI and learn how to effectively use it in your coding applications.

By the end of this course, you will be able to, discuss the Fundamental Concepts of Generative AI, and apply generative AI tools and techniques to solve real-world coding challenges.

You'll learn the fundamentals of neural network architectures, generative models, and the use of generative AI to generate codes quickly and efficiently. You will also learn to implement functions and classes, as well as about procedural code into object-oriented code, fixing syntax errors and code corrections using generative AI. You will also learn about the fundamental differences between exact search and semantic search.

This is the first course in the series of courses, where you will learn about the nuances of coding using generative AI. The course is suited for Generative AI enthusiasts, GenAI Engineers, AI Engineers, Data Scientists, Data Engineers, and Solution Architects.

To be successful in this course, you should have a curious mind, an understanding of programming languages, especially Python, and a basic knowledge of generative AI technologies and platforms.

What's inside

Syllabus

Course Introduction

The module sets the stage for an engaging exploration of the symbiotic relationship between coders and Generative AI. It provides a comprehensive understanding of how Generative AI operates, exploring its capabilities and applications in coding contexts. By the end of this module, you will have a comprehensive grasp of how to harness the potential of Generative AI to enhance code quality.

Generate code with GenAI

With this comprehensive module, learn to expedite code development through efficient techniques. Dive into the intricacies of array manipulation. Elevate your programming skills by mastering the implementation of functions and classes, gain a keen eye for identifying and rectifying syntax errors, ensuring the integrity and quality of your code. And explore the nuances of semantic search with ChatGPT, distinguishing it from traditional search methods like StackOverflow's exact search.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Suitable for Generative AI enthusiasts and professionals seeking to enhance their coding skills using Generative AI

Practical course with hands-on exercises to reinforce learning

Introduces fundamental concepts and applications of Generative AI for coders

Taught by Fractal Analytics, a leading provider of AI solutions

May require prior understanding of programming languages, especially Python

Assumes basic knowledge of Generative AI technologies and platforms

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 Coding with Generative AI with these activities:

Review Generative AI Principles

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Review core concepts of Generative AI to prepare for course content

Browse courses on AI Fundamentals

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Read introductory articles on Generative AI
Review basic principles of neural networks
Explore different types of generative models

Strengthen Coding Fundamentals

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Refresh basic coding skills to enhance comprehension

Browse courses on Python Programming

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Review programming concepts
Practice writing simple code snippets
Solve coding challenges to test understanding

Create Codes with Generative AI Tools

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Gain hands-on experience with Generative AI techniques

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Identify a simple coding problem
Use Generative AI to generate code solutions
Evaluate the generated code and optimize it

Six other activities

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Practice Semantic Search with ChatGPT

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Enhance search skills by using ChatGPT for semantic search

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Formulate complex search queries
Use ChatGPT to get context-rich responses
Compare results with traditional search methods

Fix Syntax Errors with Generative AI

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Develop practical skills in correcting code using Generative AI

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Gather a set of code with syntax errors
Use Generative AI to identify and correct errors
Test and validate the corrected code

Attend a Generative AI Workshop

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Gain exposure to industry expertise and best practices

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Identify a relevant Generative AI workshop
Register and attend the workshop
Participate actively in discussions and Q&A sessions
Network with experts and fellow attendees

Explore Generative AI in a Real-World Project

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Apply Generative AI skills to a practical project

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Define a project idea that leverages Generative AI
Plan the project scope and objectives
Implement the Generative AI solution
Evaluate the project results and make improvements

Help Others Learn Generative AI

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Solidify understanding and give back to the community

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Join a community or forum related to Generative AI
Offer assistance to beginners who have questions
Create tutorials or blog posts to share your knowledge

Participate in a Generative AI Hackathon

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Enhance skills and gain recognition through competition

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Identify an appropriate Generative AI hackathon
Form a team and prepare for the competition
Develop an innovative Generative AI solution
Present the solution and compete for prizes

Career center

Learners who complete Coding with Generative AI will develop knowledge and skills that may be useful to these careers:

Generative AI Developer

Generative AI Developers are responsible for developing and maintaining generative AI models. They work on a wide range of projects that involve natural language generation, image generation, and music generation. Many Generative AI Developers have a background in computer science or a related discipline and have experience in machine learning and deep learning. For professionals in this field, taking this course may help them become more proficient and efficient in coding with generative AI.

See salaries and explore the career path for Generative AI Developer

Natural Language Processing Scientist

Natural Language Processing Scientists are responsible for researching and developing new natural language processing algorithms. They work on a wide range of projects, from developing new methods for machine translation to creating new ways to answer questions from text. Many Natural Language Processing Scientists have a background in computer science, linguistics, or mathematics. Those in this field may find this course useful for learning how to leverage generative AI in their work to develop more innovative and effective natural language processing models.

See salaries and explore the career path for Natural Language Processing Scientist

Machine Learning Scientist

Machine Learning Scientists are responsible for researching and developing new machine learning algorithms. They work on a wide range of projects, from developing new methods for image recognition to creating new ways to predict customer behavior. Many Machine Learning Scientists have a background in computer science, mathematics, or statistics. Those in this field may find this course useful for learning how to leverage generative AI in their work to develop more innovative and effective machine learning models.

See salaries and explore the career path for Machine Learning Scientist

Software Development Engineer

Software Development Engineers design, develop, and maintain software systems. They work on a wide range of projects, from small business applications to large-scale enterprise systems. Many Software Development Engineers have a background in computer science or a related discipline. Those in this field may find this course useful for learning how to leverage generative AI in their work to optimize code quality and maintainability.

See salaries and explore the career path for Software Development Engineer

Full-Stack Developer

Full Stack Developers are responsible for designing, developing, and maintaining both the front end and back end of web applications. They work on a wide range of projects, from small business websites to large-scale enterprise applications. Many Full Stack Developers have a background in computer science or a related discipline. Those in this field may find this course useful for learning how to leverage generative AI in their work to optimize code quality and efficiency.

See salaries and explore the career path for Full-Stack Developer

Natural Language Processing Engineer

Natural Language Processing Engineers design and implement natural language processing algorithms to solve complex problems. They work on various projects that involve machine translation, text summarization, and question answering. Many Natural Language Processing Engineers have a background in computer science, linguistics, or mathematics. For those in this field, this course may be a useful resource for understanding how generative AI can be used in natural language processing.

See salaries and explore the career path for Natural Language Processing Engineer

Machine Learning Engineer

Machine Learning Engineers build and maintain machine learning models to solve complex problems. Common tasks include feature engineering, model development, and training. Successful Machine Learning Engineers typically have a background in computer science, statistics, or mathematics. This course may be useful for those working as Machine Learning Engineers who want to optimize their code using generative AI.

See salaries and explore the career path for Machine Learning Engineer

Web Developer

Web Developers are responsible for designing, developing, and maintaining websites. They work on a wide range of projects, from small business websites to large-scale enterprise applications. Many Web Developers have a background in computer science or a related discipline. Those in this field may find this course useful for learning how to leverage generative AI in their work to optimize code quality and efficiency.

See salaries and explore the career path for Web Developer

Data Scientist

Data Scientists collect, analyze, and interpret data to help organizations make informed decisions. They use their knowledge of statistics, machine learning, and programming to uncover insights and patterns in data. Many Data Scientists have a background in statistics, mathematics, or computer science. For those in this field, this course may be a useful resource for understanding how generative AI can be used in conjunction with data analysis.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts collect, analyze, and interpret data to help organizations make informed decisions. They work with stakeholders to understand the business context and data needs, and then use statistical techniques to analyze data and develop insights. Data Analysts typically have a background in statistics, mathematics, or computer science. For those in this field, this course may be a useful resource for understanding how generative AI can be used to automate data analysis tasks.

See salaries and explore the career path for Data Analyst

Software Architect

Software Architects design and implement software systems. They play a key role in ensuring that software systems are scalable, reliable, and efficient. Most Software Architects have a background in computer science or a related discipline and have worked in software development for many years. This course may be useful for Software Architects who want to stay at the cutting edge of development practices by learning how to integrate generative AI into the software development process.

See salaries and explore the career path for Software Architect

Computer Vision Engineer

Computer Vision Engineers design and implement computer vision algorithms to solve complex problems. They work on various projects that involve image recognition, object detection, and facial recognition. Many Computer Vision Engineers have a background in computer science, mathematics, or electrical engineering. For those in this field, taking this course may help them learn how generative AI can be used to enhance their work.

See salaries and explore the career path for Computer Vision Engineer

AI Engineer

AI Engineers are responsible for designing, building, and maintaining artificial intelligence models used by businesses. They work on various projects that involve natural language processing, computer vision, machine learning, and deep learning. A strong foundation in computer science and experience in software development are common amongst AI Engineers. For those in this field, taking this course may help build a foundation for using generative AI in development.

See salaries and explore the career path for AI Engineer

Product Manager

Product Managers are responsible for managing the development and launch of new products. They work closely with engineering, marketing, and sales teams to ensure that products meet the needs of customers. Many Product Managers have a background in business or engineering. For those in this field, this course may be useful for understanding how generative AI can be used to develop more innovative products.

See salaries and explore the career path for Product Manager

Software Development Manager

Software Development Managers oversee development teams of software engineers, ensuring that products are created according to specifications, on time, and within budget. They collaborate with stakeholders to understand needs, design solutions, and implement software solutions. To be successful in this role, many Software Development Managers have a background in computer science or a related discipline and have worked in software development for over 5 years. This course may be a useful resource for Software Development Managers who want to integrate generative AI into their development process.

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Coding with Generative AI

What's inside

Syllabus

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