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Wednesday, January 3, 2024

The 2024 AI Developer RoadMap

Hello guys, if you want to learn Artificial Intelligence, Generative AI, and Prompt Engineering then you are thinking on right direction. More and more companies are asking their workforce to learn Generative AI and it's only going to increase. AI Integration will be next big thing when you have to add AI capabilities to existing application to enhance them and that's why learning AI skills in 2024 is a great idea. But question comes how? Well, don't worry, I will tell you. In the past, I have shared best AI courses and best courses to learn ChatGPT, one of the most popular Generative AI example and LLM and in this article I will teach you step by step what skills you need to master AI development and integration.
If you haven't heard about AI or Artificial Intelligence then let me give you a brief overview. You can consider artificial intelligence as the way to make computers mimic the human brain to learn from data and make decisions based on this data.

Artificial intelligence engineers are in-demand in many companies, and most of them are moving to use this field to improve their products and user experience. This article will help you learn the steps you need to become an artificial intelligence engineer.

The 2024 AI Developer RoadMap

Here are key skills you need to learn to master Artificial Intelligence in 2024, it includes programming language as well as things like Machine Learning and Deep Learning which forms the basis of how AI works. 

Here is the comprehensive AI Developer RoadMap I have created which include all the skills you need to succeed to become an AI Expert in 2024. This is very comprehensive and it will take some time before you can master all the skills and many cases you don't need to learn all, so I have also listed the most essential skills after this roadmap with resources which anyone can learn to become aI Developer in 2024

AI Developer RoadMap

1. Learn Python Programming Language

The first thing you have to learn to become an AI developer is to learn programming languages. There are a lot of them, and every company can ask you to have the skills in one or more languages, but python is the most used one among all other languages. It's also one of the most popular programming language for AI development and integration. 

1.1. Python for Everybody

This is the most known course for learning python language on the internet. It is created by Michigan University and offered through Coursera and will help you be an intermediate user of the python language in just a few months. 

You will start learning the basics of this language first, then move to learn the data structure. Later, you will use python to access the web, interact with the database, and more. More than 1.6 million people have already joined this program to learn Python.

2. Learn Statistics

Most people new to the artificial intelligence field don’t understand that statistics are the critical thing that makes artificial intelligence work. You can make an AI algorithm without understanding the statistics or improving it. The artificial intelligence will search for the patterns in the data and use them to recognize things like cats and dogs in the images.

2.1. Introduction to Statistics

 This beginner course for learning statistics will help you understand the basic terms in this field. You will start learning the descriptive statistics and exploring the data and sampling. Then move to learn probability, which is essential in statistics to understand since you will use it a lot in your journey.

Learn different distributions and regression, which is very important, and much other statistics-related information for beginners.

3. Learn Mathematics

You can’t be an artificial intelligence engineer if you don’t learn math. It is also essential if you are trying to learn statistics. Artificial intelligence doesn’t require you to become a math expert or have a Ph.D. level in this field, but at least some of the basics of this science are enough, and if you want to learn more about math, that will be much better.

3.1. Mathematics for Machine Learning

 The good thing about this specialization is that it will teach you what you need to understand in math if you learn machine learning, a subset of the artificial intelligence field. It is for beginners entirely and will probably take two months to complete. You will learn about linear algebra, multivariate calculus, and the principal components analysis.

4. Learn Machine Learning

Machine learning is considered a subset of artificial intelligence and science that makes machines learn from the data you feed to them without programming it to do that thing. It can be used to recognize things in pictures, predict diseases, filter the messages in the email, detect fraud transactions in the bank, and more.

4.1. Machine Learning

This course is created by Andrew Ng and earlier offered by Stanford University is the most known one on the internet for learning machine learning. You will start this course by understanding this science and its different types. 

Then moving to the experimental phase and learning the linear regression, logistic regression, regularization, the neural network that mimics the brain function to learn from data, support vector machine (SVM), and more.

5. Learn Deep Learning

We said before that machine learning is a type of artificial intelligence, and now you can consider deep learning as a subset or type of machine learning. Machine learning uses some algorithms for making the model. Deep learning will use the artificial neural network that mimics the human brain to solve the issue of learning from data and making predictions.

5.1. Machine Learning, Data Science, and Deep Learning

 A complete course for learning machine learning, deep learning, and even data science. You will start as a beginner by understanding some of the statistics concepts and then move to learn machine learning with python. 

You will also learn to make a recommendation system and deal with real-world data. Finally, learn about deep learning and make neural networks that learn from data.

6. Generative AI

Learning Generative AI is an essential component of an AI developer's roadmap for several compelling reasons. Generative AI techniques, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), empower developers to create models that generate new, realistic data based on patterns learned from existing datasets. 

This capability is invaluable across various domains, including image and text generation, style transfer, and even drug discovery. Understanding Generative AI not only enhances an AI developer's toolkit but also fosters creativity and innovation. 

Additionally, as the technology advances, generative models are playing an increasingly pivotal role in applications like content creation, simulation, and data augmentation.

 By mastering Generative AI, developers gain the ability to design intelligent systems that not only analyze existing data but also generate novel and meaningful content, pushing the boundaries of what AI can achieve in both research and practical applications

If you need resource you can Introduction to Generative AI course on Coursera, You can also audit this course for free. 

If you need an alternative course then Andrew Ng, founder of Coursera and creator of AI for EveryOne specialization also has a course called "Generative AI for Everyone" which is another great resource to learn Generative AI. 


That's all about the 2024 AI Developer RoadMap. In conclusion, the AI developer roadmap for 2024 provides a clear path for aspiring professionals. From mastering programming and core AI concepts to staying updated on ethical considerations, the roadmap is a guide for success. As we enter 2024, the demand for AI skills is higher than ever. 

By following this roadmap, developers can navigate the evolving AI landscape, contribute to meaningful projects, and stay adaptable in a dynamic field. It's not just a roadmap; it's an essential tool for those looking to thrive in the world of artificial intelligence.

Once again Thanks for reading! I have  explained the steps you need to follow to be an artificial intelligence engineer. Still, you maybe need to have more skills than these practical skills like communication, problem-solving, work in a team to have a position in this field.

Other Programming and Development RoadMaps you may like

Thanks for reading this article so far. If you like this AI Developer roadmap then please share the word and share with your friends on social media. I appreciate your kind support.

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