The rise of AI technology like ChatGPT is revolutionizing not only how we do our jobs, but also what types of jobs we can get. For budding technologists or job seekers, the AI boom means there are even more opportunities to have careers that touch this cutting-edge tech field.
In the past year, the number of AI-related job postings has increased across practically every sector in the United States, according to a recent report out of Stanford’s Institute for Human-Centered AI. Put another way: People with AI skills are well-positioned to get hired in today’s job market.
We can help you develop the skills you need to work in AI with our catalog of courses for programmers of all levels. Our new course Intro to ChatGPT will take you under the hood of the AI chatbot’s functionality and get you thinking critically about the ethics of AI.
Read on to learn more about the in-demand technical skills and programming languages that you need to get a job in AI, and the courses and paths that will help you reach your goals.
No surprise here: Coding is a crucial part of most jobs in AI. But which programming languages are the most useful to learn for AI-related work?
The 2023 AI Index Report found that Python was the top skill included across AI job listings in 2022. With Python’s simple syntax and pre-written libraries and frameworks, you can start coding more complicated AI and machine learning concepts faster.
Fortunately for beginners, Python is a great first programming language to learn if you’re brand new to coding. Already have some Python experience? You can take our course Learn Text Generation and we’ll walk you through the process of training a computer to create language using Python.
Machine learning is a subset of AI that uses algorithms to make decisions based on patterns found in data. Our course Intro to Machine Learning will help you understand one of the hottest fields in computer science and the various ways machine learning algorithms affect our daily lives.
You can go even deeper with the path Learn Machine Learning, where you’ll get hands-on practice applying machine learning methods to real-life scenarios. And if you have your sights set on becoming a Machine Learning Engineer, dive into the Data Scientist: Machine Learning Engineer career path. Read this blog to learn more about the types of jobs you can have in machine learning.
Natural language processing
Natural language processing (aka NLP) is how we get computers to interpret, analyze, and approximate the generation of human speech. Everything from your phone’s autocorrect feature to the Amazon Alexa in your kitchen uses NLP in some capacity.
If you want to learn how to make computers act more like humans, try the path Apply Natural Language Processing with Python — it’s a great entryway into AI. Or if your goal is to specialize in NLP, the career path Data Scientist: Natural Language Processing Specialist will teach you the technical skills you need, and set you up with portfolio-ready projects that you can use in job applications.
Both data analytics and AI are concerned with finding patterns in data that inform decisions — but they accomplish this in different ways. Data analysis is the process of collecting and examining data for insights using programming languages like Python, R, and SQL. With AI, machines learn to replicate human cognitive intelligence by crunching data, and let their learnings guide future decisions. We have lots of data analytics courses and paths that will teach you key programming languages and concepts.
If you already know the programming language R, you can take our course Learn Linear Regression with R to learn how to make and interpret linear regression models. (And if you want to get started with R, check out our beginner-friendly R courses and tutorials.)
Math and statistics
Statistics, probability, linear algebra, and calculus are at the core of creating algorithms or interacting with certain machine learning models. Feel like your math skills are a bit rusty? We have courses in core math subjects — like Probability, Linear Algebra, and Learn Statistics with Python— that will help you apply these concepts in a coding context.
It takes a lot of computing power to train and run AI models. (BTW, it also requires a massive amount of energy: Studies suggest that training a large deep-learning model produces 626,000 pounds of carbon dioxide, which is about equal to the lifetime emissions of five cars.)
AI-focused businesses need to consider how they store and analyze the massive amounts of data they work with. Round out your AI knowledge by learning about big data technologies like Hadoop Distributed File System, as well as cloud computing platforms like Amazon Web Services and Microsoft Azure.
Intro to Cloud Computing will introduce you to the basics of cloud computing, so you can better understand the different deployment models and types of cloud services that are out there.
If you’re feeling energized to start your journey towards a career in AI, be sure to explore the courses in these subjects. And keep reading the blog for more advice on finding a job and advancing your career in popular tech fields.