Getting Started in AI , Data Science, and Machine Learning

Getting Started in AI , Data Science, and Machine Learning

Lately, I have been getting many questions from folks who don’t have an engineering or computer science background, especially women, asking how they can get started in the Artificial Intelligence (AI)space. The tech industry has been traditionally unwelcoming to those from non-technical backgrounds. This combined with widespread perception that you need to have an advanced degree and engineering or mathematical background to work in AI, further obscures the breadth of opportunities in this space.

Last month, I posted a list of AI jobs that don’t require a technical degree or PhD to highlight the diverse career pathways. Gaining additional skills and getting better informed about AI can lead to new opportunities whether you are transitioning from a different field or want to advance in your current role. However, for many, going to school full-time is not a viable option and attending classes in person isn’t possible because of logistical reasons.

The good news is there are many introductory courses from leading universities and organizations offered online, which make it easier to get upskilled. Most courses listed below can be audited for free, some are part of a program series, and all offer a verified certificate for an additional fee.

So let’s get started! Here are 3 online introductory courses that offer an overview of AI basics without needing any programming background or prior information.

AI for Everyone: Master the Basics In 2012, MIT and Harvard University launched edX, a nonprofit organization to provide an open online platform for university courses. Currently over 160 universities, tech companies, and other education organizations offer courses through this platform. This course on AI basics is offered by IBM and covers Artificial Intelligence (AI) key concepts including machine learning, deep learning, and neural networks.

AI For Everyone Offered by and taught by Andrew Ng, this course reiterates that AI is not only for engineers and is specially designed for non-technical people. In this course, you will learn the meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science. What AI realistically can and cannot do, and how to navigate ethical and societal discussions surrounding AI.

Introduction to AI The Elements of AI is a series of free online courses created by MinnaLearn and the University of Helsinki to to encourage as broad a group of people as possible to learn what AI is, what can (and can’t) be done with AI, and how to start creating AI methods. The courses combine theory with practical exercises and can be completed at your own pace.

Here are 5 courses that teach the basics of programming languages like R, Python as well as statistics, typically required for data science and AI/ML roles. These courses can also help you get ready for a full-time program.

Data Science: Machine Learning This is part of the Professional Certificate Program in Data Science offered by Harvard and you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.

R Programming Fundamentals This course offered by Stanford covers the basics of R: a free programming language and software environment used for statistical computing and graphics. It’s widely used by data analysts, statisticians, and data scientists around the world. This course covers an introduction to R, from installation to basic statistical functions.

CS50’s Introduction to AI with Python This is CS50x, Harvard University’s introduction to the intellectual enterprises of computer science and the art of programming for majors and non-majors alike, with or without prior programming experience. An entry-level course taught by David J. Malan, CS50x teaches students how to think algorithmically and solve problems efficiently.

Python Basics for Data Science This Python course provides a beginner-friendly introduction to Python for Data Science. Practice through lab exercises, and you’ll be ready to create your first Python scripts on your own!

Introduction to Statistics for Data Science using Python This Statistics for Data Science course is designed to introduce you to the basic principles of statistical methods and procedures used for data analysis. It does not require any computer science or statistics background but is strongly recommended to take Python for Data Science before starting this course.

Ideally, ethical considerations and principles should be baked into the AI curriculum but that’s not often the case so here are 3 courses that teach you how to build and deploy AI ethically.

Ethics in AI and Data Science In this course by Linux Foundation, you’ll learn how to build and incorporate ethical principles and frameworks in your AI and Data Science technology and business initiatives to add transparency, build trust, drive adoption, and lead with trust and responsibility.

Data Science Ethics Offered by University of Michigan, this course focused on ethics specifically related to data science will provide you with the framework to analyze these concerns. This framework is based on ethics, which are shared values that help differentiate right from wrong. Ethics are not law, but they are usually the basis for laws.

Detect and Mitigate Ethical Risks This course by CertNexus is the third one in the Certified Ethical Emerging Technologist (CEET) professional certificate, designed for learners seeking to detect and mitigate ethical risks in the design, development, and deployment of data-driven technologies.

I hope this is helpful in getting off to a good start on your AI journey. Check out this spreadsheet for more courses. I’ll keep adding more as I find them but reach out if you see any missing or have feedback on those you’ve taken.

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