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0-60 in AI for free: How to get smart in AI. Fast.

0-60 in AI for free: How to get smart in AI. Fast.

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To some of us artificial intelligence may seem like an obscure science. Maybe you think you need a PhD to correctly implement it. Good news! You don’t. I thought it would be helpful to include some of the same resources that helped me get an education on artificial intelligence, quickly. Below are the same resources I used to go from AI curious to practicing. These are all available to the general public for free and show that you can start understanding AI without the graduate degree.

With the increasing popularity of AI and Data Science, a plethora of free resources have flooded the internet. Many resources are technical in nature; however, even a non-technical person can gain a respectable understanding of AI applications. The resources described will go from less technical to more technical in nature so that anyone can get up to speed on AI.

Industry Analysis

McKinsey Featured Insights on AI

McKinsey is an excellent non-technical resource. If you are a strategic leader who wants to stay afloat on AI developments in different industries, McKinsey offers world-class research. Apt for any professional interested in AI, you don’t need to know any math to start learning. These reports are written for C-level executives and strategy leaders to stay informed on AI use cases and potential threats in their industry.

Deloitte Insights on AI

Similar to McKinsey’s AI Insights, this vast collection of articles and media will keep you busy. Deloitte delivers timely insights into how AI plays a key role in business and how it will transform the world. Easy to read yet informative, these articles will give you something to talk about at your next business dinner.

MIT Sloan Management Review - Data Analytics

True to its reputation for innovation, MIT couples research expertise with valuable business advice from global thought leaders. This high quality resource presents ideas you may never have thought of such as the value of your organization’s data. Interesting reading for the intellectually curious business person, this is one of the more practical, yet strategic, resources.

Blogs and Publications

Data Driven

You’ve understood the potential of AI to transform your business. Now you wonder where to start. This short (and free) guide is a great introduction on using data within your organization.

The authors are experienced data scientists; they provide interesting examples of data harnessing in different companies and the advantages to creating a data driven organization. Don’t let its short length deceive you. This is an excellent primer on how to think about and structure a data driven culture. Even though it is not a definitive resource, it gives you an idea of what questions to ask.

Machine Learning Mastery

If you don’t want to get a PhD, the next best thing is to learn Machine Learning from someone who already has one. This post goes into what all these AI-related terms you’ve been reading actually mean and how they relate to each other.

Machine Learning Mastery - Part 2

Now that you’ve (hopefully) understood different AI terms and AI’s potential to transform industries, you are most likely excited about applying AI. This second part provides a framework to do just that. Once you’ve applied this framework, you should be ready to start getting more technical.

Making a Simple Neural Network

This post explains one of the most important developments in AI: Neural networks. Neural networks were created by modeling the human brain with the assumption that if we can model it correctly, we can create systems that are as powerful as the brain.

I highly recommend taking time to go through the article and understand how neural networks actually work; it shows some coding, but you can skip the code and still learn a great deal.

Machine Learning for Humans (Medium)

Consider this your “AI 101” textbook. Or at least the first two sections… Machine Learning for Humans is a (beginners) technical guide to AI, written for the general audience. Whether you are technical and want to get up to speed on machine learning quickly, or a non-technical explorer looking for a primer, this guide is for all. This 1-2 hour read tours basic concepts in probability, statistics, programming, linear algebra, and calculus, yet no background on the subjects is required. I recommend it to anyone in a strategy position, looking for a solid technical foundation. I found that an understanding of the syntax of AI helps prepare you to see how everything connects. The knowledge I obtained from reading this document helps me daily how to apply machine learning concepts to new ideas and case studies.


With you will quickly develop the skills you need to apply AI models in your organization. This course places as much importance on the teaching technique as on the actual content. Also, it use Kaggle datasets, which is a good thing: Kaggle is the go to site for data scientists, where they meet to compete and learn from each other; so you will definitely be doing very practical and applicable work.

There is one prerequisite have know how to code. If you don’t, I highly recommend Getting Started with Python on Coursera. I encourage you to learn since even a basic understanding of coding can go a long way and the professor is very entertaining.

Machine Learning Stanford Course on CourseraI

t’s time to get more technical. This is one of the best introductions to machine learning. Also, it is taught by Andrew Ng, one of the most influential minds in AI. You can get through this free course with only high school mathematics, but it gets very technical very fast. It won’t be easy, but Andrew does a great job of explaining difficult concepts.

If you find the technical side more appealing, consider Andrew Ng’s Deep Learning specialization on Coursera. It is a more comprehensive set of courses that focuses on applying and improving neural networks.Harvard CS109 Data Science Course

If you want to get very technical. Harvard’s Data Science course is one of the most comprehensive free online resources you can find on AI. Going from probability and statistical methods to regression and machine learning models, you will be well-prepared to:

  • Gather and clean data
  • Use data management techniques to store data in cloud infrastructures
  • Use statistical methods and apply statistical analysis
  • Communicate the outcome of your analysis

The course prerequisites are programming (I would recommend basic/intermediate Python) and basic statistics. If you have the time to go through the whole course, you will be well on your way to becoming a data scientist and implementing AI in your organization.

Feel free to choose any of these resources depending on your AI goals. If you wish only to stay informed on developments in AI, you should bookmark the McKinsey site and refer to it regularly. If you want to start making a difference in your organization, take a look at the other resources and give KUNGFU.AI a call, we would be happy to help. If you have a non-profit, take advantage of KUNGFU.AI’s program AI for Good #Helpingforward, we hold office hours the last Friday morning of every month in Austin for those who want to use AI to improve people’s lives.

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