Python, Artificial Intelligence, and Machine Learning…Oh My!

Resources for Learning

About a month and a half ago (it is early September 2023 as of the writing of this article) I decided to become familiar with and learn a bit about Artificial Intelligence (AI)/Machine Learning (ML). After taking a few free online classes, watching some YouTube videos of use cases, and reading numerous articles, it’s easy to say my AI/ML interest developed into an “I need to learn more as AI/ML is becoming and will continue to be integrated in all aspects of work – for the IT professional, the software developer, and the tech end user.” ?Additionally, from my perspective, it’s cool. Since this epiphany I’ve found several great resources for AI/ML neophytes (like me) who want to learn. I’d like to share these resources with this community. Please note this is what I’ve found and used; I’m know there are additional resources available.

First, I’d like to share resources I’ve used and am using to learn Python. ??Why Python you ask? ?I have learned that Python is used a ton in AI/ML environments. This makes sense, in my opinion, for several reasons: Python has a large library of tools that can be leveraged in AI/ML environments; Python is not super hard to learn (I’m learning now and it’s fun – in a weird sort of way); Python is used a great deal in data analysis/data science – there seems to be low transition barrier and correlation between these disciplines and AI/ML.

?Enough rambling, here are the Python resources I’m using or have used:

1. Harvard University: CS50’s Intro to Programming with Python (via edX). This course is taught by Dr David Malan. Dr. Malan brings great energy and insight to the course. The course includes practice problems. I recommend you take the time to, at a minimum, listen to and follow along with Dr. Malan’s lectures.
2. Scientific Computing with Python (via freeCodeCamp) The lectures are given by Dr Charles Severance (University of Michigan). ?Dr. Severance does a wonderful job explaining the basics of Python - practice problems included. Again, I recommend at a minimum you listen to the lectures. ?
3. Python Crash Course book – author is Eric Matthews. I am currently working through this book and can’t recommend it enough as the structure works well with my learning style.

Second, the following are resources I’ve used, reviewed, or will be working through – all are “beginner” AI content focused:

1. Career Essentials in Gen AI - Career Essentials in Generative AI by Microsoft and LinkedIn. This includes five courses that I feel do a solid job laying the foundation for AI learning.
2. Generative AI for Business Leaders - Generative AI for business leaders (linkedin.com). High level AI review type of course.
3. Microsoft Learning Paths: Transform your business with Microsoft AI - Training | Microsoft Learn – this is a 4-module learning path. Good materialAI Builder Challenge: Collections - cloudskillschallenge | Microsoft Learn – this is an 18-module learning path. I haven’t gone through all of it (yet) but it is worth the time.
4. Google Cloud Skills boost - - Google Cloud Skills Boost always good to get varying perspectives on topics. And I liked this material.Intro to Gen AI Learning Path – I took this and liked the content.Generative AI for Developers Learning Path – I plan to take this. Machine Learning Engineer Learning Path – I plan to take.
5. Intro Semantic Kernal: Building AI-Based Apps - Introducing Semantic Kernel (linkedin.com)? Solid introduction to Microsoft’s Semantic Kernal.
6. AI for Beginners - AI for Beginners– This content is provided by Microsoft. I haven’t started this yet, but I think it will be good. Content includes text material and practice material. You will need a GitHub account.

Third, as I have learned, Machine Learning is a subset of Artificial Intelligence and therefore, in my opinion, not a bad topic to learn about. Below are a few resources:

1. As referenced above Google Cloud Skills boost - - Google Cloud Skills BoostMachine Learning Engineer Learning Path – I plan to take.
2. Microsoft’s Machine Learning Challenge - Machine Learning Challenge. This includes 13 learning modules. You will set up an Azure learning environment testing out the ML features. I enjoyed this.
3. Microsoft’s ML for Beginners – I’ve started working through this and feelings like I will enjoy. You will need to have a GitHub account.
4. Coursera – Machine Learning Specialization course. Though I haven’t taken this course, I’m thinking about it. The course is taught by Dr. Andrew Ng (Stanford University). From all I’ve read, Dr Ng is a highly regarded expert in Machine Learning. The learner will need an understanding of Python prior to taking this course. There is a cost for this course.Additionally, Dr. Ng runs Deeplearning .ai (DeepLearning.AI: Start or Advance Your Career in AI). This site offers free and paid AI/ML courses. I really like the blog posts. Dr Ng also publishes a newsletter, “The Batch”, ?that includes insight into new AI/ML issues, use cases, job market, etc.
5. Note- as you get deeper into Machine Learning it will help if learn/refresh your knowledge of Linear Algebra, Statistics, and a bit of Calculus. This advance is based on my readings. ?

Lastly, to supplement my learning with some “real world” coding I follow a few YouTubers who walk through “learning” projects for Python, AI, and ML. Here are links to their channels – and obviously there are others you may follow:

1. Tech with Tim - Tech With Tim - YouTube
2. Nicholas Renotte - Nicholas Renotte - YouTube
3. Rob Mulla - Rob Mulla - YouTube
4. Thu Vu - Thu Vu data analytics - YouTube
5. freeCodeCamp.org - freeCodeCamp.org - YouTube

I hope this information is helpful. If there are resources you have used or continue to use that are not listed above please share within the comments.

Thanks for reading and keeping learning.