Like probably many of you I’m enjoying the Christmas break. It’s been a busy year, with 2019 looking even busier and a two weeks holiday break was what was really needed.
This time of the year is particular welcome as we get to spend more time with our families, do all the things that normally we have no time to do.
This period of the year is also welcome because I can spend some quality time brushing up my skills. For example, during this break, I’ve decided to follow a particular learning path, with the overall outcome to learn various branches of AI, including Machine Learning and Deep Learning. In my line of work, keeping up to date with technology is a must and never like the present time, the pace at which technology is evolving is very fast.
I have to thank the company that employs me for making available a Safari Book Online (SBO) account. I’d strongly recommend any company wishing to have the best human capital to do the same. The quantity and quality of courses on SBO is impressive.
My Learning Path to AI
AI is a generic term which encapsulates several concepts. In my line of work it’s ultimately about machines learning from data, whether with training (Machine Learning) or unsupervised (Deep Learning). If machines have the ability to learn from data and therefore modify their behaviour based on such learning, it’s possible to apply the outcomes of such activities to a number of fields, from medical science, to fighting financial crime, to auto-trading and so on.
AI topics are quite difficult in nature, involving a lot of Math (mostly probability and statistics but also advanced algebra, combinatorial Math and so on). There’s no denying that the pioneers in this space had a lot to do to even get to the basics, but like everything in science, new ideas are built on the “shoulders of giants” and today’s AI landscape looks a lot more approachable than at its inception.
More approachable doesn’t mean easy. While the major public cloud providers like Google, Amazon and Microsoft all have support for AI activities, with targeted services, for any non-basic use case there is still a lot to learn. In the case of Google, they even have created a dedicated hardware processor, TPU, for their TensorFlow library. Some of their services (for example Natural Language Processing (NTLP) don’t even require users to know anything about AI; they offer APIs that provide “AI as a service”, like the overall sentiment of a piece of text (which, for example, can be used to automate some call centre classification, reducing processing costs), or image categorisation.
If today one looks at the AI landscape, there are few areas that emerge as required to be learned:
- Machine Learning and Deep Learning
I’ve decided to start with Python as I didn’t personally know this language at all. In particular I’m going through the following path:
- Learning the basics of Python.
- Learn Object Oriented Programming with Python
- Learn Web Development with Python
- Learn Database management and Data Analysis with Python
- Learn AI with Python
- Learn / Re-Learn Algebra and Probability, potentially some combinatorial math as well.
- Obtain the Python PCEP, PCAP and eventually the PCPP certifications
- Learn Google and AWS AI services, including TensorFlow
- Learn R
Looking at Safari Book Online, below it’s a table of the courses I’m going through:
|Python Basics||Safari Book Online||MTA 98-381: Introduction to Programming Using Python||For beginners, introduces all the basics; it can be studied as part of the MTA 98-381 exam preparation||Yes||***|
|Python Object Oriented Programming||Safari Book Online||Python Beyond The Basics – Object Oriented Programming||An essential course for all those wanting to work with Python professionally. Personally, I’d have liked the course based on Python 3 but it was based on Python 2||Yes||****|
|Learn Web Development with Python||Safari Book Online||Web Development in Python with Django: Building Backend Web Applications and APIs with Django||In Progress|
|Safari Book Online|
Web Applications with Python and the Pyramid Framework
|Python RESTful APIs||Safari Book Online||Building REST APIs with Python|
|Safari Book Online||Building RESTful Python Web Services with Flask|
|Safari Book Online||Building RESTful Python Web Services with Django|
|Python Data Science||Safari Book Online||Data Acquisition and Manipulation with Python|
|Safari Book Online||Master the Fundamentals of SQL with Python|
|Safari Book Online||Working with Big Data in Python|
|Safari Book Online||Data Science Fundamentals Part 1: Learning Basic Concepts, Data Wrangling, and Databases with Python|
|Python and R||Safari Book Online||Learning Path: Step-by-Step Programming with Python and R|
|R||Safari Book Online||Learning Path: R Programming for Data Analysts|
|Python Data Visualisation||Safari Book Online||Data Visualization with Python: The Complete Guide|
|Python Data Science||Safari Book Online||Python Data Science Essentials|
I will update the list as I go along. I hope you found this article useful.
How about you? What does your learning path look like for 2019?