Great to have you reading my first post in 2019! Like some wise man once said; ‘better late than never’. This past year’s been full of ups and downs and a lot of learning and traveling involved. Just a quick recap of what I have been up-to before I go on:
- Attended an in-class Fast.ai Deep Learning course at the University of San Francisco from March 2019 — May 2019.
- Attended UseR Conference in Toulouse, France 2019. (call for diversity scholarships is now open. Apply!)
- Attended the Deep Learning Indaba. Nairobi, Kenya 2019.
- Speaker ACCA Power of Digital Round Table(Machine Learning). Nairobi, Kenya.
- Currently a Student at the African Masters in Machine Intelligence(AMMI) in AIMS Ghana.
Due to my recent admission to the African Masters in Machine Intelligence, I’ve realized that there’s more to machine learning than what I knew before. I came to then learn that there are actually two types of data scientists; Those that are machine learning enthusiasts that don’t really know what happens behind the scenes, and those that know the mathematics behind the machine learning algorithms. I’d say, I was mostly the former and I’m sure most people are and I cannot emphasize more on the importance of knowing what goes on behind all those sklearn libraries that you import.
If you didn’t know; behind all those flashy machine learning products and applications, there’s mathematics and algorithms that make them what they are. This is the main reason why I’ve decided to get back to writing so as to share the important concepts in machine learning as well as learn more by teaching others.
Apart from that, I realized that I’ve just been taking it all in and not pushing out any work which makes it hard for me to measure my progress and show proof of work. I was also re-reading this book called The Visible Expert. This book gives you advice on how to become a well-known expert in your field by employing various methods to produce leads. These methods include creating content through blog posts, articles, videos, speaking engagements, networking, relationship building and focusing on target markets. If your plan is to become an expert in your field, make sure you get yourself a copy of that book.
I’m sure you’ve noticed that most of the people well known for their work in machine learning or in any other field, have either written multiple papers, been invited to speak at several conferences and meets and are well known experts in a specific area be it NLP or Computer Vision etc. So, I recently made a decision to get a broad understanding about every area in machine learning but be an expert in one field which is Natural Language Processing. This decision came right after watching Andrew Ng’s video on career advice and how to read research papers. which I found very useful and has some general advice for everyone and not just for people in the machine learning field. If interested, find it here.
Throughout the next year, I will be writing posts on machine learning concepts that I learn along the way as well as get deeper into NLP. I’m not sure about how frequent I will be writing because of the intensity of my masters program but be sure to expect a post at least once or twice every month. My classmate also started a YouTube channel talking about various machine learning concepts and algorithms so If you’re more of a video than article person, have a look at her channel here and subscribe!
Feel free to also subscribe to my website to get a notification every time I put out a post, or follow my medium page @categitau. I would also really appreciate feedback. It’s been a minute since I last wrote, so my writing is a bit rusty but will improve with time.
Thank you for reading and Happy Holidays!
Originally published at https://categitau.com on December 24, 2019.