Learning is my biggest hobby, and I am always looking at ways to improve both my skillset and learning methods. As my main professional interest is using NLP to build recommenders, I have been using the kinesthetic learning approach in the past few years, to learn while actually coding personal projects using NLTK, SpaCy and various Japanese language modelling libraries. This approach, while fun and provides instant feedback, lacks a foundational structure. So in the past few months, I ventured into the subscription models of both Coursera and Edx, as well as supplementing with several knowledge platforms to pick up foundational theories in math and NLP. This post is a short recap of what I learnt in these learning platforms. It is intended as a piece of a bigger series on knowledge management.
platforms I explored
| What they offer | My Recommended Courses | ||
|---|---|---|---|
| Coursera | paid subscriptions offer autograders (for both programming and written assignments), hands-on-lab and quiz. Dashboard makes it easy to track your progress in a specialization (a series of courses) or individual course. | recommended courses | |
| edx | paid subscriptions offer hands-on-lab and assignments. I do not notice autograding as programming outputs are used to answer quizzes instead. Dashboard makes it easy to track your progress in a specialization (a series of courses) or individual course. | ||
| deeplearning.ai | various short courses taught by industrial practitioners with hands-on-lab on a dual pane setup, so that you can try what is being explained simultaneously. They also host a very responsive forum for all their courses on Coursera. | Your Guide to Generative AI Courses - DeepLearning.AI | |
| Linkedin Learning | many libraries provide free subscription access. Quality varies. | ||
| Harvard | Courses | Harvard University | CS50's Introduction to Artificial Intelligence with Python | |
| Stanford | depends on the professor, some courses provide link to free courseware (book, weekly slides, videos, assignments). Breaking down everything into a module basis makes it easy to follow along | • CS 276: Information Retrieval and Web Search (stanford.edu)• CS246 • Speech and Language Processing | |
| MIT OpenCourseWare | -MIT OpenCourseWare - MIT Open Learning Library | -MIT Deep Learning 6.S191 - Matrix Methods in Data Analysis, Signal Processing, and Machine Learning | |
| video | Lots of animations, good for visualizing math concepts. Good channels include 3B1B, Serrano Academy, StatQuest, DeepFindr | -Generative Machine Learning - Natural Language Processing and Large Language Models - Essence of calculus - Essentials of Linear Algebra - Neural networks - video https://www.youtube.com/embed/ | |
| Blogs | written by practitioners on a single topic. Can delve deep but sometimes lacks context | towardsdatascience, company engineering blogs |
Since I spent most of my time on Coursera, I will describe their learning approach and my tactics to improve memory retention and practical usage.
Coursera structure
short videos
Unlike Edx which tends to have very long videos, Coursera chops videos into small chunks, usually under 6 minutes (that seems to fit the modern brain’s attention span). The video content varies per provider, with some university offerings being very static (professor just talks in front of the camera), to some lecturers using visually engaging animations and decks (e.g., IBM). Content depth also varies, with some topics just scratching the surface (e.g, some of the IBM AI ones), while others being similar to an undergraduate course.
quick quiz in a video
Sometimes in the middle of the video, a short quiz will pop up. At first I found these very distracting, but after knowing some learning theory, I kinda got their purpose to do a quick recall to reinforce retention, or to clear up some confusion immediately. These both assist in long term memory.
hands on lab
Hnads-on-labs are usually placed in the middle of a weekly module, right after a new concept is introduced. These are good resources as they provide a timed sandbox in a vendor environments (GCP, AWS, IBM Watson) for you to practice what you just learnt, and try out different things. These are valuable not just for getting your hands dirty, but also for evaluating these commercial cloud environments for your real projects without upfront payment.
In general, IBM labs are very good. For example, their Tensorflow labs break down each concept into very small manageable labs, and the markdown explanations in the Jupyter notebooks are easy to understand. Their Generative AI labs are also very generous with a customized Watson.ai playground for you to test many different models, along with prompt engineering or programming coaching on a side-by-side explanation pane. I think their labs make up for the content depth deficiency in their videos.
module quiz
This serves as a quick recap of what you learnt in this week. Good to check proficiency before jumping into programming assignment (if any), and helpful for retrieval practice too.
programming assignments
Most technical courses will have programming assignments. Not all of them have autograders (i.e., you submit your code and the machine will check for accuracy and grade you).
The deeplearning.ai assignments usually have very thorough unit tests that can catch most logical bugs you have. Since all of their assignments are in a step by step format (i.e., you implement a function X, which is then used by a later function Y, then by func Z…..), with usually 10+ steps, unit tests along each step is a good guardrail to ensure you’re heading in the right direction and can save a lot of debugging time in later steps.
However, sometimes putting up too many guardrails dampens enthusiasm and transfer motivation to external (to complete the assignment only), which means you are learning not for yourself. And since the programming assignments are very long with rigid structure (fill in some lines of implementation, while calling pre-configured utility procs), it is very much unlike what you would implement in your own project. Sometimes in the middle of the assignment, you might already forget all the functions you implemented before (if you do not use an external IDE that has something like Intellisense outside of the assignment platform). Towards the end, it just feels like a chore to complete. Unlike building your own app, where you can implement classes to encapsulate common functions, and can freely open multiple code panes to view code definition and verify, Jupyter notebook in a classroom setting has its pros (easy to follow the teaching path with a combo of both markdown text and code) and cons (no Intellisense, cannot open the same file in multiple panes to refer to other sections).
writeups
In most of the business and marketing courses I took, you are expected to come up with real world examples that align with the concepts introduced in the coursework, and articulate the pros/cons of the example cases, or propose alternative actions. In product management courses, you are expected to follow the exact industrial practices on product development from ideation to design to experimenting to preparing a launch. All the deliverables are submitted in the form of reports, charts, drawings or video presentations. Some are peer reviewed, while some textual content are rated by AI, called Coach in Coursera. I tend to find the AI grader more consistent than peers, as it closely follows the published rubics in grading, and can often offer useful feedback for your work. You can switch to human grading if you are not pleased with its results.
capstone projects
A lot of the courses contain capstone projects to wrap up everything you learn. These are often mixed-modal, with programming, writeup, presentations to build an end-to-end solution. Sometimes a case study is provided, and sometimes you are asked to provide an industrial problem you face at work. Even if the course content might not be optimal, doing the capstones is extremely helpful as they often resembles industrial scenarios, and mixing formats and reconnecting them in a final delivery activate various parts of our learning brain, to build and connect new neural paths.
forums
One thing that seems to be lacking in MOOC platforms is the real interaction among students and lecturers. Coursera’s own forums usually have active student participants, but the lecturers might not be active at all. For some courses that have confusing instructions or faulty autograder, it is hard to raise your hand and ask. deeplearning.ai, which hosts its own forums for all its Coursera courses, usually fare better in terms of responsiveness. Still, you might not expect the same level of interaction from an in-person setting, or stackoverflow.
what courses are good
Coursera has more choices than edx. If you’re aiming for the latest AI development such as LLM or Generative AI (whether from an application development or theoretical angle), Coursera has more to offer due to its association with deeplearning.ai. OTOH, edx has strong offerings on math and engineering courses too. Some institutions put identical offerings on both platforms.
Here are the Coursera courses I recommend in AI, PM, Business and Marketing. These courses really give you a good grasp of the foundations, supplement by assignments that align closely with what you learnt.
| AI/CS | Deep Learning |
|---|---|
| PM | -AI Product Management <br>- Digital Product Management <br>- Software Product Management |
| Math | -Mathematics for Machine Learning and Data Science <br>- Analytics for Decision Making <br>- Solving Complex Problems <br>- Model Thinking |
| Business | -Business Strategy <br>- Strategising: Management for Global Competitive Advantage <br>- Strategic Leadership: Impact, Change, and Decision-Making <br>- Foundations of Management |
| Marketing | -Data Science for Marketing <br>- Marketing Strategy <br>- Market Research |
course hacking
You do not need to follow the course in a sequential manner. What matters is how much information you can extract to make them your own knowledge. I use the following hacks to maximize the payoff of both my time and gained knowledge.
use courses as curation/pointers to an unknown world
The more courses I take, the more I see that MOOC platforms are best viewed as a curator or a tour guide. It’s just like a first time visitor to Japan who joins a guided tour so that he doesn’t need to flip through Lonely Planet or numerous travel websites/blogs to piece together an itinerary. In Coursera’s case, for example, if I have absolutely no idea about corporate finance, enrolling in this specialization will give me a bird’s eye view on what are the building blocks in this area, what should I learn to have a basic grasp in this field so that I can read corporate finance reports or listen to earning calls with my own judgment. It’s similar to the quick reading method, in which you get a skeleton of what the author covers by first scanning the TOC. In this scenario, it is reading the course outlines and objectives to understand what you are expected to learn and achieve, then use the quick quiz method to orient.
Most of the courses are solid but some really leave more to be desired. Using them as a guidance (e.g., in a particular domain, here are the most important algorithms to consider, and here are governing rules) as well as a sandbox (their practice labs are quite good to get your hands wet immediately after learning something new), they are worthwhile. However, don’t stop there but look beyond. Use the course structure as various pointers to explore outside resources. Or better yet, do a small project on your own using your newly gained skills. You will often find that you overestimate what you know. It’s often through doing that you have a real assessment of your skill level.
use quiz as orientation
Before I start a module, I like to use quiz to quickly check if I’m familiar with the subject. Luis Serrano from Mathematics for Machine Learning and Data Science and Professor Konstan from Recommender Systems Specialization both recommend this approach. Even if you can’t make out the quiz at first try, you can use them to orient yourself. This is because usually the most important concepts will be tested. So by preemptively doing a quiz, you know the important things to look for, and in the coursework you can be more focused, like a hawk knowing both the landscape and what to hunt!
what’s next in my learning journey
Overall I am pretty happy with what I got from the Coursera subscription. The classes tend to be a sound navigator for my professional journey. In the next few months, I plan to take more courses in edx to compare, and will definitely supplement with the excellent video https://www.youtube.com/embed/channels I listed above. Most importantly, I will implement all the theories I learnt in my own hotel recommender project and report any interesting findings in my next blog posts!