Learning Machine Learning with Upgrad

In the last 10 months I’ve been doing a master’s degree on machine learning with Upgrad (link). It has been a very good journey, very enjoyable. I really like it a lot. The opening webinar back in October 2020 was fantastic. They talked about various applications of AI such as image recognition for blind person, chest X-ray diagnosis, NFL video advert analysis, Makoto Koike cucumber, Alpha Go Zero and Volvo recruiting car. Everyone was assigned a student mentor who guides us through our journey and answer our non-academic questions. We have technical assistants who answer our academic questions (we have a discussion forum too). We learn primarily through videos (which suit me a lot as I’m in the UK with different working hours to India) and their learning platform is very good. Every week we have doubt resolution sessions (optional) where we can ask questions to real teachers (their teachers are very good in explaining difficult concepts so they are easy to understand). A lot of webinars where industry experts share their real world experiences on AI.

The thing I like best is the small group coaching where we learn in a group of eight, coached by an industry expert. My coach is from Paypal, the same industry as me (I work in asset management in London). The sesson is interactive where our coach explains things and we can ask questions, and it is always practical, often discussing the “notebook” (meaning the Python code for those who are not familiar with Jupyter). My mentor is an expert in ML and a very good teacher. We are really lucky to he’s willing to spend time coaching us. Sometimes we had a one-to-one discussion with our coach. At one time (just once) we students thaught each other, we learned from one another. But everyone was also assigned an industry mentor, with whom I discuss my job in the real world and my blog, and my aspirations/ideas in ML. Most students are looking for a job in ML and received a lot of guidance from their mentor. I’m not looking for a new job, but I’m very grateful to have a very experienced mentor. My mentor is from Cap Gemini, an industry leader in AI with 25 years of experience (13 of which were with Microsoft). Really lucky that he’s willing to spend time mentoring me.

In the first month I was learning Python and SQL, covering data structures, control structures, pandas, numpy, data loading, visualisation, etc. all on Jupyter notebook. I’m a SQL and BI veteran but I rarely do coding at work. I mean real coding, not SQL, ETL or BI tools. The last time I did real coding was 10 years ago (Java) and before that it was 20 years ago (C#). When I was young I really liked coding (Basic, C++, Pascal) and this Python coding with Upgrad really took me back to my childhood hobby. I really enjoy coding in Python as part of this course.

Then I learned about statistics and data exploration. I did Physics Engineering at uni so I did statistics and learning it again was enjoyable. The teacher was really good (from Gramener, link) and gave us real world examples like restaurant sales, securities correlation and electricity meter reading. Also learned about probability, central limit theorem and hypothesis testing. All these turned out to be come very useful when applying machine learning algorithms. The assignment was real world cases, such as investment analysis and loan, and the fact that they were in finance made me enjoyed them more.

Then for a few months I learned with various ML algorithms such as linear regression, logistic regression, Naive Bayes, SVM, Decision Tree, Random Forest, Gradient Boosting, clustering and PCA. Also various important technique such as regularisation (Ridge, Lasso), model selection, accuracy, precision. Again the assignments were real world cases such as predicting house prices, how weather affects sales, and telecommunication industry.

Then I learned about natural language processing (NLP) which was very different. All the other algorithms were based on mathematics, but this one is based on languages. It was such as an eye opener for me to learn how computer understand human languages (I wrote an article about it: link). And now I’m learning neural network, which is the topic I like most because it is the most powerful algorithm in machine learning. We started with computer vision (CNN, convolutional neural network, link) and now I’m studying RNN (Recurrent Neural Network, link) which is widely used for stock market analysis and any other sequential data.

I feel lucky I studied Physics Engineering in uni, because it helped me a lot in understanding the mathematics behind the algorithms, especially the calculus in neural network. I’ve done a few ML courses on Coursera (see my article on this: link, link) but this Upgrad one is way way better. It is a real eye opener. I can now read various machine learning papers. I mean real academic research papers, written by PhDs! A few years ago I was attending a machine learning “meetup” in London. Meetup is an app where people with similar interest gather together to meet. Usually the ML meetups were in the form of lecture, i.e. 1.5 hour session in the evening where two speakers explained about machine learning. But this time it was different. It was a discussion forum of 10 people and there was no speaker. Everyone must read a paper (it was Capsule Neural Network paper by Geoffrey Hinton) and in this meetup we discuss it. I didn’t understand a thing! I did understand neural network a bit, but I had no background in CNN so I could not understand the paper. But now I understand. I can read research papers! I didn’t know that I would be this happy to be able to read machine learning papers. It is really important to be able to read ML papers because ML progresses so fast, and the research papers provide superb sources on the latest invention is on ML.