First challenge – 79 moons (at least) on Jupiter inspire a 79 day challenge. My goal is to achieve week of a Coursera or Deep Learning. ai course each day for 79 days (or more!) I will also do my best to redo each lab with a new dataset or different application, and post about that here. Also I will summarise how I went (and problems encountered) as I go.

First up, shopping spree. I secured myself a gorgeous little Microsoft Surface, which almost feels like an extension of my hands, even more so than my mobile device. I also prime’d a Shokz OpenRun Bone Conduction Sports headphone for listening to lectures on the go. Somehow I need to figure out how to make this work on a daily basis amidst a full-time corporate job and my family (including our very intense 9-month-old infant) and trying to lose all that stubborn baby weight.

A simple but helpful trick I’ve discovered is to block my direct corporate stakeholder calls into 8 calls over 4 hours from 6am to 10am and type meeting notes during the calls religiously (no AI transcripts allowed at our company, unfortunately). I find myself so much more motivated and engaged with shorter meetings, more specific outcomes, and the fact that half my work day is over by 10am. 10am I hit the gym down the road and am back online to study just after 11. I also do a similarly clustered afternoon session, with some blocks targeting my reporting, management deck updates and call follow-ups. Wherever possible I do a 30 minute study session or call on the stationary exercise bike (my trusty Schwinn). It certainly feels far more productive than previously, and I have so much more energy now I’ve learning something I’m passionate about. Finally, I’ve replaced my evening K-drama or wuxia hit with some of the less involved lectures, even if just an hour. And whilst it’s not Condor Heroes, Andrew Ng’s material is pretty entertaining. This gives between 2-5 hours daily for learning.

Support and study groups are also a key part of getting ahead and taying motivated. I find myself a tutor who just graduated from a top US university in mathematics and machine learning, plus another 3rd year student to practice business applications with as I go. My fellow Deeplearning.ai students and I also set up a Whatsapp group to share reources and lab case studies. I’m excited to hear from the viewpoints of a diverse array of students across countries, ages and industries and hope the group momentum kicks off.

Finally, I’ve approached one of my husband’s friends, who leads a cutting-edge data science team at a fast-growing eCommerce company. He agrees to ask to be a mentor, chatting every couple of weeks about direction, real-life applications, and industry pain points.

Next, start learning. I decided to start by familiarising myself with Python and enrolled in 2 Coursera crash courses on Python. I’ll post about each course in a separate blog. GTP also helps with debugging and explaining my mistakes but know I’ll need to understand the concepts to repeat on my own. Finding that balance is going to be an interesting journey and indicative of times ahead.

Turns out, one needs to set up their environment to work on these courses, and it’s not as straightforward as hoped. Here’s a list of what I’m starting out with:

• Jupyter notes for data projects and labs (NOT the lite version, unless you are in search of more problems than Jay-Z)
• Gitbash (for talking to the terminal instead of using the CMD prompt)
• Visual studio: yet to figure out how exactly this differs from Jupyter labs, but will update when I do
• RStudio: useful for stats. Had a little issue finding the SVN link until my tutor advised downloading VisualSVN Server. No issues connecting with Github after this. The other issue with R studio was that Latex wasn’t installed – and it wasn’t obvious to me that the solution was to download basic-miktex-23.4-x64. Anyway, after a frustrating 30 minutes on stack overflow, I managed to ‘knit’ a dummy R markup file.
• Chat GPT professional subscription, plus I load my credit card in the API section. Fingers crossed I’ll figure out how to use it soon.
• A kaggle account
• A Github account (I connect this with my RStudio) and Gitbash
• I try to sign up for Amazon SageMaker and Google Cloud but neither is easy to navigate for non techies. Azure looks very tempting but I think I’ll save the enticing 30 day trial until I can do more with it. All three look like they have some very helpful courses. I’ll do a post on the pros and cons later when I have the basics down.

I