I'm reading a 'Personal Ph.D.' curriculum consisting of data science, linguistics, and film studies to learn more about these topics and to build a tool that allows folks to enjoy film + tv quotes and scenes better.

I'll explain more about the concept and the motivation behind this another time.

Data Science

In this first "quarter," I'm taking Andrew Ng's Machine Learning course on Coursera.

The course has lectures and quizzes. The lectures are technical but smooth. The quizzes, from Linear Regression on, are in Octave. I've elected to skip the quizzes because I can't be bothered to learn another language to do machine learning. I use Python in my day job, so I want to focus any improvements in my coding abilities on Python.

A few weeks ago, the job put us on with a Machine Learning course in Datacamp. The class is even more hands-on than Ng, and the exercises are Python-based. I'm getting the ML guts from Coursera and the hands-on from Datacamp.

So far, I've worked through some supervised methods and the underlying math behind them. While I knew about linear and logistic regression and their intuition, I'd never drilled the algorithms in Python, nor had I gotten into the math under the hood of gradient descent like Ng has me doing.

Linguistics

Initially, I planned only to do one course this 12 week year. The first two or three weeks working a day job and doing the Machine Learning lectures proved to be thick enough for me to be content with only taking one course. However, after hitting a slump several weeks into the 12 week year (it was getting tough to log onto Coursera and listen to Dr. Ng beat linear algebra into my head), I permitted myself to cut the linguistics class on. That definitely helped me get my swagger back, create momentum, and build consistency.

So far, I've learned about the following concepts:

• History of language
• Definition of language
• Phonetics and Phonology
• Consonants

For the rest of the quarter, I want to make sure I'm hunting for ways to apply what I learn to my linguistic interests–African American English (AAE), slang, and memorability–and Natural Language Processing and Film.

Film Studies

I'm committing to watching one movie a week for the third curriculum component. I thought this would be a solid way to get tighter with film until a future quarter when I take a film studies course. While this method is not quite deliberate, getting more movies under my belt will help later in my studies.

So far, I've watched:

• The Bourne Ultimatum
• No Time to Die (I immediately committed to watching a film a week after seeing this. I knew I would be missing out if I wasn't consuming the film medium regularly.)
• The Silence of the Lambs
• Transformers
• The French Dispatch
• The Sum of All Fears
• Dune

Currently, the only way I intentionally watch films is to take notes of quotes or scenes I like; however, I typically reserve this for my second viewing to ensure a more *organic* experience on the first run.

Warm-up concept

Early in this 12 Week Year, Keith sent me a video from Thomas Frank about 'warming up.' Essentially, preparing ourselves for a task by easing into it with an intentional activity can improve our performance on the end task.

Reading the data science articles that I'd saved helped me ease into and get excited about the Andrew Ng Machine Learning course and my day job. I'll continue to run through my stash of data science articles before working on anything data science-related.

Personal Ph.D. overview post coming soon.