Introduction to the Command Line

My hope for this post is to lower the barrier to entry for using the shell for people who are otherwise overwhelmed by not knowing where to begin. I will outline a series of commands that will allow you to become comfortable with the basics of the command line on unix/linux/Mac and learn enough to be able to explore and learn more about it on your own (think of this as your launchpad). Read more Introduction to the Command Line

Study Progress

ISLR: Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani, ISBN 978-1-4614-7137-0
Hands-on: Hands-On Machine Learning with Scikit-Learn & TensorFlow by Aurélien Géron, ISBN 978-1-491-96229-9

Created my first database replication server, a Postgres replication server from server to laptop following book “PostgreSQL Replication” by Hans-Jürgen Schönig and Zoltan Böszörmenyi.

Wrote an API using python, falcon, and psycopg2 for storing data from garage sensors into a postgresql database.

Build LSTM using Keras on TensorFlow for predicting the number of parking spaces available in each parking garage.

On parking garage data, added circular statistics to hour of the day and day of the year, and increased accuracy on generalized linear model by 1% from 77% to 78% using h2o Flow. Started learning the basics of TensorFlow, computational graphs, constants/variables/placeholders, sessions.
Reading Hands-on, chapters 2 (data processing, scikit-learn pipelines), 9 (intro to TensorFlow), and 14 (RNNs and LSTMs).

SpotMe, viewing and cleaning parking garage data so that it can be used to train a machine learning model. Learned about a tool called

Started working for SJSU student-led startup SpotMe Solutions.

ISLR Chapter 4 Logistic Regression (page 136).
Handson-ML Chapter 2 End-to-End Machine Learning Project (page 49).

Potential resources:
Things on my links page ( (Deep Learning Coursera Specialization by Andrew Ng).