Learning With Data

// Learning With Data

May 18
Top Strategies for Getting a Data Science Job with Ben Taylor
In this video, I discuss the top strategies for getting a data science job with Ben Taylor. Ben Taylor is the co-founder of Zeff - an AI company that recently sold to Data Robot. Sorry that I am still working on getting the lighting and camera to look good on my end! The question the video starts with for Ben was whether he thought that doing the start-up was worth it.
May 15
How To Tag Any Image Using Deep Learning
You probably have photos, right? You probably want those photos tagged automatically for you, right? But you also don’t want to write a ton of code to do so. Read on to learn how to use deep learning and Pytorch to tag any photo with less than 60 lines of code. The best part is, you’ll only have to change about 3 lines of code to get it to work for your own images!
May 3
The Ultimate Guide to Linear Regression
In this post we are going to discuss the linear regression model used in machine learning. Modeling for this post will mean using a machine learning technique to learn - from data - the relationship between a set of features and what we hope to predict. Let’s bring in some data to make this idea more concrete. from sklearn.datasets import load_boston import pandas as pd import seaborn as sns import matplotlib.
Apr 19
5 Leadership Books You Must Read
I love books. And I truly believe these books can help transform you into an even better leader. A great book is like a cheat code to life. You can get access to years of research and experience for typically less than $20. That is insane to me. I also think that books are very underutilized — especially when it comes to leadership books. Often leaders don’t have a lot of time and that lack of time doesn’t lend itself well to picking up a new book.
Apr 9
7 Reasons To Not Hire a Data Scientist
I think the title is pretty clear, so let’s get straight to it. 1: You don’t have any data Before even thinking about hiring a data scientist, you should step back and consider your data. A data scientist’s job is to create value from data. If you are unsure whether you even have data, that is a very good sign that you’re not ready for a data scientist. If you know you have data, but really have no idea how to access it, it’s reliability or any of the specifics, then you should first answer those questions.
Mar 28
How To Create Unique Pokémon Using GANs
My son is really into Pokemon. I don’t get it, but I guess that’s besides the point. I did start to wonder, though, if I could create new Pokemon cards for him automatically using deep learning. I ended up having a bit of success generating Pokemon-like images using Generative Adversarial Networks (GANs) and I thought others might enjoy seeing the process. Generative Adversarial Networks Source I don’t want to spend a lot of time discussing what GANs are, but the above image is a very simple explanation of the process.
Mar 25
How To Get Started with Deep Learning
Photo by JESHOOTS.COM on Unsplash I recently sent out a poll to people who subscribe to my email list and asked what they were most interested in learning. About 86 percent said deep learning. That blew my mind. I knew deep learning was a hot topic, but I had no idea just how interested people were in learning more. So — I thought I would write up how I would start learning deep learning if I were to start today.
Mar 20
How to Make Remote Work Effective for Data Science Teams
In 1973, at the height of the OPEC oil crisis and skyrocketing fuel prices, NASA scientist and USC professor Jack Nilles began thinking about ways work could be done without the need for commuting. Nilles’ thought experiments evolved into case studies, numerous books, including The Telecommunications-Transportation Tradeoff, the original book on telecommuting, as well as dozens of papers, articles and keynote speeches. To this day, Nilles remains one of the principal evangelists for remote work as a viable alternative to a traditional office.
Mar 19
How to Get Started Analyzing COVID-19 Data
Photo by National Cancer Institute on Unsplash Earlier this month, Kaggle released a new dataset challenge: the COVID-19 Open Research Dataset Challenge. This challenge is a call to action to AI experts to develop text processing tools to help medical professionals find answers to high priority questions. To that end, and in partnership with AI2, CZI, MSR, Georgetown, NIH & The White House, Kaggle assembled a dataset “of over 29,000 scholarly articles, including over 13,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses.
Mar 4
Don’t Worry, Excel is Surprisingly Effective
Photo by Mika Baumeister on Unsplash It is common for data scientists to look down on Microsoft Excel. Compared to a programming language like Python, it seems like a tool from the stone age. It doesn’t scale well, it’s hard to reproduce results, and once you start writing VBA macros, you might as well be using Python. Given all that, though, Excel has survived. I can’t even think of a business that doesn’t use some type of spreadsheet software to help analyze data.