Learning With Data
How To Get Free GPU Hardware
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You’ve heard about deep learning. You’ve heard it is changing the world. You’ve also heard it requires a pretty nice GPU.
Naturally, you take a look at your computer to see what kind of hardware you have. You will probably discover 1 of 3 things:
You don’t have a dedicated GPU at all (common in laptops). You have a GPU, but it isn’t an Nvidia GPU which is pretty much required for deep learning.
Why Specialized Data Science Programs Are Disappointing
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One of the most common questions I get is:
Should I attend a data science Bootcamp or Masters?
I usually recommend people avoid these specialized data science programs because they tend to lack the rigor to prepare students for the quickly evolving world of data science.
I think these programs are extremely expensive in an attempt to capitalize upon the data science craze. And, thus, are more focused on the money than preparing students.
5 Tips to Improve Your Stale Terminal
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You don’t know it, but your terminal right now is more like a go-cart when you could have a Ferarri.
I will show you some great tools to take your command line to the next level.
iTerm2 If you are on Mac and using the default terminal. Stop. Go and install iTerm2.
iTerm2 has so many great features including:
Easy to split panes to have multiple terminals on one screen Search within your terminal Autocomplete Profiles 256 colors so it looks beautiful Smart selection — making it easy to copy the text you most likely wanted in one click Extensive customization options Once you have it downloaded, take some time to poke around and learn about all the options.
How to Become a Great Data Scientist in 2020
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Next year is your year.
This time next year, you are going to look back and be amazed by how much you have grown as a data scientist. I don’t doubt it. And neither should you.
To do so, though, will take work and dedication. To start you off right, I want to discuss 5 New Year’s resolutions to help you get to the next level.
The Top Five Most Useful Commands in Pandas
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You’ve just started learning data science, you’ve got your data in a dataframe, now what?
I’d love to introduce you to the commands that I use the most with Pandas to help you discover insights in your data even faster. I will assume you have a dataframe in your code called df.
Head Every time I read data into Pandas, the first thing I do is use the head command to view the first 5 rows of data.
The Most Undervalued Data Science Course
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You all know of Coursera’s machine learning course and Andrew Ng’s deep learning specialization. You even talk about fast.ai, Udemy, and EdX. These are all excellent resources to learn data science, but I want to make you aware of a lesser-known, yet superb, set of courses with which you can augment your knowledge in only a few hours.
Kaggle Learn You’ve probably heard of Kaggle.
How Money Can Be Terribly Distracting and What to Focus on Instead
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Money, Money, Money Who doesn’t like more money? All else equal, I’d say pretty much everyone would agree that having more money would be great. If you are currently making $75,000 and another company comes offering $85,000, you are immediately interested. In fact, there are entire companies basically dedicated to making it easier for you to compare salaries across companies and positions. Glassdoor and Paysa to name a few.
The Top 3 Books to Learn Math for Data Science Right Now
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Awhile back I wrote an article on the top books to get started with data science:
The Top 3 Books to Get Started with Data Science Right Now
_And build a great foundation of knowledge_towardsdatascience.com
One of the readers left a comment asking for the best books to learn math necessary for data science. I thought that was a wonderful idea! If you are eager to strengthen your mathematical foundation and really understand the inner workings of machine learning algorithms, this will give you a great start!
Everything You Ever Wanted to Know About K-Nearest Neighbors
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K-Nearest Neighbors is one of the simplest and easiest to understand machine learning algorithms. It can be used for both classification and regression tasks but is more common in classification, so we will start with that use case. The principles, though, can be used in both cases.
Here is the algorithm: Define k Define a distance metric — usually Euclidean distance For a new data point, find the k nearest training points and combine their classes in some way — usually voting — to get a predicted class That’s it!
Why You Are Using t-SNE Wrong
t-SNE has become a very popular technique for visualizing high dimensional data. It’s extremely common to take the features from an inner layer of a deep learning model and plot them in 2-dimensions using t-SNE to reduce the dimensionality. Unfortunately, most people just use scikit-learn’s implementation without actually understanding the results and misinterpreting what they mean.
While t-SNE is a dimensionality reduction technique, it is mostly used for visualization and not data pre-processing (like you might with PCA).