Jupyter notebooks are where machine learning models go to die.

Wait— what?

Unlike what you probably learned in University, building models in a Jupyter notebook or R Studio script is just the very beginning of the process. If your process ends with a model sitting in a notebook, those models almost certainly didn't create value for your company (some exceptions might be it was only for analytics or you work at Netflix).

That doesn’t mean that your models aren’t excellent. I’m sure they are. But it probably does mean the people paying you are not super excited by the outcome.

…

Office Hours

The most common question I receive is how do I actually break into data science? So many people want to start a career in data science, but struggle to make that first step.

And I won’t lie, it is a hard step.

You are battling against companies and recruiters always wanting candidates with experience, while you are looking for your chance to start building industry experience. So — what are you to do?

While I can’t guarantee you success, I can give you a process to follow to try and maximize your chances of getting your first data science job…

I remember graduating from school, putting together a resume, and constantly wondering if my resume was “good.” It can be so hard to know what makes a resume good, but after reading hundreds of data science resumes, I wanted to point out 5 mistakes I see all the time. If you can avoid these mistakes, you will have made great progress in getting your resume to a good spot.

So — here are 5 common resume mistakes:

1. Using a non-standard resume template

I’ve found that it is increasingly common for people to try to make their resume different. Things like skill charts or personal touches…

Now, don’t get me wrong, online courses are great! You can learn so much online and that is truly amazing.

But — I find often there is a disconnect between the expected outcome of taking an online course and what actually happens. Let me first preface with the fact that I am specifically speaking to data science courses as those are the courses I am more familiar with. Other types of courses might lead to different outcomes.

Let’s start with a story of a young data scientist looking to break into the field.

In 2017, it was estimated that 750 million people worldwide used Excel. The population of the world in 2017 was about 7.6 billion. That means roughly 10% of the population was using Excel and I would guess mostly for data analytics. That is insane.

There is no doubt that Excel has been an incredibly important tool for companies and still has a place in the toolkit of every data analyst and scientist, but for most of your work, you need to stop using Excel and upgrade to Python. I’m going to show you why.

So, if you still have not…

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!

Tagging Monkeys

An extremely common machine learning problem is to classify or tag an image. …

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.pyplot as plt 
import numpy as np from sklearn.model_selection 
import learning_curve from sklearn.metrics 
import make_scorer 
%matplotlib inlinenp.random.seed(42)boston_data = load_boston() boston_df = pd.DataFrame(boston_data.data, columns=boston_data.feature_names) 

target = boston_data.target

Here is…

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.

You will get significantly more value from a data…