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Model Training with Machine Learning

Welcome to Part 6 of our Data Science Primer. In this guide, we will take you step-by-step through the model training process. Since we’ve already done the hard part, actually fitting (a.k.a. training) our model will be fairly straightforward. There are a few key techniques that we’ll discuss, and these have become widely-accepted best practices in...

Algorithm Selection for Machine Learning

Welcome to Part 5 of our Data Science Primer. Choosing the right ML algorithm for your task can be overwhelming. There are dozens of options, each with their own advantages and disadvantages. However, rather than bombarding you with all options, we’re going to jump straight to best practices. Specifically, we’ll introduce two powerful mechanisms...

Feature Engineering for Machine Learning

Welcome to Part 4 of our Data Science Primer. In this guide, we’ll see how we can perform feature engineering to help out our algorithms and improve model performance. Remember, out of all the core steps in applied machine learning, data scientists usually spend the most time on feature engineering. What is Feature Engineering? ...

The Machine Learning Workflow: Bird’s Eye View

Welcome to Part 1 of our Data Science Primer. This bird’s eye view of the machine learning workflow will give you an end-to-end blueprint for data science and applied ML. You’ll learn the “ELI5” intuition behind machine learning, key terminology, and the ingredients to an effective ML model. You may have already seen some of the tutorials on...

Exploratory Analysis for Machine Learning

Welcome to Part 2 of our Data Science Primer. Exploratory analysis is essential for effective data science because it helps you avoid wild goose chases and dead ends. This step should not be confused with data visualization or summary statistics. Those are merely tools… means to an end. Proper exploratory analysis is about answering questions....

Data Cleaning for Machine Learning

Welcome to Part 3 of our Data Science Primer. In this guide, we’ll teach you how to get your dataset into tip-top shape through data cleaning. Data cleaning is crucial, because garbage in gets you garbage out, no matter how fancy your ML algorithm is. The steps and techniques for data cleaning will vary from dataset to dataset. As a result, it’s...

15 Ways Machine Learning Will Impact Your Everyday Life

Artificial intelligence (AI) and machine learning is now considered to be one of the biggest innovations since the microchip. AI used to be a fanciful concept from science fiction, but now it’s becoming a daily reality. Neural networks (imitating the process of real neurons in the brain) are paving the way toward breakthroughs in machine learning, called...

Python for Data Science (Ultimate Quickstart Guide)

This quickstart tutorial will get you set up and coding in Python for data science. If you want to learn one of the most in-demand programming languages in the world… you’re in the right place. By the end of this guide, you’ll have a strong foundation and be able to follow along other tutorials on this site, even if you’ve never programmed before....

Python Data Wrangling Tutorial: Cryptocurrency Edition

Bitcoin and cryptocurrency have been all the rage… but as data scientists, we’re empiricists, right? We don’t want to just take others’ word for it… we want to look at the data firsthand! In this tutorial, we’ll introduce common and powerful techniques for data wrangling in Python. Broadly speaking, data wrangling is the process of reshaping, aggregating,...

How to Become a Data Scientist, The Self-Starter Way

So you want to become a data scientist… that’s fantastic! But as you may already know (or may soon find out), it’s not quite that simple. In fact, you’ll most likely face some challenges that are unique to data science… Challenge #1: WTF is a “data scientist?” You could ask 10 data scientists and get 15 descriptions of what they do. The term “data...

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