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
Explainers
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.
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
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
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
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
15 Ways Machine Learning Will Impact Your Everyday Life
Artificial intelligence (AI), specifically 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
Open Source vs Commercial Machine Learning Software
At the start of any machine learning project, you face an important choice: Which language or software should I use? Well, you have many options to choose from. Python, R, SAS, MATLAB…
Overfitting in Machine Learning: What It Is and How to Prevent It
Did you know that there’s one mistake… ...that thousands of data science beginners unknowingly commit? And that this mistake can single-handedly ruin your machine learning model? No, that’s not an exaggeration. We’re talking