Blending Machine Learning and Domain Knowledge with Feature Engineering
Data augmentation is effective when addressing dataset shortcomings. In this blog, we’ll discuss augmentation basics, and techniques for augmenting image data.
Listen to Your Data: The What, Why, and How of Exploratory Data Analysis
EDA allows ML scientists to visualize datasets from many angles so they can make informed decisions about how to improve them. In this blog, we’ll discuss the fundamentals of EDA in regards to computer vision.
5 Common ML Data Cleaning Problems and How To Solve Them
Though there’s no shortage of data today, most data needs quite a bit of work before it can be leveraged into machine learning solutions. In this blog, we discuss five common issues that are addressed during data cleaning, and some potential solutions.
The Brave 1st Step of Machine Learning: Dealing with Data
Finding data that’s well-suited to train Machine Learning solutions isn’t as easy as it may sound. In this blog we will discuss best practices for managing data along with pro-tips on determining the right amount of data required to train a machine learning model.
The Devil is in the Data: Machine Learning Process Simplified
Machine Learning may seem daunting and sound like the science fiction portrayed in The Matrix movies, but in reality, it is merely data, algorithms, and training iterations. In this blog we will break down the nine common steps of Machine Learning.
Innotescus helps scientists and engineers break the 80/20 rule
Are you frustrated with the quality and amount of time spent managing data? In this blog you will learn about new tools that help scientists and engineers break the 80/20 rule – allowing for more algorithm development, feature engineering, and model tuning.
We are a group of scientists, engineers, and entrepreneurs with a vision for better AI. With backgrounds primarily in Machine Learning and Computer Vision, the Innotescus team understands the importance of having full control over and insight into data used to train Machine Learning models.