How to use data analysis for machine learning (example, part 1) - SHARP SIGHT LABS

#artificialintelligence

In my last article, I stated that for practitioners (as opposed to theorists), the real prerequisite for machine learning is data analysis, not math. One of the main reasons for making this statement, is that data scientists spend an inordinate amount of time on data analysis. The traditional statement is that data scientists "spend 80% of their time on data preparation." While I think that this statement is essentially correct, a more precise statement is that you'll spend 80% of your time on getting data, cleaning data, aggregating data, reshaping data, and exploring data using exploratory data analysis and data visualization. And ultimately, the importance of data analysis applies not only to data science generally, but machine learning specifically.


Introduction to Altair - A Declarative Visualization Library in Python

@machinelearnbot

Visualization is one of the most exciting parts of data science. Plotting huge amounts of data to unveil underlying relationships has its own fun.


The Top 5 Benefits of Using Data Visualization

@machinelearnbot

Whether you're working on a school presentation or preparing a monthly sales report for your boss, presenting your data in a detailed and easy-to-follow form is essential. It's hard to keep the focus of your audience if you can't help them fully understand the data you're trying to explain. The best way to understand complex data is to show your results in a graphic form. This is the main reason why data visualization has become a key part of all presentations and data analysis. But let's see what are the top 5 benefits of using data visualization in your work.


The Top 5 Benefits of Using Data Visualization

@machinelearnbot

Whether you're working on a school presentation or preparing a monthly sales report for your boss, presenting your data in a detailed and easy-to-follow form is essential. It's hard to keep the focus of your audience if you can't help them fully understand the data you're trying to explain. The best way to understand complex data is to show your results in a graphic form. This is the main reason why data visualization has become a key part of all presentations and data analysis. But let's see what are the top 5 benefits of using data visualization in your work.


Data Visualization Project

#artificialintelligence

You may have seen me tweeting about some research I did on "Data Visualization for Exploratory Data Analysis" for my Cognitive Systems Engineering course. My presentation went really well! I'm less satisfied with the paper since it was done in a hurry to complete the project deliverables, but i'm including it because it explains some things that aren't obvious from the powerpoint without my commentary. Check out the references in both documents for some good resources. I'll include some links in the post below, too.