A Machine Learning project is never really complete if we don’t have a good way to showcase it.
While in the past, a well-made visualization or a small PPT used to be enough for showcasing a data science project, with the advent of dashboarding tools like RShiny and Dash, a good data scientist needs to have a fair bit of knowledge of web frameworks to get along.
And Web frameworks are hard to learn.
Object Detection is a helpful tool to have in your coding repository.
It forms the backbone of many fantastic industrial applications. Some of them being self-driving cars, medical imaging and face detection.
In my last post on Object detection, I talked about how Object detection models evolved.
But what good is theory, if we can’t implement it?
This post is about implementing and getting an object detector on our custom dataset of weapons.
I like deep learning a lot but Object Detection is something that doesn’t come easily to me.
And Object detection is important and does have its uses. Most common of them being self-driving cars, medical imaging and face detection.
It is definitely a hard problem to solve. And with so many moving parts and new concepts introduced over the long history of this problem, it becomes even harder to understand.
Data Science is the study of algorithms.
I grapple through with many algorithms on a day to day basis, so I thought of listing some of the most common and most used algorithms one will end up using in this new DS Algorithm series.
How many times it has happened when you create a lot of features and then you need to come up with ways to reduce the number of features?
Technological developments have paved the way for new niche industries, where professions like data science have appeared.
Data scientists have the knowledge and expertise to perform the work that data analysts do, and then some. They analyze and interpret complex data sets of varying structures, and are able to solve obscure problems with codes, models, and machine-learning algorithms.
As you can see in our post ‘How did I Learn Data Science?
Decision Trees are great and are useful for a variety of tasks. They form the backbone of most of the best performing models in the industry like XGboost and Lightgbm.
But how do they work exactly? In fact, this is one of the most asked questions in ML/DS interviews.
We generally know they work in a stepwise manner and have a tree structure where we split a node using some feature on some criterion.
Recently, I got asked about how to explain p-values in simple terms to a layperson. I found that it is hard to do that.
P-Values are always a headache to explain even to someone who knows about them let alone someone who doesn’t understand statistics.
I went to Wikipedia to find something and here is the definition: > In statistical hypothesis testing, the p-value or probability value is, for a given statistical model, the probability that, when the null hypothesis is true, the statistical summary (such as the sample mean difference between two groups) would be equal to, or more extreme than, the actual observed results.
