It’s one of the technique in which we implement KFold cross-validation, where k is equal to n i.e the number of observations in the data. Thus, every single point will be used in a validation set, we will create n models, for n-observations in the data. Each point/sample is used once as a test set while the remaining data/samples form the training set.
For almost a decade, Stack Overflow’s annual Developer Survey has been the largest survey of people who code around the world. This year they did a survey on 60,000+ software developers and shared some of the amazing statistics.
In this article, I’m going to write a detailed analysis of their survey result. So get ready to read some amazing analysis —
This network graph shows which technologies are most highly correlated with each other. The size of each circle corresponds to the proportion of survey respondents who use the technology tool.
Ruby and Rails tend to get used together. Similarly…
HackerEarth, with a community of around 4 million+ developers all around the world, is a highly sought after tech platform to hire quality developers. The coding problems on this platform are very exciting and challenging which can help you build a solid foundation on various topics like data structures and algorithms, data science and to say the least you can also participate in the different world class Hackathons. I have been crunching data for past few years and just two days back read this amazing survey that Hacker Earth has done where 16000 developers participated from 76 nations.
I love Data. Period.
Like every day (30 minutes reading ritual before starting my office work), I was going through some great data science and machine learning articles to keep myself abreast of latest trends/info and I found a great survey that was done by HackerRank.
In this post, I’m going to cover the trends in the developers’ community in 2020. Let’s dive in!
The results indicate that Larger companies are more likely to want to hire specialists as compared to smaller companies (startups) which considers full-stack developers more important.
Recently I received an email from one of my readers asking me to write about Python’s complex topics such as Iterators, Generators, and Decorators. In this post, I’m going to cover the basics, implementation, and how to use them in your code.
An iterator is an object that can be iterated upon which means that you can traverse through all the values. List, tuples, dictionaries, and sets are all iterable objects.
To create an object as an iterator you have to implement the methods
__next__() to your object where —
__iter__() returns the iterator object itself. …
Last few days have been very hectic at my workplace but let’s get back in the groove with Day 9 of Data Science and Machine Learning. I hope you all have already grasped the Python essentials, Statistics and Maths from day 1 — day 8( links shared below). For Day 9 we will cover the Pandas part 1.
Last few days have been very hectic at my workplace but let’s get back in the groove with Day 8 of Data Science and Machine Learning. I hope you all have already grasped the Python essentials and statistics from day 1 — day 7( links shared below). For Day 8 we will cover the Maths part 2.
Maths is extremely important in data science and machine learning. …
Statistics all the way…
I hope you all have already grasped the Python essentials from day 1 -day 6 ( links shared below). For Day 7 we will cover the Statistics part 1.
First of all, Why Statistics? As we uncover the power of stats in this statistics in python — part 1 series, there are numerous questions which stats can help you answer, like ( and the list doesn’t ends here) —
Data Science, ML, Front end, Back end, DevOps and many more…
These roadmaps will help you out in your career as a developer and let you build a clear understanding what you need to learn/know next ( Credits below).
In this post we will cover end to end Advanced Python ( Part 1) that you should know.
You can read-implement Day1, Day 2 , Day 3, Day 4 posts—
In Python, a decorator is any callable Python object that is used to modify a function or a class. It takes a function, adds some functionality, and returns it.