Machine Learning

  Would you let an Artificial Intelligence make decisions on behalf? — If Yes, then to what extent, maybe your life depends on it. From the incredibly-friendly voice of Apple’s personal assistant, Siri, to movies like Ex-Machina, Al has always excited me more than anything else. The very idea that Netflix can actually predict a recommendation list of movies based on your reaction to a previously seen movie sounds fascinating to me and with this approach I have been working on Machine Learning Algorithms and its all classifiers to make much more robust and easy to understand by everyone, So I started uploading all the basic Machine Learning Algorithms From 10 March 2020 to 10 August 2020 on my Github Repository in Python Programming and R Programming. Then one day out of nowhere I come across a video on YouTube by Siraj Raval, in which he talked about something called #100DaysOfMLCode Challenge. It means coding and studying machine learning for at least an hour, every day for the ne...

I enjoy reading and spending time browsing Medium and writing in it. I use it as my blog, as my go-to source for exciting news, opinions, idea center, topic reading, a lot.  But writing and sharing information and getting around in Artificial Intelligence is not necessarily very easy. Using Artificial Intelligence, Machine learning, Deep Learning, Natural Language Processing and other buzzing words effectively can be a little complex as it keeps changing itself. Fortunately, Blogs gave us a lot of information on all the aspects and Cheatsheets plays a very vital role in the field of learning and memorizing things. They have excellent tips and tricks and “how-to” articles but they are all over the place. I spent one month putting together all cheat sheets for  Artificial Intelligence, Machine learning, Deep Learning, Natural Language Processing all together to work effectively and get to know every useful information . Feel free to use it, critique it, recommend, add to it, etc...

Python’s popularity is still backed by a rock-solid community of computer scientists, data scientists, and AI specialists. But if you have ever been at a dinner table with these people, you also know how much they rant about the weaknesses of Python. From being slow to requiring excessive testing, to producing runtime errors despite prior testing — there is enough to be pissed off about. Therefore more and more programmers are adopting other languages — the top players being Julia, Go, and Rust.  Julia is great for mathematical and technical tasks, while Go is awesome for modular programs, and Rust is the top choice for systems programming. Since data scientists and AI specialists deal with lots of mathematical problems, Julia is the winner for them. And even upon critical scrutiny, Julia has upsides that Python cannot beat. Why Python is not the programming language of the future When people create a new programming language, they do so because they wa...

Check out these 5 cool Python libraries that the author has come across during an NLP project, and which have made their life easier. Python is a best friend for the majority of the Data Scientists. Libraries make their life simpler. I have come across five cool Python libraries while working on my NLP project. This helped me a lot and I would like to share the same in this article. 1. Numerizer Amazing library to convert text numerics into int and float. Useful library for NLP projects. For more details, please check PyPI and this Github repo . Installation !pip install numerizer Example #importing numerize library from numerizer import numerize#examplesprint(numerize(‘Eight fifty million’)) print(numerize(‘one two three’)) print(numerize(‘Fifteen hundred’)) print(numerize(‘Three hundred and Forty five’)) print(numerize(‘Six and one quarter’)) print(numerize(‘Jack is having fifty million’)) print(numerize(‘Three hundred billion’)) Output 2. Missingo It is widespr...