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...

The way that R-squared shouldn’t be utilized for choosing if you have a satisfactory model is illogical and is once in a while clarified unmistakably. This exhibit diagrams how R-squared integrity of-fit functions in relapse investigation and relationships while demonstrating why it’s anything but a proportion of measurable sufficiency, so ought not to propose anything about future prescient execution. The R-squared Goodness-of-Fit measure is one of the most broadly accessible insights going with the yield of relapse investigation in factual programming. Maybe incompletely because of its far-reaching accessibility, it is additionally one of the frequently misjudged ones. Initial, a concise update on R-squared (R2). In a relapse with a solitary free factor, R2 is determined as the proportion between the variety clarified by the model and the all-out watched variety. It is regularly called the coefficient of assurance and can be deciphered as the extent of variety clarified by the presen...

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...

Demands of data science Why do most data scientists love Python? Learn more about how so many well-developed Python packages can help you accomplish your crucial data science tasks. According to job sites such as Indeed, Glassdoor, Naukri, LinkedIn, and Dice demands data scientists and continues to grow, year over year, as businesses across the industries increasingly depend on data-driven insights. There are, in fact, many different learning paths to this hottest profession, and choosing the right one depends on where you are in your career. Besides mathematical and statistical skills, programming expertise is also one of the must-have skills an aspiring data scientist needs to acquire. Let’s dig deeper to unearth the most popular programming languages in the data science community! Top 3 programming languages most used by data scientists As revealed by the findings of a survey conducted by Kaggle, Python is the most used programming language followed by SQL and R. O...