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 next 1

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. The che

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