Machine learning and statistics with python

                  Introduction to Chatbots Introduction: Chatbots are every where in the current industry with starting from automated reply bots, from website greeting chatbots, to zomato's complaint chat bot and upto sophisticated companion ai chatbots in chatting apps. Building and discussing about chatbot is a part of natural language processing, understanding and generation task. In this article, we will describe the basic ideas of how chatbot works, what are the different parts, and we will vividly draw inspiration from the google developer course taught by priyanka verdegha called "deconstructing chatbots." Summary of the article: In this article, we will go through the concepts of what is chatbot, how dialogue works, what are intents, context, and, how the actions are performed by chatbot. This is the first of the chatbot series which I will be writing over january 2021. This post will mainly discuss around understanding the concepts, while in the next posts,

Review of Deep Fake videos  Introduction: Deepfake videos mean fake videos created with deep learning videos. With the recent wake of deep learning frameworks and researches, it has become way too easy to create very reliable results in generating fake videos using deep learning techniques. In this post, we will go through the content of the LinkedIn course "understanding the impact of deep fake videos" and thereby discuss deep fake technologies. What are the elements of deep fake? Deep fake includes two things: deep learning and fake content. A deep fake video is created often using fake voice and original video, or original voice and fake video; or even both fake voice and fake videos. The thing which we are referring as fake; is basically synthetic content, used by deep learning technologies. In deep fake, image to image, speech to speech, text to speech techniques are used to create these synthetic contents. For classic, check this amazing Nixon's speech about m

                                       Introduction SVM or support vector machine is one of the most dominant classification algorithms in the traditional machine learning. svm is a supervised classification algorithm which mainly works in a two-group classification algorithm; by projecting the features into a linear space; and then by drawing a hyperplane between the two classes thereby classifying. It is okay if you don't understand the terminologies I just used, as in the next few sections we will go through basic and advanced concepts of svm and by the end of this article, you will be confident to work with svm model. The basics of SVM: SVM is the abbreviation for support vector machine. In SVM is that in this algorithm, we try to find a hyperplane dividing the two classes of data with maximum distance between the support vectors of two classes. In case of SVM, we consider points which are near the hyperplane as support vectors; as we maximize the distance of each of the s

                                           Introduction:                                       Photo by Ethan Johnson on Unsplash I have recently embarked upon the journey of getting a few stars in my cap by starting small-time coding in hackerrank. While I am only a 3-star noob there yet, I really liked one problem which I solved today. So in this post, we will discuss the solution and speed optimizations I had to implement to solve the problem. The problem description: The problem is climbing the leaderboard . The problem statement is simple. There is a leaderboard, where dense ranking method is used. i.e. if people get same marks, then they are assigned the same rank and then the next person to them in the list is assigned the next rank. For example, if there are 4 people in the rank board, and they have got marks respectively 100,90,90,80; then their ranks will be 1,2,2,3. Now, the question is that one player comes into the competition and keeps solving problems and his/her r

     Streamlit application listing Introduction: Streamlit is an up and coming platform for hosting data apps with pythonic format to write them up. You don't need to know anything about tough and complicated web frameworks like django and flask and yet you can host your data apps using streamlit from now on. I created a streamlit application and released tutorials on starting up with streamlit and also how to design different components of apps in streamlit. Now I have connected with a few streamlit enthusiasts via that and have been seeing really cool apps being created with streamlit. So in this post, I will post some of the cool streamlit apps I find in internet. Later on I may add descriptions for some of these. NLP apps: (1) nlp app for sentiment detection, tokenization and number of nlp actions (2) my spacy streamlit app   works on spacy, does dependency parsing, entity tagging and pos tagging. Stock apps: (1) stock analysis app:  This is written by one of my open-sour

Introduction to PCA analysis                          project and writing: shyambhu mukherjee, solanki kundu Introduction: PCA(principal component analysis) is one of the major mathematical techniques used regularly behind dimension reduction, feature reduction, and low dimensional visualizations. PCA is dependent on the idea of eigenvalue decomposition of symmetric matrices .  Summary of article: In this article, we will start with basic concept of PCA and discuss the various ways to perform PCA visualizations and results. Then, once you are down with basics and usage, we will get into the linear algebra behind PCA; discuss why and how eigenvalues come into picture and how to practically use PCA, choose dimensions and all that. By the end of this article, you will be confident to use and explain PCA analysis. If you already use pca, still I guess this article will work as a great refresher for you. PCAs are nothing but modified eigen-vectors: In PCA, the main goal is to find out a l