Linear Regression Using Different Gradient Descent

Gradient Descent Gradient Descent is the most popular optimizer to update parameters and it uses the gradient of the error with respect to the parameter. But the parameter update rule is different and thus there are different variants of Gradient Descent. Mini- Batch Gradient Decent It is the simplest algorithm, where we update parameters in […]

Logistic Regression from Scratch in Python: Exploring MSE and Log Loss

Logistic Regression From Scratch Hello everyone, here in this blog we will explore how we could train a logistic regression from scratch. We will start from mathematics and gradually implement small chunks into our code. Import Necessary Module pandas : Working for DataFrame numpy : For array operation matplotlib : For visualization time : function […]

Python for Stock Market Analysis: Exploring Technical Trend Indicators

Introduction Hello and welcome back everyone to our second part of the new blog series [Python for Stock Market Analysis](). In the last part, we explored different [types of moving averages]() like Simple Moving Average (SMA), Exponential Moving Average (EMA), Weighted Moving Average (WMA) and explored other moving metrics like Moving Median and Moving Variance. […]

K means Clustering in Python from Scratch

K means Clustering in Python from Scratch Introduction K means clustering is very simple type of unsupervised learning. Which is used to solve clustering problem. Using this algorithm we can easily classify given data point in given numbers of clusters (k). To do so we should first find number of cluster. In k mean cluster […]

Making a Stack Data Type in Python

Making a Stack Data Type in Python Introduction Stack is one of the primitive data structure that we always have to study before diving into the Data Structure and Analysis. It is an example of ADT (Abstract Data Type) where operations are predefined. There are some other types of ADTs also like Queue, List etc. […]

Image Compression: Run Length Encoding in Python

Run Length Encoding is one of the image compression algorithms that is lossless. So let’s get started with simple intro of Run Length Encoding and getting hands dirty. This blog is cross-posted from q-viper.github.io. (Teaser Image taken from here.) If you are interested to learn about Huffman encoding of lossless image compression then please visit […]

Nepali News (Gorkhapatra) Scrapping Using BeautifulSoup and Python

Scraping News Data From Gorkhapatra Introduction In this blog, I am going to write about how I was able to scrap news from Gorkhapatra news portal of Nepal. I have also written a code for scraping Ekantipur and Annapurna but I will be sharing those in another blog. In the previous blog, I wrote how […]

Nepali News (Annapurna Post) Scrapping Using BeautifulSoup and Python

Scrapping News Data From Annapurna Post News Portal Using python and BeautifulSoup. Scraping News Data From Annapurna Post Introduction In this blog, I am going to write about how I was able to scrap news from Annapurna news portal of Nepal.It took me more than week to try this and still there are lot of […]

Writing a Logistic Regression Class from Scratch

Logistic Regression Logistic Regression is not exactly a regression but it performs a classification. As the name suggests, it uses the logistic function. This notebook is inspired by the github repo of Tarry Singh and I have referenced most of the codes from that repo. Please leave a star on it. Artificial Intelligence Deep Learning […]

Writing a Linear Regression Class from Scratch Using Python

Linear Regression Introduction Before there was any ML algorithms, there was a concept and that was regression. Linear Regression is considered as the process of finding the value or guessing a dependent variable using the number of independent variables. Take for a example:- predicting a price of house using variables like, size of house, age […]

Scroll to top
Subscribe to our Newsletter

Hello surfer, thank you for being here. We are as excited as you are to share what we know about data. Please subscribe to our newsletter for weekly data blogs and many more. If you’ve already done it, please close this popup.



No, thank you. I do not want.
100% secure.