Machine Learning Algorithm

As we all know we are living in a Digital Era. Almost everything around us is digitally connected. For example, QR code. Almost everyone uses QR Code for financial transactions in there day to day life. If you see any business that is not on the internet it's like they are missing out on the digital world. No business can grow immensely without creating its digital presence. What is Digital Transformation? Digital transformation is the process of using digital technologies to transform existing traditional and non-digital business processes and services, or creating new ones, to meet with the evolving market and customer expectations, thus completely altering the way businesses are managed and operated, and how value is delivered to customers. To help you stay ahead of the game, we've compiled some of the most valuable insights from today's leading digital businesses.  Some of Indian Startups bloom after Digital Transformation : 1. Lenskart Have you ever thought that you do

Top 10 business ideas Hey do you want to become an self made entrepreneur? Do you want to start your own business? If yes then you are at the right place !!! The rise of entrepreneurship in India is unstoppable, and that is something we should be proud of.The wave of entrepreneurship is on it's hype. Here are some business ideas to bloom your career. 1. Online Reselling  If you’re interested in clothing and/or sales, you might consider  starting an online reseller business . Y ou can start your business as a side hustle and turn it into a full-time resale business. Here's your action plan: Choose the right type of reselling business. Identify the industry for your business.  Identify the market and target audience for your business.  Check out your competitors.  Check if the business is viable.  Start your reseller business online. 2. Professional Organizing If you’re a highly organized person who enjoys making spaces functional and comfortable, you might be good at coaching ot

Hello Everyone there's something special for all you guys The Youth lab Colorado , USA is organizing a free webinar on October 30th 2022 on the topic Jobs is Big Data and  Artificial Intelligence. Don't miss this opportunity & do grab your seat for this webinar. Click to Register After registering mail me your names at saniyashaikh2953@gmail.com

Bias variance trade off Bias means the inability of a machine learning model to truly capture the relationship in the training data set. That means it cannot understand the pattern in the training data set. Variance is the different of fits on different data sets. The difference between the training and the testing data set is variance. Overfitting When your data set works well on the trading data set but does not perform well on testing data set its called over fitting. Underfitting When your model does not perform well on your training data set then it is called under fitting. There are three methods for controlling over fitting: 1. Regularization 2. Bagging 3. Boosting There are 3 techniques of regularization: 1. Ridge Regression In this we add some more regularization terms to reduce the over fitting. Basically it's  lambda. For performing ridge Regression we have an in-built class Ridge in sklearn Library. Let's see the code : from sklearn.linear_model import LinearRegress

What is polynomial Regression?? There are 3 types of Linear Regression Algorithms: 1. Simple Linear Regression 2. Multiple Linear Regression 3. Polynomial Regression We have already discussed about Simple Linear Regression & Multiple Linear Regression. If you want to know about that check this links out: Simple Linear Regression Multiple Linear Regression Polynomial Regression is a form of linear regression in which at the end we perform linear regression by applying same principles. It's just that we add polynomial terms in our data set. Polynomial Regression is used when we have  non-linear data set. For eg ; We have X,Y columns in our dataset. In which X is the input column & Y is the output column. In polynomial regression we extract the polynomial features in the preprocessing stage. That means if we want to create degree = 2 then we will convert X0 ,X1 ,X2 for every row. Features help us to understand the non linear relationship. In polynomial regression degree is a h