Black Friday — A Detailed Analysis & Prediction using Visualization and XGBoost. Nand Lal Mishra.
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Machine Learning Clustering Techniques. In this article, using Data Science , I will define basic of different types of Clustering algorithms.
In this blog we will be doing a project based on image classification where our problem statement describe us to classifies the images into two categories i.e. Emergency & Non-Emergency vehicle which is a binary classification problem and we will be solving using neural network.
Understanding the use of Regularization algorithms like LASSO, Ridge, and Elastic-Net regression. Before directly jumping into this article make sure you know the maths behind the Linear Regression algorithm. If you don’t, follow this article through!
This post is very much in continuation to that, as here we will be discussing one more algorithm which we used in our Custom Pipeline, SVM(Support Vector Machines).
10 things I wish I’d known before starting as a Data Scientist. I was just a computer scientist. I’m being asked by students for advice on the subject so here are a few of my opinions.
RegEx in R for Data Science. The ‘regex’ family of languages and commands is used for manipulating text strings. More specifically, regular expressions are typically used for finding specific patterns of characters and replacing them with others.
Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart “
Assumption before you start is — that you understand below concepts, if not — its worth a quick detour to below links: What does python do when you ask it to run a script? What are objects in python? How are these objects created and referenced?
It’s important yet sometimes difficult to get a handle on what may be the most fundamental idea in Python programming and is certainly the basis of much of both the conciseness and flexibility of the Python language- dynamic typing, and the polymorphism it implies. This is a slightly long topic, but i wanted to explain this in depth as this is core to understanding Python.
Simplest and quickest ways to do Exploratory Data Analysis with Pandas. I have taken Nobel Laureate dataset from Kaggle to do this exploratory data analysis, you can find the dataset here.
The purpose of writing this post is to understand the maths behind gradient descent. Most of us are using gradient descent in machine learning, but we need to understand the maths behind it. As a fresher, when I was learning stochastic gradient descent, I found it a little bit complex. Here, I tried to make it simpler for those who want to know how it works. My focus on this post is to demonstrate the mathematics behind gradient descent.
This post is about the ordinary least square method (OLS) for simple linear regression. If you are new to linear regression, read this article for getting a clear idea about the implementation of simple linear regression. This post will help you to understand how simple linear regression works step-by-step. The simple linear regression is a model with a single regressor (independent variable) x that has a relationship with a response (dependent or target) y that is
Vanishing and Exploding Gradient Problems: One of the problems with training very deep neural network is that are vanishing and exploding gradients.
A non-tech guy’s way of learning data science. I’m writing this post as a part of my journey with MySQL and since joins is a confusing thing in the SQL, I’m explaining this by simplest terms as possible.
In this blog, we’ll cover another interesting Machine Learning algorithm called Support Vector Regression(SVR). But before going to study SVR let’s study about Support Vector Machine(SVM).
In this Machine Learning tutorial, we’ll learn any Machine Learning algorithm called Logistic Regression. If we go by the name then it seems that it is similar to linear regression but there’s a difference. Linear Regression is used to predict a continuous value based on certain features i.e. it’s a regression algorithm but Logistic Regression is a classification algorithm.
Importance of Degrees of Freedom In Machine Learning and Statistics.Degrees of freedom is an important concept from statistics and Data Science(like Machine Learning). It is often employed to summarize the number of values used in the calculation of a statistic
If you are someone who is familiar with Data Science, you must have realized that somewhere between Simple Linear Regression and Deep Neural Networks we grow up to become a Data Scientist.