We already learned Linear Regression, so what’s new here? What was the need for Ridge & Lasso Regression technique? We will be answering all these questions right here in detail. First: let’s look at possible categories of results that can be obtained from training a model with linear regression.
Whenever you come across linear regression, the first thing that should come to your mind is a scatter plot image somewhat like this.
To set up the possible relationship among different variables, various modes of statistical approaches are implemented, known as regression analysis. To understand how the variation in an independent variable can impact the dependent variable, regression analysis is specially molded out.
Basically, regression analysis sets up an equation to explain the significant relationship between one or more predictors and response variables and also to estimate current observations. …
Hello everyone, as the title suggests, in this blog, we will be going through different types and processes involved in Machine Learning. But, let's first understand how machine learning works.
Python Pandas is an open-source library that provides high-performance data manipulation in Python. This tutorial is designed for both beginners and professionals.
In this blog, we will look at Python modules(NumPY & File Handling) necessary for Machine Learning. Please try to run the code by yourself for a better understanding.
Prerequisite; Basic knowledge of Python is necessary to understand this blog.
NumPy: NumPY is a Python library used for working with arrays. It also has functions for working in domain of linear algebra, Fourier transform (Fourier Transform is an important image processing tool which is used to decompose an image into its sine and cosine components), and matrices.
The study of history is important because it allows one to make more sense of the current world. It let’s you know the efforts done by our scientists for creating AI. Its always interesting to start a topic to learn from its history.
Hello guys, I am sure that you are ready to dive deep into Data Science. I will cover the complete course with essential code from 0 to 100 in this series of blogs. So, let’s begin...
Topics covered in this blog:
1) Differencing these technologies (AI vs ML vs DL)
2) Defining these technologies
3) Prerequisite to become a Data Scientist
4) Life-cycle of Data Science Project
Learning and exploring this beautiful world with amazing tech.