Here we will learn

Artificial Intelligence

&

MACHINE
LEARNING

"Machine learning is a sub-part of AI that provides
system to work upon its deficiency and improve from experiences
without being explicitly programmed."

MACHINE LEARNING

No need to panic now, here it will let you understand the concepts of Machine Learning. You will get the explanation of a particular topic including implementation, application, significance, and how it can be helpful for the coming generation within a proper manner. Also, there are codes implemented in it as well. Codes are provided with a high-quality explanation with each statement. By the end of this, you will be able to predict the outcomes, through your code, by analyzing the data given to you. Day-by-day you can meet with additional information updated in our blog section...

Scikit learn library

"Once you get stick to it,

you will be lost in it"

1. Linear Regression

Simple Linear Regression
Multiple Linear Regression

2. Logistic Regression

3. K-Nearest Neighbor

4. K-Means Clustering

Let's start to code...

This includes among the most essential libraries to make our programs shorter. Scikit-Learn is prominent for installing ML models of linear regression, logistic regression, etc. It provides input datasets for easy access. It helps to split data into a single statement.

NumPy

It is an integrable part of developing integral parts of ML. It allows us to create N-dimensional array objects, perform linear algebra, and much more. Most deep learning libraries use NumPy as their model datatype for tensors.

Matplotlib

It is a visualization library used to display your training data or your output data. This library is in competition with Seaborn, but I personally prefer Matplotlib due to experiences.

Pandas

It is a manipulation of data and analysis of the library. It is commonly used to create a specific data structure called DataFrame out of CSV/excel files. We can access data through it.