Tensorflow is a deep learning library that makes building and deploying Deep Learning Applications super easy. If you wondered what this library is all about wait no more, keep reading the blog to find what makes Tensorflow unique.

This blog provides an overview to the Tensorflow library and provides a brief introduction to the topic with some important keywords, it’s installation and demo code.

- It is a
**free and open-source platform for high-performance numerical computation**, specifically for ML and Deep Learning. - Has a
**flexible architecture**and can be deployed across a variety of platforms (CPUs, GPUs and TPUs) as well…

In this blog we’ll try to understand one of the most popular tools used to **containerize and deploy** **applications** over the internet i.e. Docker. It makes deploying applications extremely simple.

We will try to look at the things that make Docker so special and learn how you can **build, deploy, and fetch applications** using Docker & Docker Hub using just a few steps.

- It is a tool used to
**create, deploy and run applications by using containers**. *Containers*allows developers to**package up an application with all the parts it needs**.*Containers*are**isolated from one another**and**bundle their…**

Support Vector Machines (SVMs) are a set of supervised learning methods which learn from the dataset and can be used for both regression and classification. An SVM is a kind of **large-margin classifier:** it is a vector space based machine learning method where the goal is to find a **decision boundary** between two classes that is maximally far from any point in the training data.

The term Support Vectors refers to the **co-ordinates of individual observation**. Support Vector Machine is a frontier which best segregates the two classes using a **hyperplane/ line.**

In this blog we’ll try to dig deeper into Random Forest Classification. Here we will learn about ensemble learning and will try to implement it using Python.

You can find the code over **here**.

It is an ensemble tree-based learning algorithm. The Random Forest Classifier is a set of decision trees from randomly selected subset of training set. It **aggregates the votes from different decision trees** to decide the final class of the test object.

Ensemble algorithms are those which **combines more than one algorithms of same or different kind for classifying objects**. …

Logistic Regression is a **Supervised learning algorithm widely used for classification.** It is used to **predict a binary outcome (1/ 0, Yes/ No, True/ False) given a set of independent variables.** To represent binary/ categorical outcome, we use **dummy variables**.

Logistic regression uses an equation as the representation, very much like linear regression. It is not much different from linear regression, except that a *Sigmoid***function** is being fit in the equation of linear regression.

**Simple Linear and Multiple Linear Regression Equation:**

`y = b0 + b1x1 + b2x2 + ... + e`

**Sigmoid function :**

*p* = 1 /…

A Decision Tree is a simple representation for classifying examples. It is a Supervised Machine Learning where the data is continuously split according to a certain parameter.

**Nodes**: Test for the value of a certain attribute.**Edges/ Branch**: Correspond to the outcome of a test and connect to the next node or leaf.**Leaf nodes**: Terminal nodes that predict the outcome (represent class labels or class distribution).

In this blog, we’ll talk about one of the most widely used machine learning algorithms for classification, which is the K-Nearest Neighbors (KNN) algorithm. K-Nearest Neighbor (K-NN) is a simple, easy to understand, versatile and one of the topmost machine learning algorithms that find its applications in a variety of fields.

In this blog we’ll try to understand what is KNN, how it works, some common distance metrics used in KNN, its advantages & disadvantages along with some of its modern applications.

K-NN is a **non-parametric** and **lazy learning algorithm**. Non-parametric means there is no assumption for underlying data distribution…

In this blog we’ll try to understand one of the most important algorithms in machine learning i.e. Random Forest Algorithm. We will try to look at the things that make Random Forest so special and will try to implement it on a real life dataset.

The Code along with the dataset can be found **here**.

An Ensemble method is a technique that **combines the predictions from multiple machine learning algorithms** together to make more accurate predictions than any individual model. A model comprised of many models is called an **Ensemble model**.

We’re going to be implementing Linear Regression on the ‘**Boston Housing**’ dataset.

The Boston data set contains information about the different houses in Boston. There are 506 samples and 13 feature variables in this dataset. Our aim is to **predict the value of prices of the house using the given features**.

`import numpy as np`

import pandas as pd

import matplotlib.pyplot as plt

`data = pd.read_csv("boston.csv")`

To get **basic details** about our Boston Housing dataset like **null values or missing values, data types** etc. we can use** .info() **as shown below:

data.info()<class 'pandas.core.frame.DataFrame'> RangeIndex: 506 entries, 0 to 505…

In this blog, I’m going to provide a brief overview of the different types of Linear Regression with their applications to some real-world problems.

- Simple Linear Regression
- Multiple Linear Regression

In Simple Linear Regression, we try to find the relationship between **a single independent variable **(input) and **a corresponding dependent variable (output)**. This can be expressed in the form of a straight line.

The same equation of a line can be re-written as:

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