# Deep Learning Optimizers

In Deep Learning the optimizers play an important role. It takes the key role in losses. Basically, the optimizers do reduce the losses. How do the optimizers play in Deep Learning? Well, coming to this deep learning model if any changes in weight in the input layer the output layer…

# Batch Normalization

Before going to our topic let see why Normalization is an essential thing for data. When coming to the data analysis/prediction part.

What is Data Normalization?

Today’s world is engaged with data from our day-to-day life. Say instance buying the products from Amazon, commenting on the products that you have…

# Transfer Learning

Introduction

As humans growing and learning in day-to-day activities right from childhood. As humans acquire knowledge by learning one task. By using the same knowledge we tend to solve the related task. Say in real-time scenarios as

• Know how to ride a motorbike ⮫ Learn how to ride a car

# What is Perceptron: A BeginnersTutorial For Perceptron

Perceptron algorithm used in supervised machine learning for classification. There are two types of classification. One will classify the data by drawing a straight line called a linear binary classifier. Another will be cannot classify the data by drawing the straight line called a non-linear binary classifier.

Artificial Neuron

In…

# Docker in Machine Learning

Today we are going to see a very interesting topic. In the modern world, AI plays a vital role in every domain. For instance say, the Retail business having a huge role in ML. …

# Naive Bayes

Naive Bayes is a probabilistic machine learning algorithm. It is used widely to solve the classification problem. In addition to that this algorithm works perfectly in natural language problems (NLP).

The Naive Bayes algorithm is based on the Bayes theorem. Let’s see what the theorem explains.

# Support Vector Machine

Introduction

The Support Vector Machine is one of the most popular supervised machine learning algorithms. Which is used for classification as well as regression. The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that we can…

# Hierarchical Clustering and Density-Based Spatial Clustering of Applications with Noise (DBSCAN)

Introduction

Hierarchical clustering is another unsupervised machine learning algorithm, which is used to group the unlabeled datasets into a cluster and also known as hierarchical cluster analysis or HCA.

In this algorithm, we develop the hierarchy of clusters in the form of a tree, and this tree-shaped structure is known…

# K-Means Clustering

Introduction

K-Means Clustering is an unsupervised machine learning algorithm which is used to solve the clustering problems in the machine learning. In real-world scenarios, the unlabelled data that might be exists to solve problems. In such cases, the K-means algorithm plays a vital role to solve the problem. Whereby taking…

# K-Nearest Neighbor

Introduction

K-nearest neighbors (KNN) is a type of supervised learning algorithm used for both regression and classification. KNN tries to predict the correct class for the test data by calculating the distance between the test data and all the training points. Then select the K number of points which is… ## Antony Christopher

Data Science and Machine Learning enthusiast | Software Architect | Full stack developer