Inside Ai

Explore the advantages of facebook new Prophet API developed for time series data specially is designed to be easier to use and find a good set of hyper parameters automatically in an effort to make skillful forecasts for data with trends without needing the requirement of special domain knowledge.

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So what is Prophet?

Prophet is an open source library published by Facebook designed to forecast Uni-variate time series data based on decomposition (trend+Seasonality+holidays) components. …


Data Journalism

Full Guide to 15 different types of clustering methods

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Hi there whatsup? I hope its all good!

Today we will take clustering into another level with more than 12 types of clustering methods.

Its goanna be a long chapter, shall we get started?

Here are the lists of clustering techniques that we will be going through:

1.) K-means

2.) H-Clust

3.) DBSCAN / Density-Based Spatial Clustering

4.) HDBSCAN / Hierarchical Density-Based Spatial Clustering

5.) Cure Algorithm

6.) Mini-Batch K-means Clustering

7.) Fuzzy Clustering

8.) Spectral Clustering

9.) Mean Shift

10.) BRICH / Balanced Iterative Reducing and Clustering using Hierarchies

11.)…


Ordering Points To Identify Cluster Structure (OPTICS) ) is a density based clustering technique that allows to partition data into groups with similar characteristics(clusters)

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Its addresses one of the DBSCAN’s major weaknesses. The problem of detecting meaningful clusters in data of varying density.

In a density based clustering, clusters are defined as dense regions of data points separated by low-density regions.

It adds two more terms to the concepts of DBSCAN clustering. They are:


Inside Ai

Explore the advantages of facebook new Prophet API developed for time series data specially is designed to be easier to use and find a good set of hyper parameters automatically in an effort to make skillful forecasts for data with trends without needing the requirement of special domain knowledge.

Image for post
Image for post

So what is Prophet?

Prophet is an open source library published by Facebook designed to forecast Uni-variate time series data based on decomposition (trend+Seasonality+holidays) components. …


Understand and perform Composite & Standalone LSTM Encoders to recreate sequential data.

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What are AutoEncoders?

AutoEncoder is an artificial neural network model that seeks to learn from a compressed representation of an input.

There are various types of autoencoders available suited for different types of scenarios, however the commonly used autoencoder is for feature extraction.

Combining feature extraction models with different types of models have a wide variety of applications.

Feature Extraction Autoencoders models for prediction sequence problems are quite challenging not because of the length of the input can vary, its because machine learning algorithms and neural networks are designed to work…


Understand and perform Composite & Standalone LSTM Encoders to recreate sequential data.

Image for post
Image for post

What are AutoEncoders?

AutoEncoder is an artificial neural network model that seeks to learn from a compressed representation of an input.

There are various types of autoencoders available suited for different types of scenarios, however the commonly used autoencoder is for feature extraction.

Combining feature extraction models with different types of models have a wide variety of applications.

Feature Extraction Autoencoders models for prediction sequence problems are quite challenging not because of the length of the input can vary, its because machine learning algorithms and neural networks are designed to work…


Data Journalism

Full Guide to 15 different types of clustering methods

Image for post
Image for post

Hi there whatsup? I hope its all good!

Today we will take clustering into another level with more than 12 types of clustering methods.

Its goanna be a long chapter, shall we get started?

Here are the lists of clustering techniques that we will be going through:

1.) K-means

2.) H-Clust

3.) DBSCAN / Density-Based Spatial Clustering

4.) HDBSCAN / Hierarchical Density-Based Spatial Clustering

5.) Cure Algorithm

6.) Mini-Batch K-means Clustering

7.) Fuzzy Clustering

8.) Spectral Clustering

9.) Mean Shift

10.) BRICH / Balanced Iterative Reducing and Clustering using Hierarchies

11.)…


Inside Ai

Apply State of the Art Deep Learning MLP models for predicting the sequence of numbers/time series data.

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Hi how are you doing? I hope its great.

Today let’s understand something new and advanced that i have learn. Deep Learning’s state of the art Multi-layer Perception Models (MLP in short) for Time Series Forecasting.

So what we will learn in this article.

1.) MLP for univariate time series forecasting.

2.) Multivariate MLP models further divided into

a.) Multiple Input series

b.) Multiple Parallel series

3.) Multi step MLP Models

4.) Multi step Multi-Step MLP Models further divided into

a.) Multiple Input…


Deep Learning

Quick and easy guide to solve regression problems with Deep Learnings’ different types of LSTMs

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Hi how are you doing, I hope its great likewise.

Today we will start off with a topic LSTM, which is a powerful type of neural network designed and optimized to handle sequence of time series data.

Long-Strong-Term Memory (LSTM) is the next generation of Recurrent Neural Network (RNN) used in deep learning for its optimized architecture to easily capture the pattern in sequential data.

In this article we will learn how to create different types of Long Short-Term Memory Network and later we will…


Inside Ai

Full guide to knn, logistic, support vector machine, kernel svm, naive bayes, decision tree classification, random forest, Deep Learning and even with Grid Search Multi-Classification.

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As usual,

Hi! How are you doing? I hope its great……

Today lets understand and perform all types of classification for Multi-Class/ Multi-Label target variable.

Let’s get started, we will use a dataset that have 7 types/categories of glass. The dataset is available at UCI https://archive.ics.uci.edu/ml/datasets/Glass+Identification

Number of Attributes: 10 (including an Id#) plus the class attribute

— all attributes are continuously valued

Attribute Information: 1. Id number: 1 to 214 2. RI: refractive index…

Bob Rupak Roy

Things i write about frequently on Medium: Data Science, Machine Learning, Deep Learning, NLP and many other random topics of interest. ~ Let’s stay connected!

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