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Introduction

GAN is one of the interesting and exciting innovation in Machine Learning. Generative Adversarial Network(GAN) is a class where two neural networks contesting with each other in the form of a zero-sum game. Given a training set, this technique learns to generate new data with the same statistics as the training set.

It is an unsupervised learning task in machine learning that involves automatically discovering and learning the patterns in input data in such a way that the model can be used to generate or output new examples that can be drawn from the original dataset. …


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Forecasting is one of the most common data science task used in time series data. It helps to know where the direction of the next data is.

Time-series analysis or forecasting may be a bit hard because there are different types of methods, parameters etc. Producing high-quality forecast is not an easy problem neither for machine nor data analysts.

Introduction

Facebook made a tool “Prophet” which helps to do time-series forecasting at a scale with ease.

Prophet does forecasting model based on an additive model where non-linear trends are fit with daily, weekly and yearly seasonality plus holidays.

Most of the…


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Image by: Alexandra Gorn Source:Unsplash

Introduction

In this world with more than 7.8 billions people, everyone is in their journey.

No one is free from problems whether they live in a large building or mud roof houses, whether they eat in gold plated plates or on leaves.

For each success matters a lot. Everyone wants to become successful.

The success here is measured in money they have, name, fame they have, supporters who care and properties they own. But if we go deep down inside of them then can we get some true answer?

Are all successful people satisfied with their life?

Are all happy?

The…


Natural Language Processing(NLP) has been one of the hottest topics in the field of AI. The advancements in it and research have made it more flexible and easier to deal with large data and sort out the result. From chatbot to language translation to every other aspect NLP has ruled in the field of AI. Natural Language Processing tasks flow from generating new articles to language translation and answering standardised test questions.

Recent research and work have shown substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific…


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Introduction

Convolutional Neural Network(CNN) is one of the deep learning algorithms used in recognition, classification, etc. most commonly applied to analyze visual images which is one of the difficult tasks on doing it, designing it, or implementing it. Here, each layer of the network is connected to all neurons in the next layer. From a biological experiment in the 1950s, there has been a lot of progress and advancement in this area. …


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Tilicho. Image by author

Long Short Term Memory Network is capable of learning long term dependencies. Mostly used for solving time-series data, they are capable of learning the patterns from the previous inputs and predicting future.

In Neural Network features are learned from data. LSTM also does the same. Designing the LSTM layer might be difficult some time. While designing it for the first time, we may stick in choosing the right number of layers, sizes, etc. While using LSTM for stock price prediction I really got difficult in designing it. Sometime my model used to over fit and sometime it under fit.

Designing…


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Source:https://www.reddit.com/r/datascience/comments/d6buto/kmeans_be_like_mine_mine_mine/

Like supervised data can be used for Predictive modelling, unsupervised data are mostly used for grouping together with similar features. Data with numerals are easier to handle. They can be used with label encoding or leaving as it is for the future. But with Categorical data!!!

Well, categorical data are the types of data which are present in categories like we say Name, Food Place, Group etc.

Let us take with an example of handling categorical data and clustering them using the K-Means algorithm.

We have got a dataset of a hospital with their attributes like Age, Sex, Final Diagnosis…

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