All Articles

Grid Search for Model Tuning

In this article, I will take you through a very powerful algorithm in Machine Learning, which is the Grid Search Algorithm. It is mostly used in hyperparameters tuning and models selection in Machine Learning. Here…

GPU Can Speed Up Models

In this article, we will look at how to speed up your models by using a GPU. We will also see how to split the computations across multiple devices, including the CPU and numerous GPU…

NLP For WhatsApp Chats

Natural Language Processing or NLP is a field of Artificial Intelligence which focuses on enabling the systems for understanding and processing the human languages. In this article, I will use NLP to analyze my WhatsApp…

PyTorch for Deep Learning

PyTorch is a library in Python which provides tools to build deep learning models. What python does for programming PyTorch does for deep learning. Python is a very flexible language for programming and just like…

PDF with Python

In Data Science, you must have seen people reading CSV files and excel files to work with the data, but what about a PDF. Python is a very high level language that is the reason…

AutoML: Automated Machine Learning

Machine Learning has been an outstanding achievement in the field of Artificial Intelligence. The algorithms behind the success of Machine Learning are Deep Neural Networks which were made after research of years through the expert…

Deploy a TensorFlow Model to a Mobile

If you want to deploy your TensorFlow model to a mobile or embedded device, a large model may take too long to download and use too much RAM and CPU, all of which will make…

Deploy a Machine Learning Model

I have trained and developed a lot of Machine Learning models, if you are a student in Machine Learning, you must have also developed models. In this article, I will train and Deploy a Machine…

PySpark in Machine Learning

PySpark is the API of Python to support the framework of Apache Spark. Apache Spark is the component of Hadoop Ecosystem, which is now getting very popular with the big data frameworks. Apache Spark is…

Predict Diabetes with Machine Learning

According to the report of Centers of Disease Control and Prevention about one in seven adults in the United States have Diabetes. But by next few years this rate can move higher. With this in…

K-Means in Machine Learning

Many clustering algorithms are available in Scikit-Learn and elsewhere, but perhaps the simplest to understand is an algorithm known as k-means clustering, which is implemented in sklearn.cluster.KMeans. Introduction The k-means algorithm searches for a pre-determined number…

Employee Turnover Prediction

This article features the implementation of an employee turnover analysis that is built using Python’s Scikit-Learn library. In this article, I will use Logistic Regression and Random Forest Machine Learning algorithms. At the end of…

Customer Segmentation

If you want to find out who are your best customers, using an old technique RFM matrix principle is still the best in the business. RFM means – Recency, Frequency and Monetary. RFM is basically…

Time Series Forecasting

Time Series Forecasting

Many Business activities are seasonal in nature, where most of the business are dependent on a particular time of festival and holidays. Every business uses sales promotion techniques to increase the demand for their products…

TensorFlow

TensorFlow Tutorial

TensorFlow is a powerful library for numerical computation, particularly well suited and fine-tuned for large–scale Machine Learning ( but you could use it for anything else that requires heavy calculations). The Google Brain team developed…

Reinforcement Learning

Reinforcement Learning

Reinforcement Learning (RL) is one of the most exciting fields of machine learning today. and also one the oldest. It has been around since the 1950s, producing many exciting applications over the years, particularly in…

Merging Datasets

Merging Datasets

Merging Datasets is one of the most high-performance features, which is provided by pandas in Python. In this article, I will show how we can merge datasets in Python with the help of examples and…

Model Selection

Model Selection Technique

Evaluating a model is simple enough to use a test set. But suppose you are hesitating in model selection between two types of models (say, a linear model and a polynomial model); how can you…

Handling Missing Data in data Science

Missing Data Handling

There is a lot of difference between the data you get to practice data science skills and the data you get in the real world. Honestly speaking, many datasets you will get in the process…

Training and Test sets

Training and Test Sets

This article is about description for those who need to know what is the actual difference between the dataset split between the Training and Test sets in Machine Learning while training and classifying models. What…