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Articles by Mithilesh Pradhan
Page 5 of 5
How to split a Dataset into Train sets and Test sets in Python?
In this tutorial, we are going to learn about how to split a Dataset into a Train set and a Test set using Python Programming Introduction While creating Machine Learning and Deep Learning Models we may come across scenarios where we may want to do both training and well as evaluation on the same dataset. In such cases, we may want to divide our dataset into different groups or sets and use each set for one task or specific process (e.g. training). In such situations, we may make use of training/test sets. Need for Train and Test sets It is ...
Read MoreHow to Handle Imbalanced Classes in Machine Learning
In this tutorial, we are going to learn about how to handle imbalanced classes in ML. Introduction Generally speaking, class imbalance in Machine Learning is a case where classes of one type or observation are higher as compared to the other type. It is a common problem in Machine learning involving tasks such as fraud detection, ad click averts, spam detection, consumer churn, etc. It has a high effect on the accuracy of the model. Effects of Class Imbalance In case of such problems, the majority class overpowers the minority class while training the model. Since in such cases, one ...
Read MoreHow to Create simulated data for classification in Python
In this Tutorial we will learn how to create simulated data from classification in Python. Introduction Simulated data can be defined as any data not representing the real phenomenon but which is generated synthetically using parameters and constraints. When and why do we need simulated data? Sometimes while prototyping a particular algorithm in Machine Learning or Deep Learning we generally face a scarcity of good real-world data which can be useful to us. Sometimes there is no such data available for a given task. In such scenarios, we may need synthetically generated data. This data can also be from Lab ...
Read MoreHow to create Models in Keras?
In this article, we are going to learn about how to create Models in Keras Introduction Keras is an open-source library in Python which provides APIs for building Artificial Neural Network Models with great flexibility. Modelling in Keras can be done either using the Functional API or the Keras Sequential Model. Keras module is also available under the popular Tensorflow Library. Latest version and installation The latest version of keras as of writing this article is 2.1.0. Keras can be installed from PyPI repository using pip. Advantages of Keras for Modelling Keras is used for fast implementation due to ...
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