Month: May 2021

TOP USES OF UNSUPERVISED MACHINE LEARNING

What are the uses of Unsupervised Machine Learning?¬† There are many uses of unsupervised machine learning. Computers are becoming more complex day by day with the help of modern technologies. These techniques enable computers to learn without any direct human supervision, leading to fewer mistakes as well as faster performance from the machine. The more…

What is Data Image Processing in Machine Learning ?

Data image processing is one of the most common things used in Machine Learning. Machine Learning deals with making the training and testing of software more specific, so that the results will be better and more repeatable. One of the ways that this is done is by making the training more specific. When you train…

Significance of Feature Selection in Machine Learning

What is Feature Selection?  Feature selection in machine learning refers to the classification of data into multiple classes or features. The classification of data involves the decision to remove one class or feature, to add one class or feature, or to retain one class or feature. Essentially, it involves a trade-off between accuracy and speed….

What is Hyper Parameter Tuning in Machine Learning?

What is Hyper Parameter Tuning?  In machine learning, tuning or hyper parameter optimization is the difficulty of picking a collection of optimal parameters for a model learning algorithm. A hyper Parameter is also called a model predictor, since its value is used as a starting point for the model learning algorithm. Unlike the normal parameters,…

Role of Machine Learning in recognizing Handwriting

The basic concept of recognizing handwriting is based on the recognition of patterns. The most common handwriting patterns are alphabets and numbers. Such recognition systems may be used to recognize all types of writing. But, there must be a good reason for the usage of such recognition systems and these should be consistent and systematic….

Quantile Regression in Machine Learning

What is Quantile Regression?  Quantile regression is a way of statistical analysis that compares historical data points (data set size, number of data points, kind of data, etc.) to the expected outcome. The output from the analysis is what we want to measure and this can be used for all kinds of data analysis purposes:…

Model Evaluation Metrics in Machine Learning

Model evaluation metrics are important in the field of Machine Learning. Different types of models, such as supervised and unsupervised learning can be evaluated using these metrics. Evaluation is necessary in all Machine Learning methods to ensure that the models that have been trained to provide the desired output. It also enables a trainer to…

Models of Simplified Reinforcement Learning in Data Science

What is Simplified Reinforcement Learning? Reinforcement Learning in Data Science deals with models that can be used in the learning of reinforcement from a given reward to the next reward.  Types of Simplified Reinforcement Learning Monopiece Model¬† The most basic form of this is called the Monopiece Model. This form applies the concept of acting…

ML Algorithms in Python

The use of big data has made it possible for machine learning developers to leverage their existing programming skills in order to create new programs. Machine learning is a subset of computer science that refers to the study of software algorithms that yield useful results from relatively small amounts of data. Algorithms are very specific…

Importance of Data Mining in Big Data

Data Mining in Big Data is important due to the massive impact it has on businesses. Companies are leveraging their data to achieve a number of business goals, such as better customer service, efficient operations, and even better outcomes for products or services. But how does data mining help these companies? And how should data…

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