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Multi Task Learning in Machine Learning
Machine learning is useful when it comes to solving complex problems from data. The issue is that data can also be scarce, and good labeled data can be expensive. It is also […]
XGBoost Algorithm Explained
Boosting algorithms become mainstream in the machine learning community. Boosting algorithms grant superpowers to machine learning models to improve their prediction accuracy.Tree boosting is a highly effective machine learning method. In this […]
Bias and Variance Tradeoff Machine Learning
Machine learning has more to offer than a set of techniques that can be applied to data. Theoretical concepts and principles in machine learning can provide broader insights to research and development.Bias […]
Dataset Splitting Techniques
For developing machine learning models, it is common to split the dataset into training and test subsets. The training set is used for fitting the model, that is, to estimate the hyperparameters […]
Artificial Intelligence Hardware Requirements
Artificial intelligence enables pervasive computing and becoming an omnipresent feature of everyday life.From datacenters to smartphones and personal assistants, there is a compelling need to develop bespoke hardware that is energy-efficient and […]
Adaptive Boosting Algorithm Explained
Adaptive Boosting or AdaBoost is one of the earliest implementations of the boosting algorithm. It forms the base of other boosting algorithms, like gradient boosting and XGBoost. In this post, we are […]
Data Analytics Meaning
Data is powerful, and organizations around the world understand the value that data analytics hold when it comes to driving organizational growth and profitability. Whether you’re a recent graduate or looking to […]
Data Splitting in Machine Learning
Dataset splitting is highly necessary to eliminate or reduce bias to training data in Machine Learning Models. This process is always done to prevent the machine learning algorithms from resulted into an […]
Confusion Matrix
When a Machine Learning model is built, various evaluation metrics are used to check the quality or the performance of a model. For classification models, metrics such as Accuracy, Confusion Matrix, Classification […]
Data Science Meaning
Data science is an interdisciplinary academic field that uses statistics, scientific computing, processes, algorithms to extrapolate knowledge and insights from potentially structured or unstructured data. Also Data science continues to evolve as […]
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