What is Ridge Regression?

Ridge regression was originally developed to deal with the problem of having more feature than data point. It can also be used to add bias into our estimations, giving better estimate. Shrinkage methods allow us to throw out unimportant parameters so that we can get a better feel and understanding of the data. It can... Continue Reading →

How To Clean Data?

Data can be defined as useful information or facts. In today's world data is the backbone of technology. Most advancements and new features are totally dependent on data. Data powers new age technology such as Machine learning, Artificial Intelligence, data processing and analytics,etc.  Why to clean Data? Data that we get from sources such as... Continue Reading →

The Pegasos Algorithm

Pegasos is an acronym for Primal Estimated sub-GrAdient Solver. This algorithm is used to solve optimization problems in Support Vector Machines (SVMs). It uses a form of stochastic gradient descent. The number of iterations required is determined by the accuracy we want, not the size of the dataset.  In SVMs we are trying to find... Continue Reading →

Issues in Decision Tree Learning

Overfitting Overfitting happens when the learning algorithm continues to develop hypothesis that reduce training set error at the cost of an increased test set error. Approach to avoid Overfitting Pre-pruning that stops growing the tree earlier, before it perfectly classifies the training set. Post-pruning allows the tree to perfectly classify the training set, and then... Continue Reading →

Applications Of Machine Learning

Learning Associations Learning associations is a method for discovering interesting relations between variables in large databases. It is used to identify regularities among large scale databases. The concept of learning association is mainly used in market analysis which is finding association between products bought by customers. If people who buy "x" typically also buy "y",... Continue Reading →

Types of Machine Learning

Photo by Miguel u00c1. Padriu00f1u00e1n on Pexels.com Supervised Learning Learning process with the help of a teacher is called supervised learning. In supervised learning the algorithm generates a function that maps inputs to desired outputs. One standard formulation of the supervised learning task is the classification problem: the learner is required to learn a function... Continue Reading →

Questions That Machine Learning Still Need To Answer

Photo by Anna Shvets on Pexels.com What algorithms exist for learning general target from specific training example ?In what settings will particular algorithms converge to the desired function, given sufficient training data ?Which algorithms perform best for which types of problems and representations ?How much training data is sufficient ?What general bounds can be found... Continue Reading →

Components of Expert System

Knowledge Base Knowledge is required to exhibit intelligence. The success of any ES majorly depends upon the collection of highly accurate and precise knowledge. Factual Knowledge Factual Knowledge is that knowledge of the task domain that is widely shared typically found in textbook or journals, and commonly agreed upon by those knowledgeable in the particular... Continue Reading →

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