• 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 […]

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  • 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 […]

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  • 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 […]

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  • Create chatbot without writing a single line of code

    Create chatbot without writing a single line of code

    Yes, you read it right What is Dialogflow ? In short, Dialogflow is a natural language understanding platform that can be used to create conversational agents which can be used to create chatbots, audio response systems ..etc without writing a single line of “Code”. If you want to know more, here’s a trick = google […]

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  • 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 […]

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  • What Are The Steps In Developing A Machine Learning Applications ?

    Collect Data This include collecting the samples by scraping a website and extracting data, from an RSS feed or an API. Use necessary sensors or make use of publicly available data. Prepare Input Data Once data is collected make sure its in a useable format. The benefit of having this standard format is that you […]

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  • 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”, […]

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  • Types of Machine Learning

    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 which maps a vector into one of […]

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  • Questions That Machine Learning Still Need To Answer

    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 to relate […]

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  • 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 […]

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