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Understanding Principal Component Analysis In most practical applications of data science, you will end up with data with a lot of dimensions. It is not easy to process all these dimensions because of considerations of cost and processing power limitations. These data dimensions could be highly correlated to each other so you will lose a lot of time and money, without much to show for it. Want to be a smarter data scientist and work only with uncorrelated dimensions. You have to master Principal Component Analysis. It is one of the most used techniques for dimensionality reduction. In this Post, these are the steps we are going to take to master just that:- Understand the Important Math Terms Behind PCA Singular Value Decomposition Why Principal Component Analysis Works Step By Step Implementation of PCA Tips To Be Careful About with PCA   MATHS AND IMPORTANT TERMS TO KNOW BEFORE STARTING WITH[…]

Simple And Complete Tutorial for Naive Bayes Classifier With datasets smaller than 10,000 rows that has a very large number of features, Naive Bayes classifier might be the best bet for you, if you are sure that the features of the data set are independent of each other. The algorithm is not computationally expensive and gives a higher prediction accuracy. Table Of content Step by step approach to master Naive Bayes Classifier. Understanding Bayes Theorem Bayes theorem in terms of machine learning Understanding Naive Bayes Classifier Understanding Priors in Naive Bayes Classifier Different Kind of Distributions in Naive Bayes Classifier Assumptions we undertake in Naive Bayes Classifier Loss function in Naive Bayes Bias – variance tradeoff in naive bayes classifier   BAYES THEOREM Before we, go into Bayes theorem, let’s first brush up on our probability knowledge. The first thing we need to understand is Conditional Probability which implies, what is[…]