machine learning blogs

Supervised Machine Learning What Is Supervised Learning?  It is the machine learning algorithm that learns from labeled data. After the data is analyzed and learned, the algorithm determines which label should be given to new data supplied by the user based on pattern and associating the patterns to the unlabeled new data. Supervised Learning algorithm has two categories i.e Classification & Regression Classification predicts the class or category in which the data belongs to. e.g.: Spam filtering and detection, Churn Prediction, Sentiment Analysis, image classification. Regression predicts a numerical value based on previously observed data. e.g.: House Price Prediction, Stock Price Prediction. Classification Classification is one of the widely and mostly used techniques for determining class the dependent belongs to base on the one or more independent variables. For simple understanding, what classification algorithm does is it simply makes a decision boundary between data points

Getting started with Machine learning? In this article we know about the basics of Machine Learning.We will also discuss about most common and most frequently used terminologies in Machine learning that will help us in getting started with Machine Learning. Let's start What is Machine Learning? Actually Machine Learning is a computer science field that enables the computer or any other machine to learn without explicit programming. The main focus of machine learning is to provide algorithms that can be trained to accomplish a task. It is subset of artificial intelligence. Machine learning algorithms create a mathematical model based on sample data, known as "training data," so that predictions or decisions can be made without explicit programming for the task. What are the Types of Machine Learning problems? The most common classification of Machine Learning problems includes: Supervised Learning: The majority of practical machine learning problems uses su

Naive Bayes Classifier Tutorial What is Naive Bayes classifier? Naive Bayes Classifier is a classification technique based on Bayes’ Theorem. It is base on the principle that the predictors are independent of each other. In simple words, we can say that the Naive Bayes classifier assumes that the presence of a particular feature in a class is independent(Unrelated) with the presence of any other feature in the same class. Let's understand this concept by an example, suppose a fruit may be considered to be an orange if it is orange in color, approximately round, and about 2.5 inches in diameter. Here we can see that all of these properties independently contribute to the probability that this fruit is orange, even if these features depend on each other. This is the reason, why it is known as ‘Naive’. ( Naive meaning: Unaffected ). Naive Bayes algorithm is simple to understand and easy to build. It do not contain any complicated iterative parameter estimation. We can use naive bayes