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Hey, y'all... I know its been long since I posted in this series been busy studying hard stuff about computer science for us! But here I am with our next module of machine learning algorithms. So sit back and learn something while relaxing!!! Enjoy the video.

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Today we are going to learn, Module 4: TensorFlow Core Learning Algorithms, which includes Classification, Clustering, and, Hidden Markov Models, so let's begin!!!

In the previous module, we learned that linear regression was used to predict a numeric value, where classification is used to separate data points into classes of different labels.

It is the most important aspect of a supervised machine learning. There are several algorithms to build a classification model such as logistic regression, which is used for binary classification. Next is Naive Bayes, where it makes the assumption that features of measurement are independent of each other and predict the probability of the class of dataset.

Next in classification would be a decision tree algorithm, which is used for both predictions and classification in machine learning by giving a set of inputs, one can map the various outcomes that are a result of the consequences or decisions.

Next in classification is the K Nearest Neighbours Algorithm. where we are given prior data then the model classifies the coordinates into groups that are identified by a specific attribute.

Further into classification comes Support Vector Machine Algorithm. which plots data points in the N dimension later performs classification and finds hyperplane which differentiates the two classes very well.

Random Forest is next in the list of classification algorithms like its name implies!! consists of a large number of individual decision trees that operate as an ensemble.

The next category of core machine learning algorithms is Clustering. which involves the grouping of data points. Take a moment and understand clustering in a real-life scenario. where, the grouping of customers based, on age or gender can be done, using clustering to understand the consumer market.

The last one is Hidden Markov Models, which works with probabilities to predict future events or states. Such as weather prediction!

In the Next Module, we will learn about Neural Networks with Tensor Flow. What is the neural network and how it works? So don't forget to like and subscribe guys. See ya! bye-bye.