Probability and Statistics- the terms which resonate together to create the vast applications of the fields of Data Science and Machine Learning, have immensely grown a huge followers' base in this era. But programming the concept sometimes gets tricky and requires a lot of contemplation on the code. Allen B. Downey, in his'Think' series has written a book to solve just the problem for everyone.

Bagging (bootstrap aggregating) regression trees is a technique that can turn a single tree model with high variance and poor predictive power into a fairly accurate prediction function. Unfortunately, bagging regression trees typically suffers from tree correlation, which reduces the overall performance of the model. Random forests are a modification of bagging that builds a large collection of de-correlated trees and have become a very popular "out-of-the-box" learning algorithm that enjoys good predictive performance. This tutorial will cover the fundamentals of random forests. This tutorial serves as an introduction to the random forests.

So far we've analyzed the Harry Potter series by understanding the frequency and distribution of words across the corpus. This can be useful in giving context of particular text along with understanding the general sentiment. However, we often want to understand the relationship between words in a corpus. What sequences of words are common across our text? Given a sequence of words, what word is most likely to follow?