Explore and Learn Machine Learning Concept

Let's discuss some terms and their definitions related to statistics and probability. It will help us in brushing our concepts of probability which are essential for machine learning algorithms Definition of Probability  Probability is a numerical measurement which indicates the chances of occurrence of an event, say A. It is denoted by P(A). It is the ratio of favorable outcomes of an event A say m to the total outcomes of the experiment say n. P(A) = m/n where m represents the number of favorable outcomes of an event A and n is the total number of outcomes of the experiment. Let's understand the experiment term in more details now. An operation that results in a definite outcome is called an experiment E.g.  Tossing a coin is an experiment as it can have two outcomes either Head or Tail and it is definite in number. Throwing a fair dice is an experiment as it can have only 6 outcomes which is definite in number. Random Experiment When the outcome of an ex...

In my last blog we have discussed how we can use TF - IDF method implementation using python for more details refer [ TF - IDF Implementation Using Python  ]. In this blog we will discuss how to deal with spelling correction to do the stemming or lemmatization effectively.  There is a concept known as "Edit Distance". "Edit distance is the minimum number of edits required to one string to another string". We can perform following types of operations Insertion of a letter Deletion of a letter Modification of a letter Let's take an example to understand this concept in more detail. "success" is written as "sucess". We have two strings one with length 7 [with correct spelling] and another with length 6 [ with incorrect spelling].  Step 1:   If the string of length M and N then we need to create the matrix of size (M+1) and (N+1). In our case we will create the matrix of size 7 X 8 as follows. Step 2: Initialize the first row and first column st...

In my last blog we have discussed how we can use TF - IDF method to extract the features of text, refer to - TF - IDF Method . Now we will see how can we implement the TF - IDF concept using python. Let's consider the same three sentences which we have discussed in our last blog to understand TF-IDF implementation in python. Kesri is a good movie to watch with family as it has good stories about India freedom fight. The success of it depends on the performance of the actors and story it has. There are no new movies releasing this month due to corona virus. The first step is to import the necessary libraries to perform the text processing. import pandas as pd from nltk.tokenize import word_tokenize from nltk.corpus import stopwords from sklearn.feature_extraction.text import TfidfVectorizer You must have already noticed that we have imported TfidfVectorizer to extract the text features using TF-IDF.  Second step is to store the sentences in the list: documents = ["Kesri is a go...