ML - Lecture 5 Classifiers Part - 2 | Rules Based k-Nearest Neighbour Classifier(k-nn) | Python

In the world of machine learning, classification is one of the most widely used techniques for solving a wide range of problems. In this video, we will discuss two popular classification algorithms: Naive Bayes and K-Nearest Neighbors (K-NN).

We will begin by exploring the Naive Bayes algorithm, which is a probabilistic algorithm that makes predictions based on the likelihood of an event occurring given the data. Naive Bayes is particularly useful for text classification problems, such as spam detection, sentiment analysis, and document classification.

Next, we will dive into the K-NN algorithm, which is a non-parametric algorithm that makes predictions based on the distance between data points in a dataset. K-NN is particularly useful for problems where the decision boundary is not linear, such as image classification or object recognition.

We will compare the strengths and weaknesses of both algorithms, and provide examples of when to use each one. Additionally, we will discuss how to choose the right hyperparameters for K-NN, such as the value of k and the distance metric used.

By the end of this video, you will have a clear understanding of two powerful classification algorithms and when to use them in your machine learning projects. So, if you're interested in learning more about classification algorithms, or just want to improve your machine learning skills, make sure to watch this video!

Tags:

Machine Learning
Classification
Based Classifier
k-Nearest Neighbour Classifier
k-NN
Data Science
Algorithms

Keywords:

Machine Learning classification algorithms
Supervised Learning
Unsupervised Learning
Based Classifier
Decision Tree Classifier
Random Forest Classifier
Support Vector Machine Classifier
k-Nearest Neighbour Classifier (k-NN)
Distance Metric
Euclidean distance
Manhattan distance
Minkowski distance
Data Science
Predictive modeling.