Linear and Logistic regression are one of the most widely used Machine Learning algorithms. In this video on Linear vs Logistic Regression, you will get an idea about the basics of these tow algorithms and they differ in terms of concept and application. Finally, you will look at the two algorithms in action on two different datasets in a demo using Python. Let’s get started!

00:00:00 Introduction to Regression
00.01.28 Introduction to Classification
00.02.22. What is Linear Regression?
00.06.25 What is Logistic Regression?
00.10.25 Linear vs Logistic Regression

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A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.

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By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems

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