Application of Classification Algorithms
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About this course
The Classification algorithm is a Supervised Learning technique that is used to identify the category of new observations on the basis of training data. In Classification, a program learns from the given dataset or observations and then classifies new observation into a number of classes or group. In this video you will learn about comparison between some of the most common and popular classification algorithms and see the factors that lead to comparison. You will also, see how to evaluate each and every algorithm’s performance based on a case study and finally summarize everything. So, if you feel the need to dig deeper into Real time application of classification algorithm, sit tight as we bring to you all the information you need right on your screens.
Course Outline
This module covers the most common interview questions on the Naïve Bayes algorithm such as its definition, the meaning of naïve in Naïve Bayes, its advantages, and comparison with other theorems. You will also understand its real-world applications, decision trees, and its relation with logistic regression.
This section explains logistic regression, its advantages and disadvantages, concordance, and difference between logit and logistic regression. Later you will understand the term error, collinearity, predictive modelling, and the importance of logistic regression. You will also understand to evaluate a logistic regression model.
This section discusses a well-known method of supervised learning which is the Decision Tree. You will also understand the advantages of using a decision tree. Later on, the tutor will cover some important questions based on regression in decision trees, random forest algorithms, gradient-boosted decision trees, unbalanced data, and tools used to draw decision trees.
Hive is a data warehouse used to support interaction between the user and HDFS. This course will give you a demonstration using sample problem statements for your better understanding.
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Will I get a certificate after completing this Application of Classification Algorithms free course?
Yes, you will get a certificate of completion for the Application of Classification Algorithms after completing all the modules and cracking the assessment. The assessment tests your knowledge of the subject and badges your skills.
How much does this Application of Classification Algorithms course cost?
It is an entirely free course from Great Learning Academy. Anyone interested in learning the basics of the Application of Classification Algorithms can get started with this course.
Is there any limit on how many times I can take this free course?
Once you enroll in the Application of Classification Algorithms course, you have lifetime access to it. So, you can log in anytime and learn it for free online.
Can I sign up for multiple courses from Great Learning Academy at the same time?
Yes, you can enroll in as many courses as you want from Great Learning Academy. There is no limit to the number of courses you can enroll in at once, but since the courses offered by Great Learning Academy are free, we suggest you learn one by one to get the best out of the subject.
Why choose Great Learning Academy for this free Application of Classification Algorithms course?
Great Learning Academy provides this Application of Classification Algorithms course for free online. The course is self-paced and helps you understand various topics that fall under the subject with solved problems and demonstrated examples. The course is carefully designed, keeping in mind to cater to both beginners and professionals, and is delivered by subject experts. Great Learning is a global ed-tech platform dedicated to developing competent professionals. Great Learning Academy is an initiative by Great Learning that offers in-demand free online courses to help people advance in their jobs. More than 5 million learners from 140 countries have benefited from Great Learning Academy's free online courses with certificates. It is a one-stop place for all of a learner's goals.
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