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Machine Learning Algorithms
Master key machine learning algorithms: Naïve Bayes, Support Vector Machines, Decision Trees, and Clustering in this comprehensive course.
Course Cost
₹ 2,699
Beginner
Skill Level
13 Hours
Self-paced lessons
This course provides a comprehensive introduction to fundamental machine learning algorithms. Students will gain a deep understanding of Naïve Bayesian, Support Vector Machine, Decision Tree algorithms, and Clustering techniques. The curriculum covers theoretical foundations and practical applications of these algorithms, including probability concepts, kernel methods, regression trees, random forests, and Gaussian mixture models. Through a combination of video lectures, readings, and quizzes, learners will develop a solid grasp of how these algorithms work and when to apply them. The course assumes basic knowledge of Python programming and mathematics, including matrix multiplications and conditional probability. By the end, students will be equipped with essential skills to implement and utilize these core machine learning algorithms in real-world scenarios.
English
What you'll learn
Understand and implement the Naïve Bayesian algorithm
Master the concepts and application of Support Vector Machines
Explore Decision Tree algorithms, including regression trees and random forests
Learn various Clustering techniques, including k-means and Gaussian mixture models
Apply probability and conditional probability concepts in machine learning
Understand kernel methods and their use in Support Vector Machines
Implement and analyze hierarchical clustering methods
Develop skills in choosing appropriate algorithms for different machine learning tasks
Skills you'll gain
This course includes:
15 Hours PreRecorded video
13 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This course offers a comprehensive exploration of key machine learning algorithms, focusing on Naïve Bayesian, Support Vector Machine, Decision Tree, and Clustering techniques. The curriculum is structured to provide both theoretical understanding and practical application skills. Each module delves into the fundamental concepts, mathematical foundations, and implementation strategies of these algorithms. Students will learn about probability and conditional probability, Bayesian reasoning, kernel methods, regression trees, random forests, and various clustering approaches including k-means and Gaussian mixture models. The course emphasizes the importance of understanding when and how to apply each algorithm, along with their strengths and limitations. Through a combination of video lectures, in-depth readings, and interactive quizzes, students will develop a robust understanding of these core machine learning techniques, preparing them for advanced applications in data science and artificial intelligence.
Naïve Bayes
Module 1 · 3 Hours to complete
Support Vector Machine
Module 2 · 3 Hours to complete
Decision Tree
Module 3 · 3 Hours to complete
Clustering
Module 4 · 4 Hours to complete
Fee Structure
Payment options
Financial Aid
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Faculties
These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.
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Frequently asked Questions
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