Machine Learning Algorithms
Enroll in this Machine Learning Algorithms course to understand the machine learning methods, algorithms, and techniques employed to analyze and present data for decision-making. Gain a finer hold through demonstrated projects.
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2.25 Hrs
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About this course
This online Machine Learning Algorithms course has been designed keeping in mind that a novice learner should be able to grasp the concepts and understand algorithms with examples. This course covers the introduction to Machine Learning and the basics of algorithms, along with a theoretical and practical understanding of supervised, unsupervised, and reinforcement learning. You will also gain skills to employ K-nearest Neighbor, Naive Bayes and Random Forest algorithms, and Linear Regression and Support Vector Machines (SVM) techniques to accomplish Machine Learning tasks. A tonne of practical Python demonstrations is offered to comprehend the concepts better. Extend your learning with Machine Learning PG courses and earn industry-relevant skills to elevate your contribution to your organization.
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Course Outline
Introduction to Machine Learning
This section defines Machine Learning and explains it with an example.
Types Of Machine Learning
This section discusses Supervised and Unsupervised Machine Learning methods to accomplish various tasks.
How does a Machine Learning Model Learn?
This section explains how a machine understands to work on a dataset to deliver desired results. It explains the role of pre-fed data set and the process involved in building a Machine Learning model.
Linear Regression Algorithm
This section explains the Linear Regression algorithm with demonstrated example.
Naïve Bayes Algorithm
This section explains the Naive Bayes algorithm with demonstrated examples.
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