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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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.
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Course Outline
This section defines Machine Learning and explains it with an example.
This section discusses Supervised and Unsupervised Machine Learning methods to accomplish various tasks.
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.
This section explains the Linear Regression algorithm with demonstrated example.
This section explains the Naive Bayes algorithm with demonstrated examples.
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