Machine Learning Algorithms

Overview

What you'll learn

• Classification (Logistic Regression
• Decision Trees
• SVM)
• Regression (Linear
• Ridge
• Lasso)
• Clustering (K-means
• Hierarchical)
• model evaluation
• cross validation

Syllabus 8

1. Introduction to Machine Learning

   This section defines Machine Learning and explains it with an example.

2. Types Of Machine Learning

   This section discusses Supervised and Unsupervised Machine Learning methods to accomplish various tasks.

3. 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.

4. Linear Regression Algorithm

   This section explains the Linear Regression algorithm with demonstrated example.

5. Naïve Bayes Algorithm

   This section explains the Naive Bayes algorithm with demonstrated examples.

6. KNN Algorithm in Machine Learning

   This section explains the KNN algorithm with demonstrated examples.

7. Support Vector Machines in Machine Learning

   This section explains Support Vector Machine with demonstration example and discusses its applications.

8. Random Forest Algorithm in Machine Learning

   This section explains the Random Forest algorithm with demonstrated example.

Advantages and disadvantages

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Taught by

• Mr. Anirudh RaoAnirudh has been working in the field of Data Science and has expertise over Python, Machine Learning and other concept…