8. Introduction To Decision Trees

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Machine Learning has grown exponentially in terms of research and application in almost every surviving industry today. Although it is common to see ML on most profiles nowadays, it is very important to get your foundations strong before building a career out of it.

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Topics introduced are as follows:

1) Classification of Machine Learning
2) Regression (linear, quadratic & logistic)
3) Gradient Descent
4) Algorithms: Clustering (eg: K-Means)
5) Algorithms: Classification (eg: KNN, Gaussian Bayes)
7) Decision Trees
8) Support Vector Machines (SVM)

Prerequisites are basic knowledge of: Python programming, probability and calculus.

Видео 8. Introduction To Decision Trees канала ISTE NITK