Introduction to Machine Learning in R, Machine learning, neural networks, regression, SVM, naive bayes classifier, bagging, boosting, random forest classifier
Publisher : Holczer Balazs
Course Length : 8.5 hours
Course Language : English
Description
This course is about the fundamental concepts of machine learning, facusing on neural networks. This topic is getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detect cancer for example. We may construct algorithms that can have a very good guess about stock prices movement in the market.
Section 1:
- R basics
- data visualization
- machine learning basics
Section 2:
- linear regression and implementation
Section 3:
- logistic regression and implementation
Section 4:
- k-nearest neighbor classifier and implementation
Section 5:
- naive bayes classifier and implementation
- support vector machines (SVMs)
Section 6:
- tree based approaches
- decision trees
- random forest classifier
Section 7:
- clustering algorithms
- k means clustering and hierarchical clustering
- boosting
Section 8:
- neural networks in R
- feedforward neural networks and its applications
- credit scoring with neural networks
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Who is the target audience?
- This course is mean for newbies who are familiar with R and looking for some advanced topics. No prior programming knowledge is needed.
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