h264, yuv420p, 1280×720 |ENGLISH, aac, 48000 Hz, 2 channels, s16 | 8h 32 mn | 1.12 GB
Instructor: Holczer Balazs
Machine learning, neural networks, regression, SVM, naive bayes classifier, bagging, boosting, random forest classifier
Machine learning, neural networks, regression, SVM, naive bayes classifier, bagging, boosting, random forest classifier
What you’ll learn
Understand the basics of neural networks
Get a good grasp of machine learning fundamentals
Learn the basics of R
Learn the basics of machine learning techniques
Requirements
No prior programming knowledge is needed
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
Thanks for joining the course, let’s get started!
Who this course is for:
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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