Introduction to Machine Learning in R

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    Introduction to Machine Learning in R

    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

    Introduction to Machine Learning in R


    No prior programming knowledge is needed


    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


    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.

    Course information :

    h264, yuv420p, 1280×720 |ENGLISH, aac, 48000 Hz, 2 channels, s16 | 8h 32 mn | 1.12 GB
    Instructor: Holczer Balazs

    Course Link :

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