Udemy - Machine Learning on R 2021

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[ CourseLala.com ] Udemy - Machine Learning on R 2021
  • Get Bonus Downloads Here.url (0.2 KB)
  • ~Get Your Files Here ! 1. Introduction to Machine Learning on R
    • 1. Introduction to Machine Learning.mp4 (57.7 MB)
    • 1. Introduction to Machine Learning.srt (14.9 KB)
    2. Naive Bayes Theory and Application
    • 1. Naive Bayes Theory.mp4 (33.1 MB)
    • 1. Naive Bayes Theory.srt (9.5 KB)
    • 2. Naive Bayes Application in R.mp4 (104.1 MB)
    • 2. Naive Bayes Application in R.srt (12.9 KB)
    3. K-Nearest Neighbors (KNN) Theory and Application in R
    • 1. K-Nearest Neighbors Theory.mp4 (30.5 MB)
    • 1. K-Nearest Neighbors Theory.srt (8.5 KB)
    • 2. Application of K-Nearest Neighbors in R Part-1.mp4 (56.1 MB)
    • 2. Application of K-Nearest Neighbors in R Part-1.srt (4.4 KB)
    • 3. Application of K-Nearest Neighbors in R Part-2.mp4 (53.0 MB)
    • 3. Application of K-Nearest Neighbors in R Part-2.srt (5.0 KB)
    4. Random Forest Theory and Application
    • 1. Random Forest Theory.mp4 (33.1 MB)
    • 1. Random Forest Theory.srt (8.1 KB)
    • 2. Random Forest Application in R- Part 1.mp4 (38.6 MB)
    • 2. Random Forest Application in R- Part 1.srt (5.0 KB)
    • 3. Random Forest Application in R- Part 2.mp4 (67.5 MB)
    • 3. Random Forest Application in R- Part 2.srt (8.4 KB)
    5. Gradient Boosting Theory and Application
    • 1. Gradient Boosting Theory.mp4 (28.1 MB)
    • 1. Gradient Boosting Theory.srt (7.4 KB)
    • 2. Gradient Boosting Application in R.mp4 (112.2 MB)
    • 2. Gradient Boosting Application in R.srt (9.4 KB)
    6. Support Vector Machine Theory and Application in R
    • 1. Support Vector Machine Theory.mp4 (52.4 MB)
    • 1. Support Vector Machine Theory.srt (11.3 KB)
    • 2. Support Vector Machine Application in R.mp4 (106.3 MB)
    • 2. Support Vector Machine Application in R.srt (10.4 KB)
    • Bonus Resources.txt (0.3 KB)

Description

Machine Learning on R 2021



MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 13 lectures (2h) | Size: 706.8 MB
A clear understanding about the machine learning theory, techniques and its application in R Studio platform
What you'll learn:
The program builds a solid foundation by covering the most popular and widely used machine learning technologies and its applications.
Students will have a good working knowledge on Machine Learning on R and can use it for their Educational as well as Business projects and assignments.
Most professionals opting for only this module generally are looking at automation techniques and applications for some daily work they are doing.
The Course Includes Naive Bayes theory, K Nearest Neighbors (KNN) theory and application, Random forest theory and application, Gradient Boosting Theory.

Requirements
Prior knowledge of R programming and Data Science on R is recommended

Description
There are people who are eager to move to Analytics careers but do not have the requisite skill sets. As we move into our 12th year in the Analytics Industry, OrangeTree Global has designed specific courses for freshers and working professionals who are looking at moving to Data Science, Machine Learning and Big Data Careers.

Since 2009, OrangeTree Global has embarked on an ambitious vision of providing affordable and effective Analytics Training and Education across the country.



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772.8 MB
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Udemy - Machine Learning on R 2021


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