Machine Learning With R Full Course | Machine Learning Tutorial For Beginners | Edureka

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This "Machine Learning with R Full Course With R " video by Edureka will help you to understand the core concepts of Machine Learning and tell you how you can implement popular Machine Learning algorithms using R. Following pointers are covered in this Machine Learning With R Full Course:
00:00:00 Introduction
What is Machine Learning
Machine Learning Steps
Types of Machine Learning
What is Regression?
Logistic Regression Use Case
Linear Regression Use Case
What is KNN Algorithm?
KNN Algorithm Step-by-Step
What is SVM & How does it work?
What is K-Means Clustering & How does it work?
Naive Bayes Fundamentals
What is Classification & Types of Classifiers
How do Decision Trees & Random Forest work?
What is Time Series Analysis?
How does Sentiment Analysis work?
What is Data Mining & its Tasks
How to perform Data Mining using R
Machine Learning Projects
How to become a ML Engineer
Interview Questions

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About the Master's Program

This program follows a set structure with 6 core courses and 8 electives spread across 26 weeks. It makes you an expert in key technologies related to Data Science. At the end of each core course, you will be working on a real-time project to gain hands on expertise. By the end of the program you will be ready for seasoned Data Science job roles.

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Topics Covered in the curriculum:

Topics covered but not limited to will be : Machine Learning, K-Means Clustering, Decision Trees, Data Mining, Python Libraries, Statistics, Scala, Spark Streaming, RDDs, MLlib, Spark SQL, Random Forest, Naïve Bayes, Time Series, Text Mining, Web Scraping, PySpark, Python Scripting, Neural Networks, Keras, TFlearn, SoftMax, Autoencoder, Restricted Boltzmann Machine, LOD Expressions, Tableau Desktop, Tableau Public, Data Visualization, Integration with R, Probability, Bayesian Inference, Regression Modelling etc.

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For more information, please write back to us at sales@ or call us at: IND: 9606058406 / US: 18338555775 (toll free)
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