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Machine Learning with R
30 Hours
Online Instructor-led Training
USD 1399 (
About this Course
Machine learning is a branch in computer science that studies the design of algorithms that can learn. Typical machine learning tasks are concept learning, function learning or “predictive modeling”, clustering and finding predictive patterns. These tasks are learned through available data that were observed through experiences or instructions, for example. Machine learning hopes that including the experience into its tasks will eventually improve the learning. The ultimate goal is to improve the learning in such a way that it becomes automatic, so that humans like ourselves don’t need to interfere any more.
R is perfect for Machine Learning, Deep Learning programming, Probability and Statistic analysis due to simplicity, accomplished available standard libraries and easy learning, if you want to design and establish hyper parameters of above cases individually
R provides a scripting language with an odd syntax. There are also hundreds of packages and thousands of functions to choose from, providing multiple ways to do each task. It can feel overwhelming.
The best way to get started using R for machine learning is to complete a project.
When you are applying machine learning to your own datasets, you are working on a project.
The process of a machine learning project may not be linear, but there are a number of well-known steps:
Define Problem.
Prepare Data.
Evaluate Algorithms.
Improve Results.
Present Results.
Machine Learning with R
Course Details & Curriculum
Machine Learning using R Syllabus
Introduction to R
What is Machine Learning?
Features and Benefit of Machine Learning
Explain on use and abuse of Machine Learning
Real-time use case Scenarios
Explain on high level Machine Learning Algorithm
What is R?
Why need R?
Pre-requisite to install R
Installing R
Explain on Package and Library
Loading an R package
Environment Setup on R
Scoping Rules
GUI Editor and Command line interface
Explorer on R Studio
Getting helps and blog on R
Orange Techno mind Building Blocks on R
Variable declaration and Syntax on R
Scalars
Vectors
Factors
List
Arrays
Data Frames
Using Cbind,Rbind
Explain on Classes and Objects
Functions
Custom Functions and Arguments
IF-else statements
Lookup tables
Sequential steps
Explain on Parallel Cases
Looping statements like FOR, WHILE, REPEAT
Orange Techno mind Processing Data with R
Understanding Loading and Saving R data structure
Importing and Saving data from CSV files
Importing data from SQL database
Reading and Writing using Excel and SAS
Explain on Structure of data
Visualizing Numeric Variables
Explain Visualizing Relationships
Orange Techno mind Working with Regression and Analysis
Overview of Regression and Analysis
Benefit of Regression
Explain on Baseline Model
Liner Regression
Correlations
Multiple linear Regression
Explain on Regression and Model Trees
Polynomial Regression model with rlm
Definition Correlation
Bigger Regression Models with Biglm
Orange Techno mind Working with Regularization
Explaining on Regularization
Non-Linear Relationships
Test Regression
Orange Techno mind Lazy and Probabilistic Learning
Understanding nearest Neighbor Classification
Depth explain on K-NN Algorithm
Choose data for use with K-NN
Understanding Naive Bayes
Basic concept on Bayesian Methods
Explaining on Probability and joint Probability
Naive Bayes Algorithm
Orange Techno mind Working with Divide and Conquer
Explaining on Decision Trees
Decision Tree Algorithm
Classification Rules
Separate and Conquer
Explain on 1R Algorithm
Explain on RIPPER Algorithm
Orange Techno mind Working with Black Box Methods
What is Neural Networks?
Understanding on Neural Networks
Activation Function
Explain on Network Topology
Explain on Support Vector Machines
Orange Techno mind Working with Finding Patterns
What is Association Rules
Understanding Association Rules and Benefit
Explain on Apriori Algorithm
Setting up rules with Apriori Principle
Orange Techno mind Working with Model Performance
Overview of Model Performance
Classification on Measuring Performance
Prediction data with R
Measure Performance using Confusion Matrices
Explain on F-Measure
Explain on Improving Model Performance
Parallel Computing
Orange Techno mind Best Practices on Machine Learning and Interview Tips
Certification
Unknown
Unknown
Career Path
- R programmer
- Data Scientist
- Data Analyst
- Data Architect
- Data Visualization Analyst
- Geo Statisticians
- Database Administrator
- Quantitative Analysis with R
