Updated on Apr, 2024
Language - English
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Machine Learning with R
Machine Learning with R
Development,ML & R programming
Lectures -148
Resources -26
Duration -20 hours
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Course Description
Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making.
This training is an introduction to the concept of machine learning and its application using the R tool.
The training will include the following:
- Introducing Machine Learning
- The origins of machine learning
- Uses and abuses of machine learning
- Ethical considerations
- How do machines learn?
- Steps to apply machine learning to your data
- Choosing a machine learning algorithm
- Using R for machine learning
- Forecasting Numeric Data – Regression Methods
- Understanding regression
- Example – predicting medical expenses using linear regression
- Collecting data
- Exploring and preparing the data
- Training a model on the data
- Evaluating model performance
- Improving model performance
Target Customers:
- Data analysts
- Data scientists
Prerequisites
What are the prerequisites for this course?
- No prior knowledge of machine learning required
- Basic knowledge of R tool
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
1 Lectures
-
Introduction to Machine Learning
10:08
10:08
Getting Started
3 Lectures
Basic Data Manipulation in R
9 Lectures
Simple Linear Regression and More of Statistics
12 Lectures
Stat and Prob Required for Machine Learning
8 Lectures
Multiple Linear Regression
6 Lectures
Generalized Linear Model and Generalized Least Square
3 Lectures
Knn (K-Nearest Neighbour) Algorithm
5 Lectures
Decision Tree Classifier and Pruning of Decision Trees
4 Lectures
Decision Tree Remaining
2 Lectures
Random Forest
6 Lectures
Kmeans Clustering
7 Lectures
Naive Bayes classifier
10 Lectures
Support Vector Machine
7 Lectures
Feature Selection
4 Lectures
Dimension Reduction - Principal Component Analysis
10 Lectures
Neural Networks
4 Lectures
Neural Networks A Model Building in R
5 Lectures
Time Series Analysis
17 Lectures
Gradient Boosting Machines
9 Lectures
Market Basket Analysis
10 Lectures
New Development
6 Lectures
Instructor Details
Corporate Bridge Consultancy Private Limited
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