Machine Learning with R
- Offered byKnowledgeHut
Machine Learning with R at KnowledgeHut Overview
Duration | 40 hours |
Start from | Start Now |
Total fee | ₹59,990 |
Mode of learning | Online |
Official Website | Go to Website  |
Credential | Certificate |
- Overview
- Highlights
- Course Details
- Curriculum
- Admission Process
Machine Learning with R at KnowledgeHut Highlights
- Earn a certificate after completion of course
- Fee can be paid in installments
Machine Learning with R at KnowledgeHut Course details
Individuals interested in learning ML algorithms for real life business problems
Software or Data Engineers interested in learning quantitative analysis and ML
Understand the behavior of data as you build significant models
Learn about the various libraries offered by R to manipulate, preprocess and visualize data
Supervised, Unsupervised Machine Learning and relation of statistical modelling to machine learning
Learn to use optimization techniques to find the minimum error in your machine learning model
Learn various machine learning algorithms like KNN, Decision Trees, SVM, Clustering in detail
Implement algorithms and R libraries such as CRAN-R in real world scenarios
Learn the technique to reduce the number of variables using Feature Selection and Feature Extraction
Learn to use multiple learning algorithms to obtain better predictive performance
Learn to implement Association Rule. Use Apriori Algorithm to find associations with key metrics
KnowledgeHut brings you a comprehensive course that will help you go from basic to advanced concepts in Machine Learning using R, the language that was built by statisticians, for statisticians
Learn to build systems that learn from experience, and exploit data to create simple predictive models of the world
Machine Learning with R looks into Supervised vs Unsupervised Learning, the ways in which Statistical Modeling relates to Machine Learning and carries out a comparison of each using R libraries
You will master not only the theory but also see how it is applied in the industry by learning to build predictive models using Machine Learning techniques
Class Schedule
09:00 AM - 01:00 PM (Weekend)
Machine Learning with R at KnowledgeHut Curriculum
1. Statistical Learning
Statistical analysis concepts
Descriptive statistics
Introduction to probability and Bayes theorem
Probability distributions
Hypothesis testing & scores
2. R for Machine Learning
Intro to R Programming
Installing and Loading Libraries
Data Structures in R
Control & Loop Statements in R
Functions in R
Loop Functions in R
String Manipulation & Regular Expression in R
Working with Data in R
Data Visualization in R
Case Study
3. Introduction to Machine Learning
Machine Learning Modelling Flow
Types of Machine Learning
Performance Measures
Bias-Variance Trade-Off
Overfitting & Underfitting
How to treat Data in ML
4. Optimization
Maxima and Minima
Cost Function
Learning Rate
Optimization Techniques
5. Supervised Learning
Linear Regression
Case Study
Logistic Regression
Case Study
K-NN Classification
Case Study
Naive Bayesian classifiers
Case Study
SVM - Support Vector Machines
Case Study
6. Unsupervised Learning
Clustering approaches
K Means clustering
Hierarchical clustering
Case Study
7. Ensemble Techniques
Decision Trees
Case Study
Introduction to Ensemble Learning
Different Ensemble Learning Techniques
Bagging
Boosting
Random Forests
Case Study: Heterogeneous Ensemble Machine Learning
PCA (Principal Component Analysis) and Its Applications
Case Study: PCA/FA
8. Recommendation Systems
Introduction to Recommendation Systems
Types of Recommendation Techniques
Collaborative Filtering
Content based Filtering
Hybrid RS
Performance measurement
Case Study
Machine Learning with R at KnowledgeHut Admission Process
Important Dates
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