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Course Description
This course will help you learn all the concepts of R and ML along with Supervised vs Unsupervised Learning, the ways in which Statistical Modeling relates to Machine Learning, and a comparison of each using R libraries.
What you'll learn in this course?
At the end of Machine Learning with R training course, participants will
• Understand the behavior of data as they 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
Course Curriculum
Statistical analysis concepts
Descriptive statistics
Introduction to probability and Bayes theorem
Probability distributions
Hypothesis testing & scores
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
Machine Learning Modelling Flow
Types of Machine Learning
Performance Measures
Bias-Variance Trade-Off
Overfitting & Underfitting
How to treat Data in ML
Maxima and Minima
Cost Function
Learning Rate
Optimization Techniques
Linear Regression
Case Study
Logistic Regression
Case Study
K-NN Classification
Naive Bayesian classifiers
SVM – Support Vector Machines
Clustering approaches
K Means clustering
Hierarchical clustering
Case Study
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
Introduction to Recommendation Systems
Types of Recommendation Techniques
Collaborative Filtering
Content based Filtering
Hybrid RS
Performance measurement
Case Study
LEARN AT YOUR OWN PACE
Training Options
Discover our range of training programs and choose the ones that suit you best. Enroll today and begin your learning journey with us!
