Projects
Assignments
Overview
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.
Objectives
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
Prerequisites
- Elementary programming knowledge
- Familiarity with statistics
Course Outline
Statistical Learningedforce2021-06-30T14:50:55+05:30
- Statistical analysis concepts
- Descriptive statistics
- Introduction to probability and Bayes theorem
- Probability distributions
- Hypothesis testing & scores
R for Machine Learningedforce2021-06-30T14:50:52+05:30
- 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
Introduction to Machine Learningedforce2021-06-30T14:50:46+05:30
- Machine Learning Modelling Flow
- Types of Machine Learning
- Performance Measures
- Bias-Variance Trade-Off
- Overfitting & Underfitting
- How to treat Data in ML
Optimizationedforce2021-06-30T14:50:39+05:30
- Maxima and Minima
- Cost Function
- Learning Rate
- Optimization Techniques
Supervised Learningedforce2021-06-30T14:50:34+05:30
- Linear Regression
- Case Study
- Logistic Regression
- Case Study
- K-NN Classification
- Case Study
- Naive Bayesian classifiers
- Case Study
- SVM – Support Vector Machines
- Case Study
Unsupervised Learningedforce2021-06-30T14:49:50+05:30
- Clustering approaches
- K Means clustering
- Hierarchical clustering
- Case Study
Ensemble Techniquesedforce2021-06-30T14:49:36+05:30
- 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
Recommendation Systemsedforce2021-06-30T14:49:29+05:30
- Introduction to Recommendation Systems
- Types of Recommendation Techniques
- Collaborative Filtering
- Content based Filtering
- Hybrid RS
- Performance measurement
- Case Study
