Description
Data Analysis & Machine Learning with R
Duration:
5 Days
Course Level:
Intermediate
Target Audience
- Programmers
- Data Analysts and enthusiasts in Machine Learning, Data Science, or Deep Learning
- Statisticians who need to earn Data Analysis & Machine Learning with R
- Econometricians
Organizational Impact
- Enhanced ability to analyze complex data and derive actionable insights
- Improved decision-making through advanced statistical and machine learning techniques
- Increased efficiency in data processing and model development
- Strengthened data-driven strategies and business operations
- Development of a skilled team proficient in R for data analysis and machine learning
Personal Impact
- Mastery of R for advanced data analysis and machine learning applications
- Enhanced ability to apply statistical and machine learning methods to real-world problems
- Improved career prospects with expertise in a widely-used data analysis tool
- Increased confidence in handling and interpreting complex datasets
- Expanded skill set in statistical analysis and machine learning techniques
Course Objectives
- Understand and apply core statistical methods using R
- Develop and implement machine learning models for data analysis
- Gain proficiency in data wrangling, visualization, and exploration with R
- Evaluate and validate statistical and machine learning models
- Apply advanced techniques to solve real-world data analysis problems using R
- All these learnt on Data Analysis & Machine Learning with R
Module 1: Introduction to R
- Introduction to R and its capabilities
- Overview of R libraries and importing data
- Data cleaning and reading using R
- Working with variables, vectors, matrices, factors, data frames, lists, and arrays
- Understanding different data types in R
- Exploring various models in R
- Case Study: Analyze and clean sales data from a retail store to create a summary report. In Data Analysis & Machine Learning with R
Module 2: Introduction to Machine Learning
- Fundamentals of Machine Learning
- Supervised vs. Unsupervised Learning
- R libraries suitable for machine learning
- Linear and Logistic Regression using R
- Understanding robust models in machine learning
- Case Study: Build and evaluate a predictive model for customer churn using logistic regression, on Data Analysis & Machine Learning with R
Module 3: Data Mining in R
- K-Nearest Neighbour algorithm
- Decision Trees
- Logistic Regression in data mining
- Support Vector Machines (SVM)
- Outlier detection techniques
- Model evaluation metrics and methods
- Case Study: Use decision trees and SVM to identify fraudulent transactions in financial data
Module 4: Neural Networking Using R
- Understanding the basics of neural networks
- Introduction to activation functions, hidden layers, and hidden units
- Training a perceptron and its key parameters
- Limitations of single-layer perceptrons
- Multi-layer perceptron and backpropagation learning algorithm
- Using neural networks for real-world applications in R
- Case Study: Develop a neural network model to predict product demand based on historical sales data
Module 5: Clustering Analysis in R
- K-means Clustering techniques
- Hierarchical Clustering approaches
- Density-Based Clustering methods
- Gaussian Clustering Models
- Case Study: Segment customers based on purchase behavior using K-means and hierarchical clustering. All these in Data Analysis & Machine Learning with R
