** Machine Learning**

**Module 1: Introduction to Data Analytics**

- Business Analytics, Data, Information
- Understanding Business Analytics and R
- Compare R with other software in analytics
- Install R
- Perform basic operations in R using command line
- Learn the use of IDE R Studio
- Use the ‘R help’ feature in R

**Module 2: Introduction to R programming**

- Variables in R
- Scalars
- Vectors
- Matrices
- List
- Data frames
- Using c, Cbind, Rbind, attach and detach functions in R
- Factors

**Module 3: Data Manipulation in R**

- Data sorting
- Find and remove duplicates record
- Cleaning data
- Recoding data
- Merging data
- Slicing of Data
- Merging Data
- Apply functions

**Module 4: Data Import techniques in R**

- Reading Data
- Writing Data
- Basic SQL queries in R
- Web Scraping

**Module 5: Exploratory data Analysis**

- Box plot
- Histogram
- Pareto charts
- Pie graph
- Line chart
- Scatterplot
- Developing Graphs

**Module 6: Basics of Statistics & Linear & Logistic Regression**

- Basics of Statistics
- Inferencial statistics
- Probability
- Hypothesis
- Standard deviation
- Outliers
- Correlation
- Linear & Logistic Regression

**Module 7: Data Mining: Clustering techniques, Regression & Classification**

- Introduction to Data Mining
- Understanding Machine Learning
- Supervised and Unsupervised Machine Learning Algorithms
- K- means clustering

**Module 8: Anova & Sentiment Analysis**

- Anova
- Sentiment Analysis

**Module 9: Data Mining: Decision Trees and Random Forest**

- Decision Tree
- Concepts of Random Forest
- Working of Random Forest
- Features of Random Forest

**Module 10: Project work**