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Mechine Learning R

1.R Downloading and Installing R
Getting Help on a function
Viewing Documentation
General issues in R
Packages Management

2 Data Inputting in R Data Types
Subsetting
Writing data
Reading tabular data files
Reading from csv files
Creating a vector and vector operations
Initializing a data frame
Control structures
Selecting data frame cols by position and name
Changing directories
Re-directing R output

3 Basic Statistics in R
Computing basic statistics
Comparing means of two samples
Testing a correlation for significance
Testing a proportion
Classical tests (t,z,F)
ANOVA
Summarizing Data
Data Munging Basics
Cross tabulation

4 Data Visualization in R
Creating a bar chart, dot plot
Creating a scatter plot, pie chart
Creating a histogram and box plot
Other plotting functions
Plotting with base graphics
Plotting with Lattice graphics
Plotting and coloring in R

5 Data Manipulation in R
Appending data to a vector
Combining multiple vectors
List management
Merging dataframes
Data transformation
Strings and dates
Outlier detection
Handling NAs and Missing Values
Matrices and Arrays
Logical operations
Relational operators
Accessing Variables
Matrix Multiplication and Inversion
Managing Subset of data
Character manipulation
Data aggregation
Subscripting

6 Functions and Programming in R
Flow Control: For loop
If condition
While conditions and repeat loop
Debugging tools
Concatenation of Data
Combining Vars, cbind, rbind
Sapply, apply, tapply functions

7 R and Databases
Basics of SQL
RODBC and DBI Package
Performing queries
Advanced Data Handling
Combining and restructuring data frames

8 Statistical Modelling in R
Simple linear regression
Multiple Regression model
Logistic regression
Hierarchical Clustering
K-Means Clustering
PCA for Dimensionality Reduction


Part-I Descriptive Statistics:
• Introduction to Advanced Data Analytics
• Statistical inferences for various Business problems
• Types of Variables,
• measures of central tendency
• dispersion
• Variable Distributions
• Probability Distributions
• Normal Distribution and Properties
• Data transformation
• Data normalization


Part-II: Test of Hypothesis
• Null/Alternative Hypothesis formulation
• One Sample t Test
• two sample (Paired and Independent)
• Analysis of Variance (ANOVA),
• MANOVA
• Chi Square Test (Non Parametric Tests )
• Kruskal-Wallis, Mann-Whitney,
• Wilcoxon ,McNemar test


Part-III: Advanced Analytics
• Correlation - Karl Pearson
• Simple Regression
• Multiple Regression
• Logit Regression
• Multinominal Regression
• Regression Diagnostics
homoscedasticity,
Homogeneity,
Linearity


Part-IV: Factor Analysis
• Introduction to Factor Analysis – PCA
• Reliability Test
• KMO MSA tests, Eigen Value Interpretation,
• Rotation and Extraction
• Varimix Models


Part V: Cluster Analysis
• Introduction to Cluster Techniques
• Distance Methodologies, Hierarchical
Non-Hierarchical Procedures
• K-Means clustering
Wards Method