Machine Learning R Programming

  1. Introduction
  2. Installation of R Console
  3. Installation of R Studio
  4. Package Configuration
  5. System Configuration
  1. Vectors
  2. Matrices
  3. Dataframes
  4. Lists
  1. Numeric Functions
  2. Character Functions
  3. Date Functions
  4. User Defined Functions
  • Computing New Variables
  • Duplicate Values
  • Sorting
  1. List input
  2. Labelling
  3. Numeric Formatting
  4. Character Formatting
  5. Date Formatting
  1. Merge
  2. Append
  1. If-else statements
  2. For loops
  3. While loops
  4. Repeat, Next, Break
  1. Scatterplots, Histograms, Barcharts, Dotplots
  2. Labels, Legends, Titles, Axes
  1. Introduction
  2. Need of Machine Learning
  3. Difference between Statistics and Machine Learning
  4. Popular libraries in Machine Learning
  • Basic Statistics
  • Hypothesis Testing
  • T Test, Z Test, F Test
  • Correlation
  • Hypothesis Testing
  • ANOVA, MANOVA, ANCOVA
  1. Linear regression
    1. Introduction
    2. Assumption Testing
    3. Interpretation
    4. Validation
    5. Implementation
  2. Logistic regression
    1. Introduction
    2. Difference between linear and logistic
    3. Variable Selection Methods
    4. Interpretation
    5. Validation
  3. Cluster Analysis
    1. Introduction
    2. Hierarchal Clustering
    3. K-Means Clustering
    4. Finalization of Clusters
    5. Interpretation
    6. Output Validation
  4. Time Series
    1. Introduction
    2. Components
    3. Methods of Forecasting
    4. ARIMA/ARMA
    5. Interpretation
    6. Validation
    7. Forecasting
  5. Decision Tree
    1. Introduction
    2. Pruning of Trees
    3. CHAID
    4. CART
    5. C
    6. Interpretation
    7. Ensemble Method
  6. Random Forest
    1. Introduction
    2. Random Forest for Regression
    3. Random Forest for Classification
    4. Interpretation
    5. Validation
  7. Text Analytics & Word Cloud
    1. Tokenization
    2. Term Document Matrix
    3. Stemming
    4. Corpus
    5. Different Text Cleansing Method
  8. Sentimental Analysis using Twitter Data
    1. Introduction
    2. Setting Twitter Development Login and account
    3. Generating API and Secret Keys
    4. Extracting Data from Twitter
    5. Library for Positive and Negative Keywords
    6. Text Cleansing
    7. Classification of Negative and Positive Keywords
Course Detail
Fees
Rs 23,000

 

Class Mode
Classroom/Online

 

Duration
3 Months

 

Course Contents
Machine learning R Programming

 

Course Benefits
Student will able to use R Language and Perform in R Programming

 

RS 23,000

3 Months

Machine Learning with Python

Python, Machine Learning, Artificial Intelligence, Pycharm, Jupyter

KEY FEATURES

2 Months of Classroom Training.

 

Session have assignments and daily task.

 

Recorded Videos and presentation for reference.

Books and related data sets in form of excel and csv.

 

Interview Questions for related topic.

 

Used Case Study and Projects to understand real scenarios.

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Data
Science

Machine
Learning

Artificial
Intelligence

Python
Programming

R
Programming

Data
Visualization