Artificial Intelligence and Machine Learning

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Unit-1 Introduction to Data Science and AI & ML

  • Data Science, AI & ML
  • Use Cases in Business and Scope
  • Scientific Method
  • Modeling Concepts

 Unit 2- R Essentials (Tutorial) Programming

  • Commands and Syntax
  • Packages and Libraries
  • Introduction to Data Types
  • Importing and Exporting Data.
  • Control structures and Functions

Unit-3 Statistical Analysis Initial Data Analysis

  • Relationship between attributes: Covariance, Correlation Coefficient, Chi-Square
  • Probability (Joint, marginal and conditional probabilities)
  • Probability distributions (Continuous and Discrete)
  • Density Functions and Cumulative functions

Unit-4 Data Pre-processing and Preparation

  • Data Munging, Wrangling
  • Plyr packages
  • Cast/Melt

Unit-5: Data Quality and Transformation

  • Data imputation
  • Data Transformation (min-max, log transform, z-score transform
  • Binning, Classing, and Standardization
  • Outlier/Noise& Anomalies
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