Course Content
1. Fundamentals
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1.1 Statistics
1.2 Probability and Probability Distributions
2. Data Science using R Programming
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2.1 Introduction: What is Data Science?
2.2 Introduction to R
2.3 Basic Operations in R Programming
2.4 Data Handling in R Programming
2.5 R data structure
2.6 Using functions in R
2.7 Introduction To Machine Learning
2.8 Machine Learning Concepts & Terminologies
2.9 Linear Regression
2.10 Hypothesis Testing
2.11 Decision Trees And Supervised Learning
2.12 Unsupervised Classification Algorithms
2.13 Introduction to Deep Learning
Data Science based on R programming
About Lesson
  • Decision Tree – data set
  • How to build decision tree?
  • Understanding Kart Model
  • Classification Rules- Overfitting Problem
  • Stopping Criteria And Pruning
  • How to Find final size of Trees?
  • Model A decision Tree.
  • Naive Bayes
  • Random Forests and Support Vector Machines
  • Interpretation of Model Outputs
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