Course Content
1. Fundamentals
0/2
1.1 Statistics
1.2 Probability and Probability Distributions
2. Data Science using R Programming
0/13
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
  • Introduction to Linear Regression
  • Linear Regression with Multiple Variables
  • Disadvantage of Linear Models
  • Interpretation of Model Outputs
  • Understanding Covariance and Colinearity
Previous
Next