In this chapter, we have gained a high-level view of various basic building blocks and subcomponents involved in statistical modeling and machine learning, such as mean, variance, interquartile range, p-value, bias versus variance trade-off, AIC, Gini, area under the curve, and so on with respect to the statistics context, and cross-validation, gradient descent, and grid search concepts with respect to machine learning. We have explained all the concepts with the support of both Python and R code with various libraries such as numpy
, scipy
, pandas
, and scikit- learn
, and the stats
model in Python and the basic stats
package in R. In the next chapter, we will learn to draw parallels between statistical models and machine learning models with linear regression problems and ridge/lasso regression in machine learning using both Python and R code.
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Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, analytics and insights, innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. He is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.
Read more about Pratap Dangeti
Author (1)
Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, analytics and insights, innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. He is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.
Read more about Pratap Dangeti