Machine Learning (ML) is a wonderful field at the intersection of computer programming, mathematics and domain knowledge. The author has observed that many budding machine learning students and enthusiasts make the mistake of jumping to build and work on algorithms without adequately understanding the math behind algorithms. That is not the right way to go about learning machine learning. One must first understand the mathematics and statistics concepts relevant to machine learning. The algorithms and the associated programming should be learnt subsequently. By mathematics, we are not referring to theoretical mathematics but rather applied mathematics.The following core concepts are covered in this book.
Measures of Central Tendency Vs. Dispersion
Mean Vs. Standard Deviation
Percentiles
Dependent Vs. Independent Variables
Types of data
Sample Vs. Population
Hypothesis testing and Type 1 & 2 Errors
Outliers, Box Plot and Data Transformation
ML conceptsConcepts related to algorithms are also covered in this book.
Measuring accuracy in algorithms
Math behind regression
Multi collinearity
Math behind decision tree
Math behind kNN