De-Mystifying Math and Stats for Machine Learning: Learn from an application perspective rather than the theoretical perspective

Description

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. DispersionMean Vs. Standard DeviationPercentilesDependent Vs. Independent VariablesTypes of dataSample Vs. PopulationHypothesis testing and Type 1 & 2 ErrorsOutliers, Box Plot and Data TransformationML conceptsConcepts related to algorithms are also covered in this book.Measuring accuracy in algorithmsMath behind regressionMulti collinearityMath behind decision treeMath behind kNNGradient descent and optimizationThese concepts are explained from an application perspective, that is how these concepts are applied in real life. The author is confident that understanding these concepts will help you to lay a solid foundation and build a thriving career in artificial intelligence.