in subsequent besides thiswe also cover the different inter-disciplinary fields associated with machine learningwhich are in fact related fields all under the umbrella of artificial intelligence this book is more focused on applied or practical machine learninghence the major focus in most of the will be the application of machine learning techniques and algorithms to solve real-world problems hence some level of proficiency in basic mathematicsstatisticsand machine learning would be beneficial however since this book takes into account the varying levels of expertise for various readersthis foundational along with other in part and ii will get you up to speed on the key aspects of machine learning and building machine learning pipelines if you are already familiar with the basic concepts relevant to machine learning and its significanceyou can quickly skim through this and head over to "the python machine learning ecosystem,where we discuss the benefits of python for building machine learning systems and the major tools and frameworks typically used to solve machine learning problems this book heavily emphasizes learning by doing with lot of code snippetsexamplesand multiple case studies we leverage python and depict all our examples with relevant code files pyand jupyter notebooks ipynbfor more interactive experience we encourage you to refer to the github repository for this book at necessary code and datasets pertaining to each you can leverage this repository to try all the examples by yourself as you go through the book and adopt them in solving your own real-world problems bonus content relevant to machine learning and deep learning will also be shared in the futureso keep watching that spacethe need for machine learning human beings are perhaps the most advanced and intelligent lifeform on this planet at the moment we can thinkreasonbuildevaluateand solve complex problems the human brain is still something we ourselves haven' figured out completely and hence artificial intelligence is still something that' not surpassed human intelligence in several aspects thus you might get pressing question in mind as to why do we really need machine learningwhat is the need to go out of our way to spend time and effort to make machines learn and be intelligentthe answer can be summed up in simple sentence"to make data-driven decisions at scalewe will dive into details to explain this sentence in the following sections making data-driven decisions getting key information or insights from data is the key reason businesses and organizations invest heavily in good workforce as well as newer paradigms and domains like machine learning and artificial intelligence the idea of data-driven decisions is not new fields like operations researchstatisticsand management information systems have existed for decades and attempt to bring efficiency to any business or organization by using data and analytics to make data-driven decisions the art and science of leveraging your data to get actionable insights and make better decisions is known as making data-driven decisions of coursethis is easier said than done because rarely can we directly use raw data to make any insightful decisions another important aspect of this problem is that often we use the power of reasoning or intuition to try to make decisions based on what we have learned over period of time and on the job our brain is an extremely powerful device that helps us do so consider problems like understanding what your fellow colleagues or friends are speakingrecognizing people in imagesdeciding whether to approve or reject business transactionand so on while we can solve these problems almost involuntarycan you explain someone the process of how you solved each of these problemsmaybe to some extentbut after while