of machine learning processes and you need to change your thinking too from the traditional based approaches toward being more data-driven the beauty of machine learning is that it is never domain constrained and you can use techniques to solve problems spanning multiple domainsbusinessesand industries alsoas depicted in figure - you always do not need output data points to build modelsometimes input data is sufficient (or rather output data might not be presentfor techniques more suited toward unsupervised learning (which we will discuss in depth later on in this simple example is trying to determine customer shopping patterns by looking at the grocery items they typically buy together in store based on past transactional data in the next sectionwe take deeper dive toward understanding machine learning understanding machine learning by nowyou have seen how typical real-world problem suitable to solve using machine learning might look like besides thisyou have also got good grasp over the basics of traditional programming and machine learning paradigms in this sectionwe discuss machine learning in more detail to be more specificwe will look at machine learning from conceptual as well as domain-specific standpoint machine learning came into prominence perhaps in the when researchers and scientists started giving it more prominence as sub-field of artificial intelligence (aisuch that techniques borrow concepts from aiprobabilityand statisticswhich perform far better compared to using fixed rule-based models requiring lot of manual time and effort of courseas we have pointed out earliermachine learning didn' just come out of nowhere in the it is multi-disciplinary field that has gradually evolved over time and is still evolving as we speak brief mention of history of evolution would be really helpful to get an idea of the various concepts and techniques that have been involved in the development of machine learning and ai you could say that it started off in the late and the early when the first works of research were published which basically talked about the bayestheorem in fact thomas bayesmajor work"an essay towards solving problem in the doctrine of chances,was published in besides thisa lot of research and discovery was done during this time in the field of probability and mathematics this paved the way for more ground breaking research and inventions in the th centurywhich included markov chains by andrey markov in the early sproposition of learning system by alan turingand the invention of the very famous perceptron by frank rosenblatt in the many of you might know that neural networks had several highs and lows since the and they finally came back to prominence in the with the discovery of backpropagation (thanks to rumelharthintonand williams!and several other inventionsincluding hopfield networksneocognitionconvolutional and recurrent neural networksand -learning of courserapid strides of evolution started taking place in machine learning too since the with the discovery of random forestssupport vector machineslong short-term memory networks (lstms)and development and release of frameworks in both machine and deep learning including torchtheanotensorflowscikit-learnand so on we also saw the rise of intelligent systems including ibm watsondeepfaceand alphago indeed the journey has been quite roller coaster ride and there' still miles to go in this journey take moment and reflect on this evolutional journey and let' talk about the purpose of this journey why and when should we really make machines learnwhy make machines learnwe have discussed fair bit about why we need machine learning in previous section when we address the issue of trying to leverage data to make data-driven decisions at scale using learning algorithms without focusing too much on manual efforts and fixed rule-based systems in this sectionwe discuss in more detail why and when should we make machines learn there are several real-world tasks and problems that humansbusinessesand organizations try to solve day in and day out for our benefit there are several scenarios when it might be beneficial to make machines learn and some of them are mentioned as follows