introduction to machine learning and deep learning over period over time and with experience--thus inducing intelligence without explicit programming the first question to ask is why we would be interested in the development of algorithms that improve their performance over timewith experience after allmany algorithms are developed and implemented to solve real-world problems that don' improve over timethey simply are developed by humansimplemented in softwareand get the job done from banking to ecommerce and from navigation systems in our cars to landing spacecraft on the moonalgorithms are everywhereanda majority of them do not improve over time these algorithms simply perform the task they are intended to performwith some maintenance required from time to time why do we need machine learningthe answer to this question is that for certain tasks it is easier to develop an algorithm that learns/improves its performance with experience than to develop an algorithm manually although this might seem unintuitive to the reader at this pointwe will build intuition for this during this machine learning can be broadly classified as supervised learningwhere training data with labels is provided for the model to learnand unsupervised learningwhere the training data lacks labels we also have semi-supervised learning and reinforcement learningbut for nowwe would limit our scope to supervised machine learning supervised learning can again be classified into two areasclassificationfor discrete outcomesand regressionfor continuous outcomes binary classification in order to further discuss the matter at handwe need to be precise about some of the terms we have been intuitively usingsuch as tasklearningexperienceand improvement we will start with the task of binary classification