three stages of machine learning machine learning can often be complex field we have different types of problems and tasks and different algorithms to solve them we also have complex mathstatsand logic that form the very backbone of this diverse field if you rememberyou learned in the first that machine learning is combination of statisticsmathematicsoptimizationlinear algebraand bunch of other topics but despair notyou do not need to start learning all of them right awaythese diverse set of machine learning practices can be mostly unified by simple three-stage paradigm these three stages arerepresentation evaluation optimization let' now discuss each of these steps separately to understand how almost all of the machine learning algorithms or methods work representation the first stage in any machine learning problem is the representation of the problem in formal language this is where we usually define the machine learning task to be performed based on the data and the business objective or problem to be solved usually this stage of the problem is masked as another stagewhich is the selection of the ml algorithm or algorithms (you might have multiple possible model representations at this phasewhen we select target algorithmwe are implicitly deciding on the representation that we want use for our problem this stage is akin to deciding on the set of hypothesis models,any of which can be the solution of our problem for examplewhen we decide the machine learning task to be performed is regression looking at our dataset and then select linear regression as our regression model then we have decided on the linear combination based relationship between the dependent and the independent variables another implicit selection made in this stage is deciding on the parameters/weights/coefficients of the model that we need to learn evaluation once we decide on the representation of our problem and possible set of modelswe need some judging criterion or criteria that will help us choose one model over the othersor the best model from set of candidate models the idea is to define metric for evaluation or scoring function\loss function that will help enable this this evaluation metric is generally provided in terms of an objective or an evaluation function (can also be called loss functionwhat these objective functions normally do is provide numerical performance value which will help us to decide on the effectiveness of any candidate model the objective function depends on the type of problem we are solvingthe representation we selectedand other things simple example would be the lower the loss or error ratethe better the model is performing optimization the final stage in the learning process is optimization optimization in this case can be simply described as searching through all the hypothesis model space representationsto find the one that will give us the most optimal value of our evaluation function while this description of optimization hides the vast majority of complexities involved in the processit is good way to understand the core principles the optimization method that we will normally use is dependent on the choice of representations and the evaluation function or functions fortunately we already have huge set of robust optimizers we can use once we have decided