Steps in building a ML model
ML MODEL : A model takes input (features) and gives an output(prediction), whose internal behaviour is learnt automatically from the data provided to it instead of being programmed manually. The main goal of a model is to design it in such a way that it works well on unseen or new data. STEPS : STEP-1 : Define the problem clearly. Ask these two questions: Q1) what are you trying to predict? Q2) What will the model see? There are four types of problems, Classification - classifies the input into a category. Ex- spam VS not spam, digits 0-9..... Regression - the output is a number Ex - predicting the house price,temperature... Generation -generates a new data Ex - generating text, image ... Clustering (unsupervised) - grouping data without labels Ex - customer segmentation 1.2 INPUTS AND OUTPUTS You should be able to say, What is your input , text/image/tabular data/time-series ..etc. And my output should be a single label/multiple label/image/text/number/ve...