How To ?

Cross validation is one way to avoid over fitting of data in machine learning . Cross validation works in such way : 1st Way : One Round Cross Validation :   in this data is divided into  50-50 portion ,one portion is for test set , one portion is validation of model that is prepared with the help of 50% of data . Leave one out cross validation(LOOCV)  : In this ,we leave one data set for cross validation  and rest data set for use for training of model . k-fold cross validation : In this k data set are used to trained  the model ,while k-1 are using for validate or test  the model .

Machine Learning : if you talk about artificial intelligence , then Machine learning is one way to feed artificial intelligence to obtain intelligence in making decision . Machine learning use statistics ,algebra ,probability and other mathematical related algorithm. so there is always question ,what we should  do to   use machine learning in daily life ,because if any where you are working ,you can use  AI ,but for that you should know Machine Learning ,Deep learning and Python programming and logic to fit all these in a pattern . so first we will go with machine learning basic concept : machine learning use  training data for making algorithm to predict future decision or daily routine decision . enough past data is feed into training set to make algorithm. Machine Learning information can be divided into following categories : 1: Unsupervised learning . 2: Supervised learning  3: Semi Supervised learning    Unsupervised learning  : When learning data contains o