Training a Machine Learning Model
In Modern Era everything is getting automated and usage of machines has increased a lot from past 4–5 years. It is much useful to make machines learn and perform tasks within seconds as humans need long hours to make it done.
Humans learn quick, make it slow. But Machines learn slow but makes fast.As we have humongous amount of work to be done even if learning is slow we prefer making it quicker. That is the reason we prefer machines to work on it. For machine to do all those things first of all we need to train it on that type of data.
For example, Consider a case we need to separate dogs and cats from large folder with mixed data of cats as dogs (this example is considered as it is most considered problem to classify dogs and cats) we have to train it with all different types of dogs and cats existed. So that it learn all the patterns based on the trained data and when it is performing it gives prediction with a better accuracy.
There are many things to be taken into account while training a model, starting with which selection of framework till we get the prediction. A Machine Learning Framework is an interface, library or tool which allows developers to more easily and quickly build machine learning models.Some of the most used frameworks are Tensorflow , Keras ,Pytorch etc.., Convolution plays a major role in detecting patterns, edges , shapes of a given image to give a score required for making predictions. As there is much more to discuss things required for end-to-end training of a model. This is just a high level introduction.After getting a trained model it is used for inference purpose running it on web browser or deploying it in an application based on our requirements.So that just by uploading a picture the end user can access its features.
Machine learning is not only used for classification. It can also be used for Regression (continuous data) like prediction of house prices based on its features like sq.yards, number of rooms, area etc..,Detecting and Recognising text present in the image, Numerical data Analysis and prediction, Speech Recognition in many applications in Business, Marketing, Ads.
