There are various ML algorithms, techniques and methods that can be used to build models for solving real-life problems by using data. In this chapter, we are going to discuss such different kinds of methods.
The following are various ML methods based on some broad categories −
The following diagram shows what type of task is appropriate for various ML problems −
In the learning process, the following are some methods that are based on learning ability −
In many cases, we have end-to-end Machine Learning systems in which we need to train the model in one go by using whole available training data. Such kind of learning method or algorithm is called Batch or Offline learning. It is called Batch or Offline learning because it is a one-time procedure and the model will be trained with data in one single batch. The following are the main steps of Batch learning methods −
Step 1 − First, we need to collect all the training data for start training the model.
Step 2 − Now, start the training of model by providing whole training data in one go.
Step 3 − Next, stop learning/training process once you got satisfactory results/performance.
Step 4 − Finally, deploy this trained model into production. Here, it will predict the output for new data sample.
It is completely opposite to the batch or offline learning methods. In these learning methods, the training data is supplied in multiple incremental batches, called mini-batches, to the algorithm. Followings are the main steps of Online learning methods −
Step 1 − First, we need to collect all the training data for starting training of the model.
Step 2 − Now, start the training of model by providing a mini-batch of training data to the algorithm.
Step 3 − Next, we need to provide the mini-batches of training data in multiple increments to the algorithm.
Step 4 − As it will not stop like batch learning hence after providing whole training data in mini-batches, provide new data samples also to it.
Step 5 − Finally, it will keep learning over a period of time based on the new data samples.
In the learning process, followings are some methods that are based on generalization approaches −
Instance based learning method is one of the useful methods that build the ML models by doing generalization based on the input data. It is opposite to the previously studied learning methods in the way that this kind of learning involves ML systems as well as methods that uses the raw data points themselves to draw the outcomes for newer data samples without building an explicit model on training data.
In simple words, instance-based learning basically starts working by looking at the input data points and then using a similarity metric, it will generalize and predict the new data points.
In Model based learning methods, an iterative process takes place on the ML models that are built based on various model parameters, called hyperparameters and in which input data is used to extract the features. In this learning, hyperparameters are optimized based on various model validation techniques. That is why we can say that Model based learning methods uses more traditional ML approach towards generalization.