Online Learning

Online Learning is a kind of Machine Learning in which real time data or streaming data is used. It can be used to predict future data at each step of the process. Online Learning can also be used for Deep Learning. Online learning algorithms are used to learn from continuous stream of data.

Difference between Online and Batch Learning

Online learning is considerably different from batch learning as batch learning is offline and takes place in batches. Models are trained in batches at regular intervals. In batch learning, training happens with the help of training data set. These types of models are static in nature.

• It can also be known as large scale learning. This type of machine learning is used in large scale learning problems.
• Online Learning is faster than batch learning as there is no need to re-train the new incoming data.
• It is fast and easy in which model learns as soon as the data arrives. One of the best examples of such data is stock market data. It is cheap so it is a great option when there is less number of resources available. If the amount of data exceeds the given volume of data,

Learning Rate

Learning rate can be defined as the rate at which the model adapts to the new dataset. A high learning rate is preferred for the models of this machine learning.

Data is sequentially received and incrementally updates data points one by one. This will be used to update the predictor of future data to generate the best predictor on the entire training data.

 It is used in situations only where there is a need to dynamically adapt new patterns in data or/and there is an involvement of time in prediction. Online machine learning can be used to solve problems such as supervised, unsupervised and bandit learning.

Disadvantages

• If incorrect/bad data is fed to its model, the performance will deteriorate instantly.
• Performance monitoring is very crucial
• Data governance
• High complexity as parameters change frequently.
• Difficult to maintain

It may be a combination of various machine learning techniques to predict better. It uses Models that are constantly updated by a real-time stream of data.  As the models can be updated instantly, online learning is far more efficient than offline learning methods.

Scikit-learn library can be used for implementing with Python programming.

It is great for large scale learning in real time data even with high velocity and its analytics. Also, tools such as MOA, Scikit Multiflow can also be used to implement adaptive/online learning.

It can adapt to the changing preferences. Consider the example of Online user preferences. Since the online learning is a continuous process, the computation power required is also more than that of batch learning.

Its models are more complex and difficult to manage. As involvement of live data in machine learning algorithms grows, the use of online learning is becoming more common and the use of traditional methods is being restricted. Some applications include stock market, economic data, recommender systems among others.

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