Purnananda Behera

What is Machine Learning?

Machine learning lets us find patterns in existing data, then create and use a model that recognizes those patterns in new data.

A machine learning algorithm is like a trial and error process, but the special thing about it is that each consecutive trial is at least as good as the previous one.

Types of Machine Learning:

Supervised Learning:

– Supervised learning is a learning in which we teach or train the machine using data which is well labeled that means some data is already tagged with the correct answer. After that, the machine is provided with a new set of examples(data) so that supervised learning algorithm analyses the training data(set of training examples) and produces a correct outcome from labeled data.
– The value you want to predict is in the training data. Supervised learning rests on using labeled data.

Supervised Learning Types:
a. Classification:
– Classification is technique to categorize our data into a desired and distinct number of classes where we can assign label to each class.
– A classification problem is when the output variable is a category, such as “red” or “blue” or “disease” and “no disease”, “yes” or “no”.
b. Regression:
– Regression models are used to predict a continuous value.
– Predicting prices of a house given the features of house like size, price etc is one of the common examples of Regression.
– A regression problem is when the output variable is a real value, such as “rupees” or “weight”.

Unsupervised Learning:

– In Unsupervised learning, an AI system is presented with unlabeled, uncategorized data and the system’s algorithms act on the data without prior training.
– In essence, unsupervised learning can be thought of as learning without a teacher.
– Unsupervised learning is where you only have input data (X) and no corresponding output variables.

Unsupervised Learning Types:
a. Clustering:
– It deals with finding a structure or pattern in a collection of categorized data.
– A simple definition of a cluster could be “the process of grouping the object into classes such that each member of a class is similar to the other in one or the other way.”
– A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior.

b. Association:
– An association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases.
– An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y.

Semi Supervised Learning:

– Some algorithms can deal with partially labeled training data, usually a lot of unlabeled data and a little bit of labeled data.
– Semi-supervised learning algorithms are trained on a combination of labeled and unlabeled data.
– Use case example: Web page classification, speech recognition, etc.

Reinforcement Learning:

– Reinforcement learning is the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment.
– In reinforcement learning there’s no answer key, but your reinforcement learning agent still has to decide how to act to perform its task. In the absence of existing training data, the agent learns from experience.

Batch Learning:

– In batch learning, the system is incapable of learning incrementally: it must be trained using all the available data.
– Learning over groups of patterns.

Online Learning:

– In online learning, you train the system incrementally by feeding it data instances sequentially, either individually or by small groups called mini-batches. Each learning step is fast and cheap, so the system can learn about new data on the fly, as it arrives. Learning based on each pattern as it is observed.