Machine Learning – Definition

The standard definition as per wiki is

“Machine learning is the science of getting computers to act without being explicitly programmed.”

As per data analytics we can say that machine learning is the method of

“Data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that machines should be able to learn and adapt through experience”

Evaluation of machine learning

Machine learning is a part of the gigantic field of Artificial Intelligence (AI). It has been just a few decades since AI was introduced. With machine learning, like the name implies, the machine learns. Instead of the conventional systems with rigid codes, this machine can continuously learn and adapt itself. Internet is everywhere and there is so much data to handle. The processing and handling of massive data require adaptive learning algorithms to make them more efficient and quick.

Interesting infographics produced by PwC. To view the original article, download the infographics in PDF format, and read the comments, click here.

Types of machine learning algorithms

There some variations of how to define the types of Machine Learning Algorithms but commonly they can be divided into categories according to their purpose and the main categories are the following:

  1. Supervised Learning
  2. Unsupervised Learning
  3. Reinforcement Learning

Supervised Learning

Supervised learning algorithms consist of a target / outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, we generate a function that map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data.

List of Common Supervised Learning Algorithms
  1. Naive Bayes
  2. Nearest Neighbor
  3. Decision Trees
  4. Linear Regression
  5. Support Vector Machines (SVM)
  6. Neural Networks

Unsupervised learning

The computer is trained with unlabeled data.

Here there’s no teacher at all, actually the computer might be able to teach you new things after it learns patterns in data, these algorithms a particularly useful in cases where the human expert doesn’t know what to look for in the data.

Unsupervised learning algorithms are the family of machine learning algorithms which are mainly used in pattern detection and descriptive modeling. However, there are no output categories or labels here based on which the algorithm can try to model relationships. These algorithms try to use techniques on the input data to mine for rules, detect patterns, and summarize and group the data points which help in deriving meaningful insights and describe the data better to the users.

List of Common Algorithms
  1. k-means clustering
  2. Association Rules

Reinforcement Learning

Reinforcement Learning is a type of Machine Learning, and thereby also a branch of Artificial Intelligence. It allows machines and software agents to automatically determine the ideal behavior within a specific context, in order to maximize its performance. Simple reward feedback is required for the agent to learn its behavior; this is known as the reinforcement signal.


List of Common Algorithms
  1. Q-Learning
  2. Temporal Difference (TD)
  3. Deep Adversarial Networks

Refer ML for more details.

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