Machine Learning Jargons

If you want to understand Machine Learning Jargons in very detailed way. Congrats, you have landed on the right post.

What is Artificial Intelligence [AI]

Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks.

AI is classified into 3 types,

Artificial narrow intelligence (ANI), Artificial general intelligence (AGI) and Artificial superintelligence(ASI)

Narrow AI? It is also called as Artificial narrow intelligence (ANI), also referred to as weak AI or narrow AI Narrow AI (ANI) is defined as “a specific type of artificial intelligence in which a technology outperforms humans in some very narrowly defined task. Unlike general artificial intelligence, narrow artificial intelligence focuses on a single subset of cognitive abilities and advances in that spectrum


  • Self-driving cars

Artificial general intelligence General AI is also called as Strong AI Artificial general intelligence (AGI), also referred to as strong AI or deep AI, is the concept of a machine with general intelligence that mimics human intelligence and/or behaviors, with the ability to learn and apply its intelligence to solve any problem. AGI can think, understand, and act in a way that is indistinguishable from that of a human in any given situation

Artificial superintelligence(ASI) Artificial super intelligence (ASI), is the hypothetical AI that doesn’t just mimic or understand human intelligence and behavior; ASI is where machines become self-aware and surpass the capacity of human intelligence and ability

Most AI examples that you hear about today — from chess-playing computers to self-driving cars — rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.

What is Machine-Learning (ML)? Machine learning, is the study of computer algorithms that improve atomically through experience. It is see as a subset of artificial intelligence

In laymen terms,

machine-learning is a process of predicting the results based on the incoming data

Machine learning problems are mainly classified into

  • Supervised

Supervised: All data is labeled and the algorithms learn to predict the output from the input data. Unsupervised: All data is unlabeled and the algorithms learn to inherent structure from the input data. Semi-supervised: Some data is labeled but most of it is unlabeled and a mixture of supervised and unsupervised techniques can be used.

Supervised Learning Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output. Y = f(X) The goal is to approximate the mapping function so well that when you have new input data (x) that you can predict the output variables (Y) for that data.

This is further classified into 2 types

Classification: A classification problem is when the output variable is a category, such as “red” or “blue” or “disease” and “no disease”.

Regression: A regression problem is when the output variable is a real value, such as “dollars” or “weight

Linear regression for regression problems. Random forest for classification and regression problems. Support vector machines for classification problems.

Unsupervised Machine Learning

Unsupervised learning is where you only have input data (X) and no corresponding output variables.

The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data.

These are called unsupervised learning because unlike supervised learning above there is no correct answers and there is no teacher. Algorithms are left to their own devises to discover and present the interesting structure in the data.

Unsupervised learning problems can be further grouped into clustering and association problems.

Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior. Association: 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. Some popular examples of unsupervised learning algorithms are:

k-means for clustering problems. Apriori algorithm for association rule learning problems.

If you want to try all these algo’s [some times we also called as ML model’s]. Just have a look into the below web-site

Data science is mainly analysis the data, looking at the data and doing something with that

AI -> Data science -> Machine Learning -> Deep-Learning All these are interlinked

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If you want to understand how these machine learning algorithms works, just go to below link and upload the diff images or diff audio files and train the model and check.

Machine learning activities :

Written by

software architect, algo trader, machine learning enthusiast

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