Machine Learning

Machine Learning

Machine learning(ML) is a field of Artificial Intelligence(AI), that is concerned with teaching machine to learn to perform a specific task.

Understand common terms in ML.

Well hello there, today I am trying define common terms in machine learning. I would not dive into mathematics behind them.

What is Machine Learning?

Machine learning(ML) is a field of Artificial Intelligence(AI), that is concerned with teaching machine to learn to perform *a *specific task.

How do Machine’s Learn?

Keep in mind that Machine means an mathematical algorithm or a computer system not terminator.

Have u ever heard a term “History repeats itself!”. Well its true, there is a pattern on how things happen around us. With a accurate problem definition and relevant data we can use machine learning algorithms to find patterns in data. The algorithms predict an output based on the past events. Now, u might be wondering if we can predict with 100% accuracy, I would say no, but its quite close, after all we are still predicting.

Now lets define some keywords that are quite common in Data Science / Machine learning tongues.

*Data: *Data is just an information in its raw, unrefined form. Machine Learning team first spends significant amount of time collecting the right data. The amount of data required is not always known from the beginning, but more the merrier. Now what data u want to collect, strictly depends on the problem u r trying to solve.

*Data-frame: *Its a structure of data in tabular form with rows, columns, headers(sometimes absent).

Features/ Variables: Features and Variables are used interchangeably in ML. Basically, a feature is a column. It represents a specific property of a data. For instance, age, gender, name, address are all separate feature columns.

Observations: They are rows in a data-frame. They represent a unique sample. For instance, Lets say information of a person named Max who is 29 years old, male and lives in Amsterdam. This can be taken as one observation or a row in data-frame.

bias-variance-tradeoff data outliers machine-learning

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