Machine Learning is a Very Broad Field. If Machine Learning is a dish, then linear algebra, programming, analytical skills, statistics, and Algorithms are the primary recipes of Machine Learning. If you will go more deep inside the Machine Learning concepts, you will get confused about what to learn first or what to not focus much. So here, In this article, I will take you through the most important Machine Learning Concepts, which you need to keep as must-know concepts in machine learning.

The Most Important Concepts of Machine Learning

All Machine Learning concepts, that I have shown below are not based on the order of their rank or weightage in Machine Learning. Just keep in mind that every concept is more important than the others. So while learning Machine Learning you just can’t miss these concepts:

Pipelines

A sequence of data processing components is called a Data Pipeline. Pipelines are very common in Machine Learning systems since there is a lot of data to manipulate and many data transformations to applying.

Components typically run asynchronously. Each component pulls in a large amount of data, processes it, and splits out the result in another data store. Then, sometime later, the next component in the pipeline pulls this data and splits out its output. Each component is fairly self-contained: the interface between components is simply the data store.

This makes a system to grasp, and different teams can focus on different components. Moreover, if a component breaks down, the downstream components can often continue to run normally by just using the last output from the broken component. This makes the architecture quite robust. You can learn to create pipeline and some more machine learning concepts of creating pipelines from here.

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Cross-Validation

One way to evaluate your machine learning model would be to use the train_test_split() function to split the training set into a smaller test set and a validation set, then train your models against the test set and evaluate them against the validation set. It’s a bit of work, but nothing too difficult, and it would work fairly well.

A great alternative is to use the cross-validation feature provided by Scikit-Learn. Cross-Validation works by splitting the training set into 10 distinct subsets called folds, then it trains and evaluates a Machine Learning model 10 times, picking a different fold for evaluation every time and training on the other 9 folds. I implement cross-validation in most of the tasks. You can learn to use cross-validation and some more machine learning concepts of it from here.

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