Machine Learning Concepts Every Data Scientist Should Know

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.

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.

Grid Search

One option would be to fiddle with the hyperparameters manually until you find a great combination of hyperparameter values. This would be very tedious work, and you may have time to explore many combinations.

Instead, you should get Scikit-Learn’s GridSearchCV to search for you. All you need to do is tell it which hyperparameters you want it to experiment which and what values to try out, and it will use cross-validation to evaluate all the possible combinations of hyperparameter values. You can learn to use the Grid Search Algorithm and some more machine learning concepts of it from here.

Creating your Own Algorithms

Machine Learning Concepts
Creating your own Algorithms

If you are using Scikit-Learn, you can easily use a lot of algorithms that are already made by some famous Researchers, Data Scientists, and other Machine Learning experts. Have you ever thought of building your algorithm instead of using a module like Scikit-Learn?

All the Machine Learning Algorithms that Scikit-Learn provides are easy to use but to be a Machine Learning Expert in a brand like Google and Microsoft, you need to build your algorithms instead of using any package so that you could easily create an algorithm according to your needs. You can learn to create your own algorithms and some fore machine learning concepts about building your own algorithm from here.

Training and Deploying a Machine Learning Model in a Web Application

I have trained and developed a lot of Machine Learning models, if you are a student in Machine Learning, you must have also developed models. When you train a machine learning model, also think about how you will deploy a machine learning model to serve your trained model to the available users. You will get a lot of websites who are teaching to train a machine learning model but nobody goes beyond to deploy a machine learning model. Because training and deploying a machine learning model are very different from each other. But it’s not difficult.

Training a model is the most important part of machine learning. But deploying a model is a different art because you have to think a lot in the process of how you will make your machine learning application to your users. You can learn to Deploy a machine learning model and some more machine learning concepts of deploying a model from here.

Also, Read: Machine Learning Projects for Beginners.

I hope you liked this article on the Machine Learning concepts that every Data Scientist should know. Feel free to ask your valuable questions in the comments section below.

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