Why learn the math behind machine learning algorithms when you can readily implement it using the python libraries like scikit-learn, h2o, statsmodels etc?

This is a fair question especially coming from beginners when it is easy to implement ML with few lines of code and get the results fast.

Now, you must understand that learning the underlying mathematics behind the ML rigorously and thoroughly is the same as understanding how the algorithms work internally.

So, why learn the math behind ML?

## 1. When you learn the internals of algorithms completely, you can apply them appropriately.

You will be able to better appreciate **where** should you use a specific algorithm and where you should not use it.

You should be able to compare and contrast why a given algo makes certain predictions the way it does, and what you can do to make amends to suit your needs.

You will also know **how** you should use it appropriately in various situations.

You should also know **why** should you use a specific algorithm and why not some other technique.

In a nutshell, you will be able to bring more value to the table and able able to call out inefficiencies in approaches and provide remedial solutions.

## 2. Debug a model

Sometimes the algorithms in packages might not work the way it was intended to.

When something does not work the way it should, you want to debug your models. When you know the internals of how the algorithms work, you will be able to catch why certain outputs / behaviours happen and be able to debug them quickly.

You should be in a position to change the library if need be to suit your needs.

## 3. Modify the algorithm to suit your needs

Sometimes you might need to modify parts of the algorithm slightly to suit your project needs.

You might consider changing the objective function, the loss function, change how it learns, samples the data etc.

You might want to introduce a few changes to make the algorithm to produce better results.

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#### Do you want learn how to approach projects across different domains with Time Series?

Get started with your first Time Series Industry Project and Learn how to use and implement algorithms like ARIMA, SARIMA, SARIMAX, Simple Exponential Smoothing and Holt-Winters.

## 4. Quickly learn new algorithms

Since machine learning / deep learning is a fast evolving field, new findings, algorithms, technique keep arriving.

By knowing how the existing algorithms work, you will be better placed to understand and catch up on the new developments as they occur.

Because most new developments are a result of small mathematical changes on top of existing algorithms / models.

## 5. Ace Data Science interviews

Interviewers for Data science / machine learning roles look for how deeply and clearly you know how the algorithms / methods work and your hold on the subject in general.

Knowing the math and concepts well almost guarantees you to make a good impression in your interview rounds and grab the high paying job.

The stronger you are in the subject will make a stronger positive impression in your interview. It gives you leverage to negotiate higher salaries.

**Aren’t these reasons enough to master the math behind machine learning?**

Yes.

But how to master the math behind ML, you ask?

The complete machine learning mastery path has the concepts, math, implementation and the business understanding covered. Try it out if you want to really get good with ML.