In this article, I want to outline what mistakes you should avoid when learning math in machine learning: Did not know what math topic is necessary for machine learning, Did not ask for help, Jumping to learning Machine Learning Math without understanding the Machine Learning algorithm concept, Stuck in the “School-Days” Way of learning

As a Data Scientist, machine learning is our main tool to solve business problems and one reason we are employed within a company. However, would it enough to only use machine learning without any math knowledge behind machine learning algorithms? In my opinion, you need to learn the math behind machine learning.

And here is some arguments to support my claims:

- Math helps you select the correct machine learning algorithm. Understanding math gives you insight into how the model works, including choosing the
**right model parameter and the validation strategies**. - Estimating how confident we are with the model result by producing the
**right confidence interval and uncertainty measurements**needs an understanding of math. - The right model would consider many aspects such as
**metrics, training time, model complexity, number of parameters, and number of features**which need math to understand all of these aspects. - You could
**develop a customized model**that fits your own problem by knowing the machine learning model's math.

Even though we understand how important math is, the main problem when learning math is how high the learning curve. In my experience, many people give up learning math because they fell into pitfall learning that hinders their development.

In this article, I want to outline what mistakes you should avoid when learning math in machine learning. Let’s get into it.

- Did not know what math topic is necessary for machine learning
- Did not ask for help
- Jumping to learning Machine Learning Math without understanding the Machine Learning algorithm concept
- Stuck in the “School-Days” Way of learning

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