------------------Feature Engineering----------------------

The success of all Machine Learning algorithms depends on how you present the data. Every model gets input data and gives us an output. When your goal is to get the best possible output from input,

1/ You need to present the best data to the model. This is a problem that Feature Engineering solves. Feature Engineering refers to the process of using the domain of Knowledge to extract features from raw data.

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Let's assume you have three Features(age, height, salary) in your example.
The first feature varies from 1 to 90. The second one varies from 120 to 210 and the Third one varies from 1000 Euro to 4500 Euro.
#Thread
1/ As you can see the value of your features are in a different range. In this case, if you want to use gradient descent to find optimum parameters for your model( for instance linear regression), that leads to a slow speed of your model to converge. In this case,
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As I started to learn Data Science I didn't know what skills should I learn and where. That was a ton of content and I didn't know which one should I take. I have read more than a hundred articles and talk with some of my data scientist friend and gathered experience
1 during my journey. I want to share a roadmap and skills that you need as a junior Data Scientist and resources to learn.

1- Start with a language programming and best of all Python. You can learn Python from 3 resources.
Taking one of these courses is enough.

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