Get started with the core components of machine learning and learn to create supervised learning systems.

Zero to Hero in Python for Machine Learning

This module is a comprehensive introduction to the core components of machine learning for software engineers. We’ll be introduced to the most relevant components of the machine learning discipline. We’ll also cover the differences between traditional programming and machine learning through hands-on development in supervised learning. By the end of this module, we’ll have a working knowledge of modern techniques in the field of machine learning.

Fundamentals of Machine Learning

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(Select all that apply.) The linear regression might fail to deliver good results in which graphs.


(Select all that apply.) The linear regression might fail to deliver good results in which graphs.

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Which plot represents the **sigmoid function**?

Which plot represents the sigmoid function?

Calculate false positives (**FP**) and false negatives (**FN**) for a binary classifier that detects the digit $5$.

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Calculate false positives (FP) and false negatives (FN) for a binary classifier that detects the digit <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mn>5</mn></mrow><annotation encoding="application/x-tex">5</annotation></semantics></math>5.
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(Select all that apply.) Which of the attributes should a suitable wrapper have?

(Select all that apply.) Which of the attributes should a suitable wrapper have?

How does flattening the input image for the training phase affect accuracy?

How does flattening the input image for the training phase affect accuracy?

How Machine Learning Works

Our First Learning Program

Walking the Gradient

Hyperspace

A Discern Machine

Get Real

The Final Challenge

Conclusion

Quiz: A Discerning Machine and Getting Real