Explore machine learning's essentials for software engineers, delve into supervised learning, neural networks, and deep learning, and gain skills to tackle real-world data challenges effectively.

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Machine learning is the future for the next generation of software professionals.

This course serves as a guide to machine learning for software engineers. You’ll be introduced to three of the most relevant components of the AI/ML discipline; supervised learning, neural networks, and deep learning. You’ll grasp the differences between traditional programming and machine learning by hands-on development in supervised learning before building out complex distributed applications with neural networks. You’ll go even further by layering networks to create deep learning systems. You’ll work with complex real-world datasets to explore machine behavior from scratch at each phase.

By the end of this course, you’ll have a working knowledge of modern machine learning techniques. Using software engineering, you’ll be prepared to build complex neural networks and wrangle real-world data challenges.

Fundamentals of Machine Learning for Software Engineers

In our linear regression model,
$\hat{y}=x*w$, 
what does the symbol y^  correspond to?

In our linear regression model,
<math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mover accent="true"><mi>y</mi><mo>^</mo></mover><mo>=</mo><mi>x</mi><mo>∗</mo><mi>w</mi></mrow><annotation encoding="application/x-tex">\hat{y}=x*w</annotation></semantics></math>y^​=x∗w,
what does the symbol y^ correspond to?

**(Fill in the blank.)** The idea of approximating the examples with a line is called ________.

(Fill in the blank.) The idea of approximating the examples with a line is called ________.

Increasing the learning rate would increase the number of iterations and vice versa.

Decreasing the learning rate would increase the number of iterations and vice versa.

The number of iterations required for the training phase is independent of the learning rate.

How does the learning rate affect the number of iterations for training a specific classifier/model?

How does the learning rate affect the number of iterations for training a specific classifier/model?

In the generalized machine learning model, $\hat{y}=x*w+b$, what does the term $b$ represent?

In the generalized machine learning model, <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mover accent="true"><mi>y</mi><mo>^</mo></mover><mo>=</mo><mi>x</mi><mo>∗</mo><mi>w</mi><mo>+</mo><mi>b</mi></mrow><annotation encoding="application/x-tex">\hat{y}=x*w+b</annotation></semantics></math>y^​=x∗w+b, what does the term <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>b</mi></mrow><annotation encoding="application/x-tex">b</annotation></semantics></math>b represent?

![](/api/collection/10370001/5351678229872640/page/5671698229100544/image/6045338636386304?page_type=collection_lesson)


In the above graph, what is the total loss as per `loss()` definition, provided that the green line represents the predictive model? 

<img src="/api/collection/10370001/5351678229872640/page/5671698229100544/image/6045338636386304?page_type=collection_lesson" alt="" />
In the above graph, what is the total loss as per <code>loss()</code> definition, provided that the green line represents the predictive model?

How Machine Learning Works

Our First Learning Program

Walking the Gradient

Hyperspace

A Discern Machine

Get Real

The Final Challenge

The Perceptron

Designing the Network

Building the Network

Training the Network

How Classifiers Work

Batchin’ Up

The Zen of Testing

Let’s Do Development

A Deeper Kind of Network

Defeating Overfitting

Taming Deep Networks

Beyond Vanilla Networks

Into the Deep

Quiz: Basics of Machine Learning