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

## Introduction to supervised learning
Among the various approaches to ML, supervised learning is the one that reaped the most impressive results so far. Here is how supervised learning solves a problem like diagnosing pneumonia.

To do supervised learning, we’ll start from a set of examples, each carrying a label that the computer can learn from. For instance:

| What are we building?  |  Example |  Label |
| - | - | - |
|  A system that identifies a dog’s breed from its barking |  A `.wav` recording of a dog  | The dog’s breed, like “greyhound” or “beagle”  |
|A system that detects pneumonia|  An X-ray scan | A boolean flag: 1 if the scan shows pneumonia, 0 if it doesn’t  |
|A system that predicts the earnings of a lemonade stand from the weather|  The recorded temperatures on a day in the past |The recorded earnings on the same day|
| A system that recognizes the sentiment in a politician’s tweet  |  The tweet |  Emotional states such as “indignant,” “angry,” or “absolutely furious” |

As we can see, examples can be a lot of different things: data, text, sound, video, and so on. Also, labels can be either numerical or categorical: 

* **Numerical labels:** These are just a number, as in the case of the temperature-to-lemonade converter. 
* **Categorical labels:** These represent a category in a pre-defined set, as in the case of the dog breed detector. 

With some imagination, we can come up with many other examples of predicting something, be it numerical or categorical, from something else.

So, let’s assume that we have already put together a collection of labeled examples. Now we can dive into the two phases of supervised learning:


### Phase 1: Training
During this phase, we feed the labeled examples to an algorithm that’s designed to spot patterns. For example, the algorithm might notice that all pneumonia scans have certain common characteristics, (maybe certain opaque areas), that are missing from non-pneumonia scans. This is called the **training phase**, because the algorithm is looking at the examples repeatedly and learning to recognize those patterns.

### Phase 2: Prediction
Now that the algorithm knows what pneumonia looks like, we switch to the prediction phase, where we reap the benefits of our work. We show an unlabelled X-ray scan to the trained algorithm, and the algorithm tells us whether it contains signs of pneumonia or not.

Another example of supervised learning is a system that recognizes animals. Each input is the picture of an animal, and each label is the species. During the training phase, we show labeled images to the algorithm. During the prediction phase, we show it an unlabelled image, and the algorithm guesses the label:

# Introduction to supervised learning
Among the various approaches to ML, supervised learning is the one that reaped the most impressive results so far. Here is how supervised learning solves a problem like diagnosing pneumonia.

To do supervised learning, we’ll start from a set of examples, each carrying a label that the computer can learn from. For instance:

| What are we building?  |  Example |  Label |
| - | - | - |
|  A system that identifies a dog’s breed from its barking |  A `.wav` recording of a dog  | The dog’s breed, like “greyhound” or “beagle”  |
|A system that detects pneumonia|  An X-ray scan | A boolean flag: 1 if the scan shows pneumonia, 0 if it doesn’t  |
|A system that predicts the earnings of a lemonade stand from the weather|  The recorded temperatures on a day in the past |The recorded earnings on the same day|
| A system that recognizes the sentiment in a politician’s tweet  |  The tweet |  Emotional states such as “indignant,” “angry,” or “absolutely furious” |

As we can see, examples can be a lot of different things: data, text, sound, video, and so on. Also, labels can be either numerical or categorical: 

* **Numerical labels:** These are just a number, as in the case of the temperature-to-lemonade converter. 
* **Categorical labels:** These represent a category in a pre-defined set, as in the case of the dog breed detector. 

With some imagination, we can come up with many other examples of predicting something, be it numerical or categorical, from something else.

So, let’s assume that we have already put together a collection of labeled examples. Now we can dive into the two phases of supervised learning:


## Phase 1: Training
During this phase, we feed the labeled examples to an algorithm that’s designed to spot patterns. For example, the algorithm might notice that all pneumonia scans have certain common characteristics, (maybe certain opaque areas), that are missing from non-pneumonia scans. This is called the **training phase**, because the algorithm is looking at the examples repeatedly and learning to recognize those patterns.

## Phase 2: Prediction
Now that the algorithm knows what pneumonia looks like, we switch to the prediction phase, where we reap the benefits of our work. We show an unlabelled X-ray scan to the trained algorithm, and the algorithm tells us whether it contains signs of pneumonia or not.

Another example of supervised learning is a system that recognizes animals. Each input is the picture of an animal, and each label is the species. During the training phase, we show labeled images to the algorithm. During the prediction phase, we show it an unlabelled image, and the algorithm guesses the label:

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

Diabetes Prediction Using Keras

Defeating Overfitting

Taming Deep Networks

Beyond Vanilla Networks

Into the Deep

Recognize Handwritten Digits Using a Deep Neural Network

Supervised Learning

Introduction to supervised learning

What are we building?	Example	Label
A system that identifies a dog’s breed from its barking	A `.wav` recording of a dog	The dog’s breed, like “greyhound” or “beagle”
A system that detects pneumonia	An X-ray scan	A boolean flag: 1 if the scan shows pneumonia, 0 if it doesn’t
A system that predicts the earnings of a lemonade stand from the weather	The recorded temperatures on a day in the past	The recorded earnings on