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

# Interesting history
In the 1950s, the nascent field of artificial intelligence was split into rival factions. Two groups competed for academic mindshare. The leading group was the “_symbolists_,” led by authorities such as Marvin Minsky and John McCarthy. The runners-up were the “_connectionists_,” led by  Frank Rosenblatt.

The two groups had very different approaches. The symbolists believed in programming an intelligent machine from the ground up. Piece by piece, they planned to build computers that would eventually manipulate concepts faster and better than humans.

This idea might sound overly optimistic today, but it made perfect sense in the $1950s,$ when people were inventing the first high-level programming languages. Those languages seemed much closer to human thinking than plain old assembly language. Who knew how far that process could go? (In fact, John McCarthy invented one of the first high-level programming languages, LISP, in his quest to code intelligence.)

The opposite faction, the connectionists, chased another dream. Their idea could be summed up as: _build a brain, and intelligence will come._

To simplify things, the brain is made of neurons connected through fibers. Each neuron has multiple input fibers and one output fiber. If the inputs are active in a certain pattern (maybe because they get a signal from the sensory organs), then the output is also activated. The connectionist leader Frank Rosenblatt built a machine inspired by that mechanism. BHe named the machine “perceptron” based on neurons and its final processing step “activation function.”

The first perceptron was very different from our tiny Python program. The Mark 1 perceptron was a room-sized piece of hardware that looked a bit like a server rack covered by an impenetrable tangle of wires. It had a camera connected to 400 photocells with  very low-resolution pixels. The weights were implemented with potentiometers wired to the photocells. During the learning phase, the potentiometers were physically rotated by electric motors.

To overcome the perceptron’s limitations, the connectionists also studied multilayer perceptrons, which was able to tackle non-linearly separable data. Meanwhile, the symbolists were busy writing programs that solved algebra problems and stacked construction blocks with a robot arm.

To be fair, neither faction was making much progress toward intelligent machines. On the other hand, both factions were inclined to publicity and extravagant promises. At one point, Rosenblatt declared that the perceptron was the first step toward machines that would not only be smart, but even self-conscious. The popular press brought into it the public eye , which made symbolists jealous.


The feud went on for years, with the symbolists reaping the lion’s share of research funds, and the connectionists playing the part of the popular underdogs. Then, at some point, things really hit the fan.

# The final battle

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

A Tale of Perceptrons

Interesting history