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

It helps to overcome getting stuck at the local minima problem.

It helps to converge faster than batch GD in training.

It helps to make the processing of the training set faster.

(Select all that apply.) What are the benefits of using **mini-batch gradient descent**?


(Select all that apply.) What are the benefits of using mini-batch gradient descent?

To test our system on data that it has not seen before.

To train our system on data that it has not seen before.

To validate our system on data that it has not seen before.

What is the purpose of the **blind test rule**?




What is the purpose of the blind test rule?

Most of the data is concentrated at one point, not necessarily the mean.

Most of the data is uniformly distributed.

What does it mean to have less standard deviation in the data?


What does it mean to have less standard deviation in the data?

It would keep on increasing the accuracy.

It would increase the accuracy and then becomes stable.

It would increase the accuracy, and then after some time, the accuracy would start to decline.

What would be the effect if we keep increasing the number of epochs?

What would be the effect if we keep increasing the number of epochs?

To use an average of 0 and standard deviation of 1.

To use the number of hidden nodes an average between the number of input and output nodes.

To use an average learning rate, a compromise between high accuracy and faster convergence.

What is the **average rule** to find an optimum boundary for a nonlinearly separable data?

What is the average rule to find an optimum boundary for a nonlinearly separable data?

Let’s evaluate your learning with respect to developing the network.

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: Develop the Network