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

Python boasts a few large ML libraries such as [PyTorch](https://pytorch.org) and [TensorFlow](https://www.tensorflow.org). They are complex, and they tend to evolve fast. We will not use them in this course.

Instead, we will use a slimmer and more stable library named [Keras](https://keras.io). Keras is a thin layer that sits on top of the “big” libraries and hides them behind a nice, clean programming interface. Keras started out as a tool for prototyping, but it quickly became one of the most popular ways to build neural networks in Python — it was even adopted by TensorFlow as an official interface. With Keras, we can use the heavyweight libraries without getting bogged down by their complexities.

## A plan and a piece of code

Let’s build a neural network for the Echidna dataset. We can use the MNIST network that we designed back in [Designing the Network](https://www.educative.io/courses/fundamentals-of-machine-learning-for-software-engineers/assembling-a-neural-network-from-perceptrons) as a starting point.

Python boasts a few large ML libraries such as [PyTorch](https://pytorch.org) and [TensorFlow](https://www.tensorflow.org). They are complex, and they tend to evolve fast. We will not use them in this course.

Instead, we will use a slimmer and more stable library named [Keras](https://keras.io). Keras is a thin layer that sits on top of the “big” libraries and hides them behind a nice, clean programming interface. Keras started out as a tool for prototyping, but it quickly became one of the most popular ways to build neural networks in Python — it was even adopted by TensorFlow as an official interface. With Keras, we can use the heavyweight libraries without getting bogged down by their complexities.

# A plan and a piece of code

Let’s build a neural network for the Echidna dataset. We can use the MNIST network that we designed back in [Designing the Network](https://www.educative.io/courses/fundamentals-of-machine-learning-for-software-engineers/assembling-a-neural-network-from-perceptrons) as a starting point.

Learn how to create a neural network using Keras.

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

Build a Neural Network with Keras

A plan and a piece of code