The course will help you understand machine learning fundamentals by diving into theoretical concepts and algorithms of machine learning.

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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

After a mathematical detour, we can return to work at hand. We want to upgrade our learning program to deal with multiple input variables. Let’s make a plan of action so that we do not get lost in the process:

1. First, we’ll load and prepare the multidimensional data, to feed them to the learning algorithm.
2. After preparing the data, we’ll upgrade all the functions in our code to use the new model. We’ll switch from a line to a more generic weighted sum, as mentioned in [Adding More Dimensions](https://www.educative.io/courses/fundamentals-of-machine-learning-for-software-engineers/add-more-dimensions).

## Prepare data
ML is all about building amazing AIs. The reality is that a large part of the job is preparing data for the learning algorithm. To do that, let’s start from the file that contains our dataset:

After a mathematical detour, we can return to work at hand. We want to upgrade our learning program to deal with multiple input variables. Let’s make a plan of action so that we do not get lost in the process:

1. First, we’ll load and prepare the multidimensional data, to feed them to the learning algorithm.
2. After preparing the data, we’ll upgrade all the functions in our code to use the new model. We’ll switch from a line to a more generic weighted sum, as mentioned in [Adding More Dimensions](https://www.educative.io/courses/fundamentals-of-machine-learning-for-software-engineers/add-more-dimensions).

# Prepare data
ML is all about building amazing AIs. The reality is that a large part of the job is preparing data for the learning algorithm. To do that, let’s start from the file that contains our dataset:

Prepare data by adding more dimensions and upgrading the algorithm according to the updated dataset.

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

Upgrade the Learner

Prepare data