Machine Learning (ML) is a deeply fascinating topic. Here developers can write programs that "magically" solve difficult problems.
Feed some data to the algorithm, and a result will appear. If you are not satisfied, then add more data or train the model. Repeat.

In this course you will learn how to use Machine Learning (ML) to solve problems of different kind. We will start with a simple problem like how to train a model to behave like a logical AND operator. From there we move on to predict height based on age and to predict people's political views based on socio-economic data.

This course will focus on understanding the fundamentals of ML. We will solve the same problem in 3 different ways:
First using Google Sheets, then a Deep Neural Network Simulation Tool and finally using Python.


For the mandatory task, students will define their own project. It can be based on any of the following:
Deep Neural Network (DNN)
Reinforcement learning
Azure Machine Learning Studio
TensorFlow.js
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)

The goal is for the student to get inspired and find their own problem to solve.

The student knows how a computer can learn.
The student knows several ML methods, such as Regression, Classification, Clustering and Deep Learning. And hence the concepts of Supervised Learning and Unsupervised Learning.
The student knows tools such as Numpy and Pandas.

The student can use ML to solve practical tasks.
The student can prepare a dataset from raw data. This includes Data Cleaning, Feature Selection, Data Transforms and Feature Engineering.
The student can make visual representation of data to select features. The student can train and test a model.
The student can use an ML-framework to solve a practical task.

The student knows how to select a relevant ML method to solve a given problem.
The student can assess the quality and efficiency of a solution.
The student can find and use relevant online resources and documentation to build a ML solution.