Academy

In the Artyficial Academy you can learn about artificial intelligence, machine learning, big data, data science and Python. Follow our Course: 7 steps to AI.

In the Artyficial Academy you can learn about artificial intelligence, machine learning, big data, data science and Python. We want to provide you the shortest way to get familiar with AI and related topics. Therefore we have made a short track, based on our experiences to learn the basics of AI.

After you have completed the basics you can further develope yourself in a direction you choose, either on this site or with one of the many resources that we provide on this site.

It is our mission to educate people in order to enhance the democratic and unbiased use of computer algorithms.

Our 7 steps approach to demystify AI and Big Data:

  1. Mindset
    It all starts with the right mindset. You don’t need a PhD or a background in programming. Everyone with the right mindset can master the basics of AI. Just give it a try and you’ll be amazed about the many possibilities.

  2. Tools
    Many professional tools are available. They are open source and can be used free of charge. We pick some tools that are easy to use and are in the top -10 of tools used by professionals every day.

  3. Coding
    Learn how computers think by mastering the basics of programming your own code. We use Python for this, as Python is a strong and compact programming language that is used in a very wide range of solutions, not limited to AI. And Python is easy to learn compared to other programming languages.

  4. Data Science
    Python can be extended with Numpy and Pandas. These are libraries for calculation and data science. We work on different cases. With different plotting libraries we make our results visible.

    We perform our own EDA: Exploratory Data Analysis to examine and prepare data for use in a Machine Learning Environment. Some well-known datasets are introduced.

  5. Big Data
    We start small and think big. After using the Pandas Dataframe to import and export CSV- and Excel-files, we dive deeper into SQL, NoSQL and Graph Databases. We use ETL to import and transform data from different sources. We look at structured, unstructured and streaming data and how to handle them.

  6. Machine Learning
    ML consists of four different approaches, each with their own algorithms and benefits.

  7. Deep Learning
    Computer Vision, Speech Recognition, Convolutional Neural Networks, and other related topics, also called Narrow AI, are explained by several cases.