In the Artyficial Academy you can learn about artificial intelligence, machine learning, big data, data science and Python. We want to show 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 develop 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.
Practical Data Science Courses
In our courses we have a little background theory followed by a lot of hands-on exercises. This way you learn by doing a lot of the work in a way the professionals do.
There is a big difference between inventing models on an academical level or making a pilot project on one hand and putting your model in production and deploy it on a cloud environment on the other.
To learn the basics of artificial intelligence, it is important the learn the basics of data science. We have put this in our track ‘7 steps 2 AI’.
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.
7 steps 2 AI
Our 7 steps approach to demystify AI and Big Data:
1. Data is key!
In artificial intelligence the key ingredient is the data. Data is the oil in the machine when it comes to Machine Learning.
2. Python for Data Science
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.
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.