Getting your hands dirty
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"For the things we have to learn before we can do them, we learn by doing them." Aristotle |
You can find a lot of teaching and explainations around Data Science, ML and AI on the web - but what you really need is practical experience. Really coming in contact with real-life data and challenges will be the decisive step in you learning journey. So check out these ressources and get started.
Google Colab
Based on Google Cloud Platform infrastructure, Google provides aspiring data scientist with a wonderful environment for doing Data Science. In notebooks forked from project Jupyter you can right-away start coding - without any need to have big computational ressources on your own and install all the software with complex dependencies.
Go to colab.research.google.com →
Kaggle
Kaggle is an online platform for hosting data science competitions. Many companies provide challenging machine learning problems along with data to have them solved by the community of Data Scientists. It is a fantastic opportunity to apply what you've learned.
Kaggle uses their own flavor of Jupyter Notebooks to provide an easy-to-use environement to work on the challenges. As these notebooks can be shared, you can have a glimpse what other Data Scientists are doing and give your own experiments a warm start.
Kaggle also provides a variety of "Beginners" datasets that can be used to learn the basics.