Studying through the Internet means swimming inside an infinite ocean of information.
How many times, trying to approach a new topic or subject, you felt baffled, disoriented and without a real "path" to follow, to ensure yourself a deep knowledge and the ability to apply it?
Hi, i'm Giacomo.
I'm an Italian student currently having a stage in a shiny Machine Learning and AI startup in Bologna. My boss asked me if it was possible to create a study path for me and newcomers, and I've put a lot of efforts to share my 3-4 years of walking around the internet and collecting sources, projects, awesome tools, tutorial, links, best practices in the ML field, and organizing them in an awesome and usable way.
This repository is intended to provide three complete and organic learning paths for the following fields:
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Machine Learning
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Business Intelligence (coming soon)
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Cloud Computing (coming soon)
Also I organize and collect for you some Specializations. They are optional but highly recommended, and you need them in order to expand day-by-day your skillset and your expertise.
You will both understand theory and be able to apply it in practice, with hands-on projects.
If carefully followed they will bring you to a complete awareness and expendable skill from scratch.
In fact, they do not require any previous knowledge, but being confident with programming and high school math is necessary to understand and implement most of the concepts.
Every source listed here is free or open source.
I tried to be concise to avoid information overhead.
I tried to organize the content hierarchically and by level of complexity, in order to give you a coherent idea of how things work.
Click on "watch", I'm updating this in the free time and weekends.
If you want to contact me for whatever reason, just e-mail me at giacomo.ciarlini@student.unife.it
I think the second guide (Business Intelligence) will be out in 2 or 3 weeks. Yo!
This is the roadmap of the coming guides (the Machine Learning one is already out).
You can take them in order or choosing the one that most fits to you and your inclinations, but I recommend you to walk through them all at least once.
I've planned two types of Specializations:
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Data Specializations
- Data Preprocessing [Already Out!]
- Data Collection [Coming Soon - Next]
- Data Visualization [Coming Soon]
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SoftSkills Specializations
- Effective Communication [Coming Soon]
- Impactful Presentations [Coming Soon]
- Pragmatic Decision Making [Coming Soon]
The former are about Data (you wouldn't have said that?) and they truly are the core toolkit of everyone working with data. To work with data is an art by some point of view, and rules of thumb and best practices will help you understanding the right way to treat them, but also you need to develop a "sense" of what to do with the data, and this "sense" is mostly driven by the situation and the experience. Because of that, these specializations will be strongly focused on exercises and practice.
The latter are about... everything that's not written in technical books. Take and master them, because they are the real value enabler for you. You can be the best developer or engineer of the world, but If you can't communicate your data to your audience, or use data to suggest practical action in the real world, you're useless for a company.
So, stay tuned because I'm building this section during weekends and free time, and I hope to provide you one specialization each week!
As usual, feel free to suggest improvements and collaborations :)