Studying through the Internet means swimming inside an infinite ocean of information.
How many times, trying to approach a new topic or subject, have 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 contributed lots of effort 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 and some Tools in-depth guides. They are optional but highly recommended. You will need them to expand day-by-day your skillset and expertise.
You will learn to understand and apply theory with hands-on projects.
By carefully following this guide, you will gain complete awareness and expendable skills from scratch.
You do not require any prior knowledge of machine learning, but be confident with programming and high school-level math 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 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 [email protected]
I think the second guide (Business Intelligence) will be out in 2 or 3 weeks. Yo!
- Latex
You can take them in order or choosing the one that most fits to you, 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 is about Data (you wouldn't have said that?) and is the core toolkit for everyone working with data. Working with data is an artform, and the rules of thumb and best practices will help you understanding the way to deal with them. You need to develop a "sense" of what to do with the data, and this "sense" is primarily driven by the situation and the experience. Because of that, these specializations will be strongly focused on exercises and practice.
The latter is about... everything that's not written in technical books. Use and master them, because they are the real value enabler for you. You can be the best developer or engineer in 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 :)
Everyone can committ their own guides, following the style I've chosen, and I'm proud to tell you that very soon the Tools Sections will host several guides about everything you need to know about a partiular technology/language/methodology! I've alreay planned with some contributors a guide on Latex and one about ElasticSearch! So stay tuned!
You can alredy find here a cool Latex guide for beginners!
This is the roadmap of the coming guides (the Machine Learning one is already out).
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent