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i.am.ai
AI Expert Roadmap
Roadmap to becoming an Artificial Intelligence Expert in 2022
tweet
tweet
post
post
Roadmap
Roadmap
2022
2022
Author
Author
AMAI GmbH
AMAI GmbH
License
License
MIT
MIT
Below you find a set of charts demonstrating the paths that you can take and the technologies
that you would want to adopt in order to become a data scientist, machine learning or an AI
expert. We made these charts for our new employees to make them AI Experts but we wanted to
share them here to help the community.
If you are interested to become an AI EXPERT at AMAI in Germany, or you want to hire an AI
Expert, please say hi@am.ai.
Note
👉
An interactive version with links to follow about each bullet of the list can be found at
i.am.ai/roadmap
👈
To receive updates star
⭐
and watch
👀
the GitHub Repo to get notified, when we add new
content to stay on the top of the most recent research.
Follow our AI Newsletter to stay up to date with the latest developments in AI. We cover new use
cases and research topics.
Disclaimer
The purpose of these roadmaps is to give you an idea about the landscape and to guide you if you
are confused about what to learn next and not to encourage you to pick what is hip and trendy.
You should grow some understanding of why one tool would be better suited for some cases
than the other and remember hip and trendy never means best suited for the job.
Introduction
GIT - Version Control
Papers With Code Personal Recommendation!
Available Options
Data Scientist
Big Data Engineer Machine Learning
Deep Learning
Data Engineer
Required for any path
AI Expert in 2022
Choose your path
Legend
Semantic Versioning
Keep a Changelog
Fundamentals
Fundamentals
Fundamentals
Matrices & Linear Algebra
Fundamentals
Database Basics
Relational vs. non-relational databases
SQL + Joins (Inner, Outer, Cross, Theta Join)
NoSQL
Tabular Data
Data Frames & Series
Extract, Transform, Load (ETL)
Reporting vs BI vs Analytics
Data Formats
JSON
XML
Regular Expressions (RegEx)
Expressions
Basics
P th P i
CSV
Some boxes link to additional resources
Interactive version on
i.am.ai/roadmap
Python Basics
Important libraries
Virtual Environments
Variables
Data Structures
Functions
Install packages (via pip, conda or similar)
Codestyle, e.g. PEP8
Numpy
Pandas
Python Programming
Exploratory Data Analysis /
Data Munging / - Wrangling
Dimensionality & Numerosity
Reduction
Normalization
Data Scrubbing,
Handling Missing Values
Unbiased Estimators
Binning sparse values
Feature Extraction
Denoising
Sampling
Principal Component Analysis
(PCA)
Awesome Public Datasets
Kaggle
Jupyter Notebooks / Lab
Data Sources
Data Scientist Data Engineer
Data Mining
Web Scraping
Data Science Roadmap
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