Roadmap to a Solid Foundation in Artificial Intelligence
This is a fork and adaptation of https://github.com/AMAI-GmbH/AI-Expert-Roadmap
I've ported over the flowcharts to use the Mermaid library, that is intragrated into GitHub
If you are interested collaborating, drop a note to [email protected].
👉 An interactive version with links to follow about each bullet of the list can be found at (https://jasonmhead.com/ai-expert-roadmap-with-connections/) 👈
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flowchart TD
en>AI Developer Concepts] -->
A[Foundational Concepts] --> B{Choose Your Path}
B --> C[Data Scientist]
B --> D[Data Engineer]
C --> E[Machine Learning]
D --> F[Big Data Engineer]
E --> G[Deep Learning]
mindmap
root((Basics))
Matrices & Linear Algebra Fundamentals
Database Basics
Relational
SQL
Inner
Left
Right
Full Outer
NoSQL
Vector
Graph
Tabular Data
Data Frames & Series
Extract, Transform, Load ETL
Data Use
Reporting
BI
Analytics
Data Formats
JSON
XML
CSV
Regular Expressions RegEx
mindmap
root((Python))
Basics
Expressions
Variables
Data Structures
Functions
Install packages via pip. conda etc.
Codestyle. e.g. PEP8
Important libraries
Numpy
Pandas
Virtual Environments
Jupyter Notebooks / Lab
mindmap
root((Data Sources))
Data Mining
Web Scraping
Great Public Datasets
Kaggle
Huggingface
mindmap
root((Working with Data))
Data Mining
Web Scraping
Great Public Datasets
Kaggle
Huggingface
mindmap
root((Working with Data))
Principal Component Analysis PCA
Dimensionality & Numerosity reduction
Normalization
Data Scrubbing, Handling Missing Values
Unbiased Estimators
Binning Sparse Values
Feature Extraction
Denoising
Sampling
mindmap
root((Data Scientist))
Statistics
Probabilty Theory
Randomness, random variable and random sample
Probability distribution
Conditional probability and Beyes' theorem
Statistical independence
Independent and Identically Distributed
Functions
Cumulative distribution
Probability density
Probability mass
Continous distributions
Normal / Gaussian
Uniform
Beta
Dirichlet
Exponential
x2 chi squared
Discrete distributions
Uniform
Binomial
Multinomial
Hypergeometric
Poisson
Geometric
Summary Statistics
Expectation and mean
Variance and standard deviation
Covariance and correlation
Median, quartile
Interquartie range
Percentile / quartile
Mode
Estimation
Hypothesis Testing
Confidence Interval CI
Monte Carlo Method
If you think any of the roadmaps can be improved, please do open a PR with any updates and submit any issues.