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Optimal Tooling for Machine
Learning and AI
Boyan Angelov
Data Scientist, MindMatch
Who is this presentation for?
Main topics
Languages /
Software
Packages
IDEs
Visualisation /
EDA tools
Which languages should I use?
More is better!
And if you are feeling adventurous:
Essential software packages
So what do we need to do data science?
Data Cleaning: dplyr (R)
Weirdest, but intuitive syntax
Data Visualization: ggplot2, plotly (R, Python)
Machine Learning: caret, mlr (R)
Data Cleaning: pandas (Python)
Python Libraries: seaborn (Python)
Machine Learning: scikit-learn (Python)
Most consistent ML API!
Visualise model performance: yellowbrick (Python)
Basic Tools
What do we need?
Requirements: Free (ideally open source) + Cross Platform
IDE
Exploratory Tool
(data
visualization)
Why not basic editors?
The anatomy of the ideal IDE:
Plotting area
Variable inspectionScript
Console
Jupyter + JupyterLab
Most likely to grow!
The best option for Python
Atom + Hydrogen
Visualisation Tools
Why do we need
them?
Our options: Past, Orange
My final recommendation
Stay productive! www.mindmatch.ai
Twitter: @thinking_code
E-mail: ba@mindmatch.ai

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