In recent years, there has been an explosion of tools and technologies in the ML/AI space. While this is understandable in such a fast moving field, it also presents a challenge to newcomers who have to decide which ones to try first, and where the right mix between cutting edge and stability is. As a data scientist, there is always more theory to learn, so you should maximize your productivity. This talk presents a complete and free/open-source tooling solution that you can start using right away, based on many hours of research and comparisons.

1. Optimal Tooling for Machine
Learning and AI
Boyan Angelov
Data Scientist, MindMatch

2. Who is this presentation for?

3. Main topics
Languages /
Software
Packages
IDEs
Visualisation /
EDA tools

4. Which languages should I use?

5. More is better!
And if you are feeling adventurous:

6. Essential software packages

7. So what do we need to do data science?

8. Data Cleaning: dplyr (R)
Weirdest, but intuitive syntax

9. Data Visualization: ggplot2, plotly (R, Python)

10. Machine Learning: caret, mlr (R)

11. Data Cleaning: pandas (Python)

12. Python Libraries: seaborn (Python)

13. Machine Learning: scikit-learn (Python)
Most consistent ML API!

14. Visualise model performance: yellowbrick (Python)

15. Basic Tools

16. What do we need?
Requirements: Free (ideally open source) + Cross Platform
IDE
Exploratory Tool
(data
visualization)

17. Why not basic editors?

18. The anatomy of the ideal IDE:
Plotting area
Variable inspectionScript
Console

19. Jupyter + JupyterLab
Most likely to grow!

20. The best option for Python

21. Atom + Hydrogen

22. Visualisation Tools

23. Why do we need
them?

24. Our options: Past, Orange

25. My final recommendation

26. Stay productive! www.mindmatch.ai
Twitter: @thinking_code
E-mail: ba@mindmatch.ai