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Optimal Tooling for Machine Learning and AI


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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.

Published in: Data & Analytics
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Optimal Tooling for Machine Learning and AI

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