Key takeaway on how to start your ML journey

I recently gave a talk on ML and that has inspired me to tread down the path and explore more.

I can't call it a recent post but it is a recent post from Jason Brownlee that I read which has given me better direction to add some seriousness to this field

Some key takeaway on how to start mine/your ML journey:

  1. DO NOT go through ML tutorials , Get onto hands-on realtime problems 
  2. Get onto Kaggle
  3. Follow the likes of Triskelion 
  4. Reproduce what others have done, DO NOT reinvent the wheel
  5. What other tools ? Vowpal Wabbit Scikit-learn
  6. R or Weka or Python or DO NOT have a weapon of choice
  7. "Competing consistently is the key to getting good."
  8. New to Data Science? Get started with a tutorial on our most popular competition for beginners, Titanic: Machine Learning from Disaster.
  9. Machine learning is the hottest field in data science, and this track will get you started quickly.

  • Practice a lot: Do as many challenges as you can, incremental improvements.
  • Study evaluation metrics: Really understand AUC, etc. (see a list of metrics)
  • Study the domain: Business cases, papers, state of the art, feature engineering
  • Team up: Top 10 finish is hard, but he need to team up to achieve it.
  • Read the forums: Post to competition threads, understand winning solutions.
  • Share on forums: Lots of angles on a given problem, don’t share too much.
  • Use ensembles: They always improves results, can give you a top 10 with simple models.
  • Experiment: Try out ideas rather than living in thought
  • Creativity: Think outside of the box
  • Tools: Find and use good algorithms.
  • Tuning: Use cross-validation, tune all model parameters.


Popular posts from this blog

Trim / Remove spaces in Xpath?

Complete list of Serenity properties

XPATH for IE / internet explorer