“Machine Learning in Code - Supervised ML tuning essentials” is a rapid immersion into the world of machine l earning, model customization and adaptation f or different projects. House pricing prediction algorithms as well as other real examples of the use of machine learning will be considered during the workshop.
- Remind some basics regarding how machine learning works and what it is used for
- Discuss essentials of feature selection and model tuning
- Tune the algorithm f or house pricing prediction problem and see how works great stuff discussed before
- Beginner and junior data scientists
- Analysts - Developers
- ML-related project managers
- Some basic mathematics knowledge ( number, function, logarithm, multidimensional space)
- Some basic programming skills ( variable, cycle, array, file, algorithm)
- General introduction in Machine Learning motivation and applications ( 40 minutes)
- Short break
- Essentials of supervised machine l earning feature selection and model tuning ( 40 minutes)
- Short break
- Practical application of concepts described above in kaggle's House Prices " getting started" competition and Q&A ( 2.5 hours)
In the last section we will collectively guess and t est various features and models t o develop basics of ML intuition. The workshop's attendees will l earn in which cases and how t o apply simple ML routines instead of hearing some buzzwords or discussing t oo specific stuff.