Course Outline
Introduction
- Building effective algorithms in pattern recognition, classification and regression.
Setting up the Development Environment
- Python libraries
- Online vs offline editors
Overview of Feature Engineering
- Input and output variables (features)
- Pros and cons of feature engineering
Types of Problems Encountered in Raw Data
- Unclean data, missing data, etc.
Pre-Processing Variables
- Dealing with missing data
Handling Missing Values in the Data
Working with Categorical Variables
Converting Labels into Numbers
Handling Labels in Categorical Variables
Transforming Variables to Improve Predictive Power
- Numerical, categorical, date, etc.
Cleaning a Data Set
Machine Learning Modelling
Handling Outliers in Data
- Numerical variables, categorical variables, etc.
Summary and Conclusion
Requirements
- Python programming experience.
- Experience with Numpy, Pandas and scikit-learn.
- Familiarity with Machine Learning algorithms.
Audience
- Developers
- Data scientists
- Data analysts
Testimonials (2)
Pelatihan yang luar biasa, salah satu yang terbaik yang pernah saya hadiri! Pembawa acara, Rafal, merespon dengan sempurna permasalahan yang diangkat dan menjelaskan semua caranya dengan sangat matang. JestSaya sangat puas dan dengan senang hati akan memanfaatkan kembali pelatihan yang dilakukan oleh pelatih ini.
Darek Paszkowski - Orange Szkolenia Sp. z o.o.
Machine Translated
Gambar di flipchart, keseluruhan pelatihan.
Kasia Nawrot - Orange Szkolenia Sp. z o.o.
Machine Translated