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)
Fantastic training, one of the best I have ever attended! The moderator, Rafal, provided excellent answers to the issues raised and explained all the methods very thoroughly. JestI am very satisfied and will gladly take advantage of the training conducted by this trainer again.
Darek Paszkowski - Orange Szkolenia Sp. z o.o.
Machine Translated
Drawings on a flipchart, the entire training.
Kasia Nawrot - Orange Szkolenia Sp. z o.o.
Machine Translated