- Python programming experience.
- Experience with Numpy, Pandas and scikit-learn.
- Familiarity with Machine Learning algorithms.
Audience
- Developers
- Data scientists
- Data analysts
Feature Engineering is the process of selecting and transforming data to improve the accuracy of machine learning algorithms. It requires deep familiarity with the data on the part of a subject matter expert.
This instructor-led, live training (online or onsite) is aimed at persons who wish to apply feature engineering techniques to better process data and obtain better machine learning models.
By the end of this training, participants will be able to:
- Set up an optimal development environment, including all needed Python packages.
- Obtain important insights by analyzing the features of a data set.
- Optimize machine learning models through adaptation of the raw data itself.
- Clean and transform data sets in preparation for machine learning.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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
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