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 (1)
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
