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
  14 Hours
 

Testimonials

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