In this course, you will continue to use the Python programming language to perform machine learning and data science tasks. More specifically, you will use Pandas, NumPy, Matplotlib and Sci-kit learn to develop machine learning and predictive analytics solutions.
Data Analytics Tasks:
- Develop predictive models for appropriate customer credit limits
Tools used: Python, Jupyter Notebook, Matplotlib, Pandas, SciKit-Learn, SQL
Course Topics:
- Using the Sci-kit learn machine learning library for Python
- Identifying and solving collinearity through feature engineering and feature selection
- Constructing, justifying, and applying custom data science processes
- Drawing relationships between learner performance and measured features to help understand model performance
- Conducting feature selection to investigate the correlation between different features in a dataset
- Defining the business purpose of a data analytics project and making a principled, realistic analysis plan
- Assessing the predictive performance of classifiers by examining key error metrics
- Identifying where learning methods fail and gaining insight into why with error analysis
- Selecting and justifying appropriate types of data analysis and statistical procedures
- Using data mining tools and different classifiers (e.g., k-nearest neighbor, decision trees, support vector machines) to develop predictive models