Data Science
Machine Learning & Data Science with Python
Overview
In recent years industry, not just academia, has found that creating powerful data models provides the next level of value past traditional business intelligence. This course focuses on state of the art machine learning techniques combined with a practical approach designed to teach you to process your data and build models using Python’s scikit-learn. In this class you will learn to load and analyze your data with Pandas (a data analysis library), build visualizations with pyplot, and create predictive models using scikit-learn.
Duration
4 Days
Who Should Take This Course
Audience
This course is for software engineers or data analysts with little or no machine learning knowledge who would like to learn to build and deploy models.
Course Outline
Machine Learning & Data Science with Python
- Introduction to supervised and unsupervised learning
- Importing data with Python
- Using DataFrames with Pandas
- Building plots
- Data exploration
- Creating models
- Support Vector machines
- Decision Trees
- Model evaluation
- Applied Data Science and Business Analytics
- Common Data Science algorithms for supervised and unsupervised machine learning
- NumPy, pandas, Matplotlib, scikit-learn
- Python REPLs
- Jupyter notebooks
- Data analytics life-cycle phases
- Data repairing and normalizing
- Data aggregation and grouping
- Data visualization