Machine Learning & Data Science with Python
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.
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.
Register 21 days before class start date and save 10%! Enter discount code EARLY10 during registration.
Register 21 days before class start date and save $250! Enter discount code EARLY250 during registration.
- 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
Is there a discount available for current students?
UMBC students and alumni, as well as students who have previously taken a public training course with UMBC Training Centers are eligible for a 10% discount, capped at $250. Please provide a copy of your UMBC student ID or an unofficial transcript or the name of the UMBC Training Centers course you have completed. Online courses are excluded from this offer.
What is the cancellation and refund policy?
Student will receive a refund of paid registration fees only if UMBC Training Centers receives a notice of cancellation at least 10 business days prior to the class start date for classes or the exam date for exams.