Python for Machine Learning (en)
The course begins with a Deep Dive in Python to ensure that students have the necessary skills.
Through hands-on classes and labs, students will learn how to use NumPy and Pandas for data management, perform statistical analysis, and implement classification, regression, and clustering algorithms with Scikit- Learn. The course also includes theoretical sessions on Artificial Intelligence.
CODE: DSAI101
Category: Artificial Intelligence
Teaching methodology
The course includes educational laboratories in which each student will be able to work in order to complete training exercises that will provide practical experience in using the instrument, for each of the topics covered during the course.
Prerequisites
- Basic knowledge of programming
- Familiarity with Linux
The following is an overview of course content:
- Deep Dive in Python
- NumPy
- Pandas
- Data Exploration and Pre-Processing in Python
- Regression with Scikit-Learn
- Classification with Scikit-Learn
- Clustering with Scikit-Learn
At the end of the course, participants will be able to:
- Understanding the capabilities of Python.
- Use variables and perform computations in Python.
- Use the main control and loop structures in Python.
- Using the main built-in data structures of Python.
- Using Python for statistical calculations.
- Introduction to the NumPy library for performing
- calculations efficiently and quickly.
- Introduction to the Pandas library for analyzing and collecting data.
- First steps of Machine Learning with Python leveraging the Scikit- Learn library.
- Basics of Clustering with Scikit-Learn.
- Basics of Linear Regression with Scikit-Learn.
- Basics of Classification with Scikit-Learn.
Duration – 1 day
Delivery – in Classroom, On Site, Remote
PC and SW requirements:
- Internet connection
- Web browser, Google Chrome
- Zoom
Language
- Instructor: English
- Workshops: English
- Slides: English
