Python for Machine Learning (en)
Explore the foundations of data science and machine learning with our course, DSAI101 – Python for Machine Learning. This hands-on program equips you with essential skills in Python programming, statistical analysis, and the utilization of key libraries like NumPy and Pandas. By delving into topics such as AI fundamentals and Scikit-Learn, you’ll gain the expertise to develop machine learning models for classification, regression, and clustering tasks. Whether you’re a beginner or seeking to deepen your knowledge, DSAI101 provides a solid foundation for leveraging Python in data-driven applications.
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 Programming Knowledge
- Basic Python Knowledge
- Familiarity with Numpy
- Familiarity with Pandas
The following is an overview of course content:
Python Basics: Dive into the fundamentals of Python programming language, covering syntax, data types, control structures, functions, and more.
Statistics: Gain a solid understanding of statistical concepts essential for data analysis, including probability, hypothesis testing, descriptive statistics, and inferential statistics.
Numpy: Explore the powerful capabilities of NumPy, a fundamental package for scientific computing in Python, focusing on array manipulation, mathematical operations, and numerical computing.
Pandas: Learn how to effectively work with data using Pandas, a versatile library for data manipulation and analysis, covering data structures like Series and DataFrame, data cleaning, filtering, and aggregation.
AI Fundamentals: Delve into the foundational concepts of Artificial Intelligence, including machine learning algorithms, supervised and unsupervised learning, model evaluation, and the broader landscape of AI applications.
Scikit-Learn: Discover Scikit-Learn, a user-friendly library for machine learning in Python, and learn how to implement various machine learning algorithms for classification, regression, clustering, and more.
At the end of the course, participants will be able to:
- Use Python for application and script development.
- Implement advanced Python programming concepts, including functions, classes, and exception handling.
- Apply basic statistical techniques for data analysis using Python.
- Effectively use the NumPy library to perform operations on multidimensional arrays and manipulate numerical data.
- Leverage the features of the Pandas library for data manipulation, analysis, and cleaning of tabular data.
- Implement machine learning algorithms using Python and specialized libraries like Scikit-learn.
- Use Python to develop machine learning models for classification, regression, and clustering.
- Evaluate the performance of machine learning models using appropriate metrics.
- Interpret and visualize the results of data analysis and machine learning models using graphs and visualizations.
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
