
Python for Machine Learning | DBDA.X427
This course introduces students to the Python programming language essential for data manipulation, statistical analysis, and predictive modeling techniques required for machine learning and artificial intelligence.
We will explore the wonderfully concise and expressive use of Python’s advanced module features and apply it in probability, statistical analysis, training models, and various other applications. Students will explore mathematical operations with array data structures, optimization, probability density function, interpolation, visualization, and other high-performance benefits of core scientific packages such as NumPy, Pandas, scikit-learn, and Matplotlib.
Additionally, students will learn modern machine learning concepts and techniques, including supervised, unsupervised, and semi-supervised learning, to develop predictive models using Python libraries. The course concludes with a real-world, end-to-end machine learning project, providing students with practical experience in solving challenging problems.
Learning Outcomes
At the conclusion of the course, the student should be able to
- Develop complex functions and scripts to perform complicated calculations to solve engineering, financial, mathematical and scientific problems and visualize the results of these calculations.
- Install, configure Python and essential Python development tools and write programs to perform data analysis, statistical analysis, learning and AI techniques.
- Manage and manipulate data, perform data type conversions, merge datasets, deal with missing values, and extract, delete, or transform subsets of data based on logical criteria.
- Manage a complete machine learning workflow, from data preparation, dimensionality reduction and feature engineering to model selection, training, prediction, evaluation and optimization through a real-world machine learning project.
- Attain deeper understanding of the mathematical toolkit provided by powerful core packages and acquire hands-on experience.
Topics Include
- Training models
- Random forests
- Dimensionality reduction
- Clustering methods
Skills Needed:
Basic Programming Knowledge as can be acquired in Python Programming for Beginners (CMPR.X415) and a knowledge of Fundamentals of Statistics
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Schedule
Date: | Start Time: | End Time: | Meeting Type: | Location: |
---|---|---|---|---|
Mon, 05-19-2025 | 8:30 a.m. | 3:00 p.m. | Flexible | SANTA CLARA / REMOTE |
Tue, 05-20-2025 | 8:30 a.m. | 3:00 p.m. | Flexible | SANTA CLARA / REMOTE |
Wed, 05-21-2025 | 8:30 a.m. | 3:00 p.m. | Flexible | SANTA CLARA / REMOTE |
Thu, 05-22-2025 | 8:30 a.m. | 3:00 p.m. | Flexible | SANTA CLARA / REMOTE |
Fri, 05-23-2025 | 8:30 a.m. | 3:00 p.m. | Flexible | SANTA CLARA / REMOTE |