Practical Python for AI: Advanced Libraries and Techniques
Master Python libraries like NumPy, Pandas, and Matplotlib for AI coding. Learn data manipulation, visualization, and object-oriented programming.
Master Python libraries like NumPy, Pandas, and Matplotlib for AI coding. Learn data manipulation, visualization, and object-oriented programming.
Dive into advanced Python programming for AI with this comprehensive course. Designed for beginners, it covers essential libraries and techniques used in AI coding. Learn to manipulate data using NumPy arrays and Pandas DataFrames, visualize information with Matplotlib and Seaborn, and understand object-oriented programming concepts. The course focuses on practical applications, guiding you through exercises with real datasets and preparing you for AI modeling with Scikit-learn, TensorFlow, and Keras. By the end, you'll have a solid foundation in Python for AI and a local coding environment ready for advanced AI projects.
4.6
(21 ratings)
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What you'll learn
Master NumPy for efficient array manipulation and operations
Use Pandas for data analysis and manipulation with DataFrames
Perform advanced string operations and file handling in Python
Create data visualizations using Matplotlib and Seaborn
Understand object-oriented programming concepts in Python
Apply Python libraries to real-world AI and data science problems
Skills you'll gain
This course includes:
7 Hours PreRecorded video
5 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This course provides a comprehensive introduction to advanced Python libraries and techniques essential for AI coding. It starts with NumPy, teaching array manipulation and operations crucial for efficient data handling. The Pandas module covers DataFrame usage, data manipulation, and basic statistical analysis. Students learn string manipulation and file handling, vital for data preprocessing. The data visualization section introduces Matplotlib and Seaborn for creating insightful graphs and charts. The course concludes with an introduction to object-oriented programming, explaining classes and inheritance. Throughout, the focus is on practical application, with exercises using real-world datasets. The course aims to prepare students for AI modeling using libraries like Scikit-learn, TensorFlow, and Keras, and helps them set up a local AI coding environment.
Numpy library: Using arrays
Module 1 · 1 Hours to complete
Pandas library: Using DataFrames
Module 2 · 2 Hours to complete
Strings and files
Module 3 · 1 Hours to complete
Data visualization: matplotlib and seaborn
Module 4 · 2 Hours to complete
Object oriented programming: introducing class object
Module 5 · 1 Hours to complete
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Instructor
Academic Profile and Resources
Professor at the School of Business and Technology Management within the College of Business at KAIST, currently on sabbatical at UPM in Madrid, Spain. For more information, visit his homepage. He offers a KAIST MOOC on Practical Python for AI Coding, available in Korean. Additionally, two courses on Coursera are available: Practical Python for AI Coding 1 and Practical Python for AI Coding 2.
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4.6 course rating
21 ratings
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