自備筆電。For non-EE and non-CS students.兼通識A6*。。A6*:數學數位與量化分析領域。可充抵通識

2024/2/19 updated. Course outline and materials of 1st week. https://docs.google.com/document/d/1nb8CWIcRap_hbMo1Yv54KibXwTHhbpUJqWawRO1NYjU/edit?usp=sharing 2024/1/16 updated. 1. There are no authorization code in this course because it is controlled by the system and the limit of the number of student is 80. Therefore, if you ask the authorization code, I won't reply you. 2. Before you take this course, please took Python programming or related course before, or understood Python at least. 3. There are no online classes, e-learning materials, live streaming, and recorded video in this course, right now. 4. If you want to audit / sit in on this course, please come to the classroom in the first week. Please do not send the email to the teacher or TAs. 5. The final schedule and details of this course will be updated on the middle of Feb. Grading: 1. Homework 30% 2. Midterm Project 30% 3. Final Project 40% Schedule (temp) Week 1 Course Overview, What is Data Analysis, and What is Machine Learning Week 2 Steps and Flows in Data Analysis, Introduction to Pandas, Functions and Module in Pandas for Data Analysis I Week 3 Functions and Module in Pandas for Data Analysis II Week 4 Machine Learning: Supervised vs Unsupervised vs Reinforcement Learning Week 5 Homework 1 Presentation, and Other Implementations and Discussions Week 6 Algorithms and Modules in Supervised Machine Learning I Week 7 Algorithms and Modules in Supervised Machine Learning II Week 8 Midterm Project Presentation Week 9 Algorithms and Modules in Unsupervised Machine Learning Week 10 Data Modeling, Tuning, and Explanation (by Manual) Week 11 Data Modeling, Tuning, and Explanation (by Automatic and Functions) Week 12 Homework 2 Presentation, and Other Implementations and Discussions Week 13 Case Study: Energy Saving and Product Defect Detection Week 14 Case Study: Prognostics and Health Management (PHM) Week 15 Case Study: When Machine Learning Meets Groundwater Week 16 Final Project Presentation ---------------------------------------------------------------------------------- This course will be your guide to learning how to use the power of Python to analyze data and use powerful machine learning algorithms, and then create beautiful visualizations for the analysis results and predictions. This course is designed for beginners with some programming experience, the guys who already know some Python and are ready to dive deeper into using those Python skills for data analysis and Machine Learning, or experienced developers looking to make the jump to Data Science. I want to help guide students to learning a set of skills to make them extremely hirable in today's workplace environment. Therefore, I'll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Enroll in the course and become a data scientist!

1. Use Python for Data Analysis and Machine Learning 2. Understand how to Use Tools to Analyze Data 3. Understand how to Use Existing Machine Learning Modules/Packages 4. Learn Related Modules and Tools in Python, like NumPy, Pandas, Matplotlib, and SciKit-Learn

Take Python programming or related course before, or understand Python

1. Introduction to Machine Learning with Python: A Guide for Data Scientists 2. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 3. Machine Learning with Python Cookbook： Practical Solutions from Preprocessing to Deep Learning 4. Python Cookbook 5. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts,Tools, and Techniques to Build Intelligent Systems