176x Filetype PDF File size 1.00 MB Source: www.zhaw.ch
Machine Learning Fundamentals in Python Machine Learning Fundamentals in Python Ziel: To develop a new course on Machine Learning Fundamentals in Python Geförderte Fähigkeiten: New module as a part of CAS in Digital Life Sciences Specialization of Machine Learning Research in Life Sciences Nutzende: Projektbeschrieb: Attendees of further education at the IAS This project creates and develops a course module as a part of ● Beginners and/or intermediate in ML and/or Python continuous education at the Institute of Applied Sciences (IAS) ZHAW students and employees The course offers students and/or professionals in all areas to start Maker Space for Coding Literacy with and to develop ML algorithms using Python Projektskizze (Umsetzung & Innovation): Lessons learned: Python Programming Content creation ● Basics, functions, scripts, data structures, data manipulation ● Programming assignments ● Essential data science and data visualization libraries ● scikit-learn, Pandas ● Github + Google Colab ● SciPy, Numpy Real data source ● Seaborn, Matplotlib ● Kaggle ML algorithms implementation ● Supervised and unsupervised learning Reference Books Model regularization and evaluation ● Bias and variance, model over-fitting ● Cross-validation Application to real world data Provide students with digital (programming) know-how for active problem solving in the field of ML Nächste Schritte: Offene Fragen: Content creation with focus on: Flipped Classroom ● Video lectures for selected topics – how? ● unsupervised learning Assignments assessment methodology ● model evaluation ● Generalization, automation??? Create a video tutorials for selected topics Courses inter-connection ● Prerequisites Name: Martin Rerabek Institut/Abteilung: Institute of Applied Simulation
no reviews yet
Please Login to review.