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