Introduction to  Machine Learning for Sociology-Online-Live Basic Course, April-July 2024

A Basic course for beginners in Humanities and Social Science covering basis of machine learning including teaching Python programing towards applications in social science.

This is a great pleasure to share with you that our Machine Learning and Sociology Groups, jointly developed this new official course for the FACULTY OF ARTS, HUMANITIES AND SOCIAL SCIENCE at Technical University of Dresden (https://tu-dresden.de/gsw/phil ), chair of Chair of Methods in Empirical Social Research ( https://tu-dresden.de/gsw/phil/iso/mes/die-professur/prof-dr-menold ).

After organizing two runs our basic Course on Machine Learning with Python Programming in 2023 and 2024 https://www.world-academies.com/groups/machine-learning-1658727501/ by the World-Academies.Com , we are going to share our experience and extend our collaboration for training and application of Machine Learning in specific fields.

This is an official course basically open and free for the official students of the Technical University of Dresden, starting from today, an online-Live Course, 14 Lectures – April 8th to July 15, one lecture per week, Mondays 16:40-18:10

However we can try to accept some international guest students in this official course, if you are looking for such basic Machine Learning Course for application in Sociology Research Area (you can send message in this regard to the registration@world-academies.com ). For guest students upon course completion with related tasks, World-Academies Certification will be provided. The live lectures are recorded and can be reviewed, however participation in live lectures with direct access to the Instructors and Teaching Assistants is expected and recommended also for the international guest students.

Course Content :

Section I- Approaches and Applications

Lecture 1: Machine learning in social science

Section II- Python Programming basics

Lecture 2: Data type, operators, variables, string, formatted string, sets.

Lecture 3: List, Tuple, dictionary, conditional logic, loops

Lecture 4: Function and scope

Lecture 5: Object oriented programming (OOP) & Error handling & Modules

Section III- Introduction to Machine Learning

Lecture 6: Pandas

Lecture 7: NumPy , Matplotlib

Lecture 8: Machine learning with scikit-learn (Supervised learning: Regression)

Lecture 9: Machine learning with scikit-learn (Supervised learning: Classification)

Lecture 10: Deep learning (Introduction to deep learning With TensorFlow)

Lecture 11: Deployment (How to deploy a machine learning model)

Section IV: Machine Learning Case Studies

Student Projects (Example)

Question and Answer Homework at the beginning of each session (10- 15 minutes).