Intendierte Lernergebnisse
Inhalt / Content: This tutorial supports the beginners to Data Mining and Deep Learning who are enrolled in data mining, machine learning, or deep learning courses.
Lehrmethodik inkl. Einsatz von eLearning-Tools
Online
Inhalt/e
The course consist of three main parts:1- Introduction to Data Mining:Data Fundamentals (Data Science Terminology, Data Types, ... etc).Data Mining Process (Data Collection, Understanding, Preparation, Modelling, ... etc).Data Mining Techniques (Regression, Classification, Clustering, Neural Networks, ... etc).How to Build and Evaluate Data Mining Models.2- Introduction to Deep Learning:What is AI, Machine Learning, and Deep Learning ?Artificial Neural Networks ArchitecturesML Techniques (Supervised, Unsupervised, Semi-Supervised Learning).Activation Functions, Loss Functions, OptimizationDeep Learning Frameworks (Tensorflow, Keras, Pytorch, ... etc).Introduction to Convolutional Neural Networks.3- Practical Implementation:Most of the parts mentioned above will be implemented during the tutorial online through coding.Introduction to Pandas, Numpy.Introduction to Tensors.How to build and evaluate your model.Interaction with different datasets (MNIST, Fashion MNIST, Diabetes, CIFAR10,... etc)Final Project (Optional)Parts of the mentioned above will be explained in parallel to review the theory and implement it in practice.
Erwartete Vorkenntnisse
- Basic-Intermediate Python Skills.(Python basics can be covered during the tutorial depending on the knowledge of the students)- Notice: Content can be adapted depending on the time-frame and previous knowledge of the enrolled students (Even If they have no knowledge), the tutorial is meant to support students who need help!