My workshop on machine learning using python language to implement different algorithms (University of Tabriz, Iran, 2017).

• Week 01 and 02: Introduction to Numpy and Matplotlib packages
• Week 03 and 04: Using Scikit Learn for Supervised Learning
• Week 05: Using Scikit Learn for Unsupervised Learning

• Week 06: Linear classification
• Week 07: Implementing Loss functions (Softmax loss and SVM loss)
• Week 08: Implementing gradient descent, Backpropagation and Artifitial Neural Networks (MLP)
• Week 09: Advanced topics including dropout, batch normalization, weight initialization and other optimization methods(Adam, RMSProp)
• Week 10: Inroduction to Deep Learning and implementing a Convolutional Neural Network (CNN) for image classification.

• A basic knowledge of Python programming language.
• A good understaning of Machine Learning.
• Linear Algebra

• http://www.youtube.com/c/SeyedNaserRazavi

• containing anything you need to learn and of course to use machine learning in real world applications:
• http://wwww.snrazavi.ir/

• http://www.snrazavi.ir/ml-with-python/

Note: The materials of this workshop are inspired from awesome lectures presented by Andrej Karpathy at Stanford, 2016.

• Parts 6 to 8 are inspired from the wonderful course cs231n.
• Parts 5 and 6 are heavily inspired from SciPy 2016 Scikit-learn Tutorial.