Machine-Learning-in-Python-Workshop

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

Contents

Part 1: Using existing packages for machine learning (Week 1 to 5)

• 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

Part 2: Implementing our machine Learning algorithms and models (Week 5 to 10)

• 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.

Prerequisites:

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

Videos in YouTube (in Persian):

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

My website Address:

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

The workshop page on my website:

• 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.

References:

• Parts 6 to 8 are inspired from the wonderful course <a href="http://cs231n.stanford.edu/">cs231n</a>.
• Parts 5 and 6 are heavily inspired from <a href="https://github.com/amueller/scipy-2017-sklearn">SciPy 2016 Scikit-learn Tutorial<a>.