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Introduction to Machine learning
Jupyter Notebook Python
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README.md

README.md

Introduction to Machine learning

This is the main repository of the Machine learning course teach at November/December of 2017. You can find different folder containing the dataset and content needed for the course.

Table of content

Slides

Class Slides Solutions
Session 1 - The What Why and when of Machine Learning Slides Solutions
Session 2 - Feature Engineering Slides Solutions
Session 3 - Linear Classifiers Slides
Session 4 - Classifier Optimization Slides
Session 5 - Neural Network Overview Slides

Getting Started

Python Installation

Windows Installation

  • For windows, the easiest thing to do is to install a distribution of python, and not just the raw python installation.

  • One of the most popular distributions of Python is Anaconda (https://www.anaconda.com/download/)

  • Download the 3.6 version (because more future compatibility)

    • For our purposes, we don't care as long as version is greater that 2.7
  • Follow Installation instructions

    • Add it to your path, as that would make life so much easier.
    • Do it during the installation installer
    • or if you know what you're doing, set it later. [Not Recommended]

Linux Installation

  • Assuming that you're an expert
    • sudo apt-get install python3.6
    • sudo apt-get install jupyter-notebook python-scipy python-spyder

Environment Setup

Anaconda (Windows)

  • Open up Anaconda Navigator
  • If it asks you to make a virtual environment, do that with the default settings
  • Open up an anaconda prompt anaconda-prompt
  • install the required packages conda install seaborn scikit-learn matplotlib *// [Optional]// if you like a scientific IDE - conda install spyder

Linux

  • if you do not have a virtual-env : sudo pip install matplotlib seaborn scikit-learn scipy numpy notebook

  • if you do have a virtual env, skip the sudo

  • Cheers! You're ready to go! Open up spyder or notebook

Cython==0.26.1
ipykernel==4.6.1
ipython==6.1.0
ipython-genutils==0.2.0
ipywidgets==7.0.0
jupyter-client==5.1.0
jupyter-console==5.2.0
jupyter-core==4.3.0
jupyterlab==0.27.0
jupyterlab-launcher==0.4.0
matplotlib==2.0.2
notebook==5.0.0
numpy==1.13.1
numpydoc==0.7.0
pandas==0.20.3
pandocfilters==1.4.2
scikit-image==0.13.0
scikit-learn==0.19.0
scipy==0.19.1
seaborn==0.8
tensorflow==1.4.0
tensorflow-tensorboard==0.4.0rc3

Interesting links

Session 1

Session 2

Session 3

  • Random Forest - Good video for understand the algorithm.
  • Random Forest 2 - Random forest algorithm introduction with explanations of the main concepts.
  • SVM - SVM algorithm simple explanation.
  • SVM 2 - SVM algorithm advanced explanation.
  • SVM 3 - Demo.
  • Visual Machine Learnign - A visual introduction to machine learning.

Session 4

Questions

If you want to ask something, feel free to write your question in the issues section.

Courses

  • November '17 (12 assistants)
  • January '18
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