Machine Learning Dojo - Part I

Introduction

Welcome Machine Learning Dojo - Part I. We have a lot of work ahead of us to do but we are going to start small. For this dojo you do not need to have any previous experience with machine learning nor Python. Dojo duration is planned to be around three hours depending on how fast you complete your tasks.

Prerequisites

Some programming skills in any language.

Environment setup

In order to start please follow these steps:

• Navigate to your home folder

  cd ~
  

• Download and install Anaconda from here (install version 3.7): https://www.anaconda.com/distribution/. You can also do it from command line in a following way:

  curl -o Anaconda2-4.4.0-MacOSX-x86_64.sh https://repo.continuum.io/archive/Anaconda2-2019.07-MacOSX-x86_64.sh
  

• Start installation and follow instructions on the screen. Accept all default options offered during installation:

  bash Anaconda2-2019.07-MacOSX-x86_64.sh
  

• Update environment variables:

  source ~/.bash_profile
  

Install Tensorflow

• Install package directly from Google repository:

  pip install https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.3.0-py2-none-any.whl
  

• In case you do not have pip (you should though) this is how you can install it:

  sudo easy_install pip
  

• Type right there in the console

  python
  

• Python console will open. Enter these two lines of code:

  import tensorflow as tf
  tf.__version__
  

• The result should be:

  '1.3.0'
  

Starting Jupyter notebook

• Navigate to folder where you want to checkout Github project for further fork

• Checkout required Github repository from here.

  git clone https://github.com/ljubisap/ml-dojo-part-I.git
  

• Navigate into newly created folder

  cd ml-dojo-part-I
  

• Execute Jupyter with following commands

  export BROWSER=open
  jupyter notebook Machine\ Learning\ Dojo\ -\ Part\ I.ipynb
  

  Note: Since we are going to use some larger photos, you might need to start Notebook in a following way:

  jupyter notebook --NotebookApp.iopub_data_rate_limit=10000000000  
  

  and then to select Notebook file you want. This tutorial has two of them:

  • Do it yourself.ipynb
  • Machine Learning Dojo - Part I.ipynb

  by clicking on a file name, appropriate Notebook will be opened.

Further readings and references

• Scikit Learn website
• Pandas website
• Scipy website
• Numpy website
• Matplotlib website
• Titanic dataset on Kaggle
• House prices dataset on Kaggle
• Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
• Introduction to Data Science - Laura Igual and Santi Seguí
• Code academy Python course