This repository contains course materials for my Machine Learning class on the topic of Time Series specificities:

• unsupervised_awareness.pdf : course presentation with teaching content
• time_series_usecase.ipynb : Python notebook illustrating the course notions
• time_series_usecase-empty.ipynb : Python notebook illustrating the course notions, empty versions for students

To follow the notebooks with Google Colab, simply go to https://colab.research.google.com/. Import a new notebook from GitHub, search for "jfabrice" and open one of the notebooks of this class (ml-awareness-unsupervised-learning), for example time_series_usecase-empty.ipynb. Then click on "Copy to Drive" to be able to execute it. The first section of the notebook is there to initialize the environment from Google Colab.

To setup the Anaconda environment with required dependencies, execute the following instructions in Anaconda prompt or Linux shell.

# Clone this github repository on your machine
git clone https://github.com/jfabrice/ml-awareness-unsupervised-learning.git

# Change working directory inside the repo
cd ml-awareness-unsupervised-learning

# Create a new virtual environment
conda create -n timeseriesenv python==3.6

# Activate the environment
## For Linux / MAC
source activate timeseriesenv
## For Windows
activate timeseriesenv

# Install the requirements
pip install -r requirements.txt