> For the complete documentation index, see [llms.txt](https://opencampus.gitbook.io/opencampus-machine-learning-program/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://opencampus.gitbook.io/opencampus-machine-learning-program/courses/einfuehrung/woche-8-zeitreihenanalysen.md).

# Week 5 - Time Series Analyses and Introduction into Machine Learning

### This week we will...

* learn about different patterns in times series
* walk through the general procedure for training machine learning algorithms
* get to know how to test predictions on Kaggle
* get an impression of current developments in AI

### Learning Resources

{% file src="/files/OUbb2WyTEihzrwic31gZ" %}

* [Example notebook](https://colab.research.google.com/github/opencampus-sh/einfuehrung-in-data-science-und-ml/blob/main/Zeitreihenanalyse/time_series_predictions.ipynb) for graphical analyses of time series

### Until next week you should...

* [x] complete the sections on „[Linear Regression](https://learn.deeplearning.ai/specializations/data-analytics/lesson/d211v/what-is-linear-regression%3F)“ and „[Multiple Linear Regression](https://learn.deeplearning.ai/specializations/data-analytics/lesson/syrdh/multiple-linear-regression)“ from Module 4 of the course Python for Data Analytics by Deeplearning.ai (about 2 hours).<br>
* [x] download the test data set from Kaggle and merge it with your current data set.
* [x] write code to split your dataset into a training dataset from 01.07.2013 to 31.07.2017, a validation dataset from 01.08.2017 to 31.07.2018, and the test set from 01.08.2018 to 31.07.2019.\
  (Ensure that the number of rows and the defined IDs match those in the downloaded test dataset!)


---

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