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ep:labs:10 [2021/12/04 16:23]
vlad.stefanescu [Resources]
ep:labs:10 [2021/12/04 17:03] (current)
vlad.stefanescu [Resources]
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 ===== Resources ===== ===== Resources =====
  
-The exercises will be solved in Python, using various ​popular libraries that are usually integrated in machine learning projects:+In this lab, we will study basic performance evaluation techniques used in machine learning, covering elementary concepts such as classification,​ regression, data fitting, clustering and much more.  
 + 
 +You will work in an environment that is easy to use, and provides a couple of tools like manipulating data and visualizing results. We will use a **Jupyer Notebook** hosted on **Google Colab**, which comes with a variety of useful tools already installed. 
 + 
 +The exercises will be solved in Python, using popular libraries that are usually integrated in machine learning projects:
  
   * [[https://​scikit-learn.org/​stable/​documentation.html|Scikit-Learn]]:​ fast model development,​ performance metrics, pipelines, dataset splitting   * [[https://​scikit-learn.org/​stable/​documentation.html|Scikit-Learn]]:​ fast model development,​ performance metrics, pipelines, dataset splitting
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   * [[https://​numpy.org/​doc/​|NumPy]]:​ scientific computation   * [[https://​numpy.org/​doc/​|NumPy]]:​ scientific computation
   * [[https://​matplotlib.org/​3.1.1/​users/​index.html|Matplotlib]]:​ data plotting   * [[https://​matplotlib.org/​3.1.1/​users/​index.html|Matplotlib]]:​ data plotting
 +
 +As datasets, we will use some public corpora provided by the Kaggle community:
 +
 +  * [[https://​www.kaggle.com/​uciml/​pima-indians-diabetes-database/​data|Classification Dataset]]
 +  * [[https://​www.kaggle.com/​zaraavagyan/​weathercsv|Regression dataset]]
 +
 +You can also check out these cheet sheets for fast reference to the most common libraries:
 +
 +**Cheat sheets:**
 +
 +  * [[https://​perso.limsi.fr/​pointal/​_media/​python:​cours:​mementopython3-english.pdf)|python]]
 +  * [[https://​s3.amazonaws.com/​assets.datacamp.com/​blog_assets/​Numpy_Python_Cheat_Sheet.pdf|numpy]]
 +  * [[https://​s3.amazonaws.com/​assets.datacamp.com/​blog_assets/​Python_Matplotlib_Cheat_Sheet.pdf|matplotlib]]
 +  * [[https://​s3.amazonaws.com/​assets.datacamp.com/​blog_assets/​Scikit_Learn_Cheat_Sheet_Python.pdf|sklearn]]
 +  * [[https://​github.com/​pandas-dev/​pandas/​blob/​master/​doc/​cheatsheet/​Pandas_Cheat_Sheet.pdf|pandas]]
  
 <​solution -hidden> <​solution -hidden>
-Solution{{:​ep:​labs:​lab_12_ml_revisited_solution.zip}}+[[https://colab.research.google.com/​drive/​1aeV9PGF_uxBA3FoKNMEzsiXMxjVSCcm4?​usp=sharing|Solution]]
 </​solution>​ </​solution>​
  
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 ==== Google Colab Notebook ==== ==== Google Colab Notebook ====
  
-For this lab, we will use Google Colab for exploring ​pandas and seaborn. Please solve your tasks [[https://​github.com/​cosmaadrian/ml-environment/blob/master/EP_Plotting_II.ipynb|here]] by clicking "​**Open in Colaboratory**"​.+For this lab, we will use Google Colab for exploring ​performance evaluation in machine learning. Please solve your tasks [[https://​github.com/​vladastefanescu/machine-learning-introduction/blob/main/Machine_Learning_Introduction.ipynb|here]] by clicking "​**Open in Colaboratory**"​.
  
 You can then export this python notebook as a PDF (**File -> Print**) and upload it to **Moodle**. You can then export this python notebook as a PDF (**File -> Print**) and upload it to **Moodle**.
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-===== References ===== 
- 
-[[https://​www.kaggle.com/​uciml/​pima-indians-diabetes-database/​data|Classification Dataset]] 
- 
-[[https://​www.kaggle.com/​zaraavagyan/​weathercsv|Regression dataset]] 
  
-{{namespace>:​ep:​labs:​10:​contents:​tasks&​nofooter&​noeditbutton}} 
ep/labs/10.1638627796.txt.gz · Last modified: 2021/12/04 16:23 by vlad.stefanescu
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