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ep:labs:10 [2021/12/04 16:30]
vlad.stefanescu [Resources]
ep:labs:10 [2021/12/04 17:03] (current)
vlad.stefanescu [Resources]
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 ===== Resources ===== ===== Resources =====
  
-In this lab, we will study the basic performance evaluation in machine learning, covering elementary concepts such as classification,​ regression, data fitting, clustering and much more. +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 **Google Colab**, which comes with a variety of useful tools already installed. ​+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.
  
-You can also check out these cheet sheets for fast reference to the common libraries:​ +The exercises will be solved in Python, using popular libraries that are usually integrated in machine learning projects:
- +
-**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]] +
-  - [[https://​s3.amazonaws.com/​assets.datacamp.com/​blog_assets/​Python_Seaborn_Cheat_Sheet.pdf|seaborn]] +
- +
-<​note>​This lab is organized in a Jupyer Notebook hosted on Google Colab. You will find there some intuitions and applications for pandas and seaborn. Check out the Tasks section below.</​note>​ +
- +
-The exercises will be solved in Python, using various ​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://​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://www.kaggle.com/uciml/pima-indians-diabetes-database/data|Classification Dataset]] +  * [[https://perso.limsi.fr/pointal/_media/​python:​cours:​mementopython3-english.pdf)|python]] 
-[[https://www.kaggle.com/zaraavagyan/weathercsv|Regression dataset]]+  * [[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>​
  
ep/labs/10.1638628212.txt.gz · Last modified: 2021/12/04 16:30 by vlad.stefanescu
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