✏️ Projects and Exercises

• Numpy, Pandas, and Matplotlib:

  • Numpy, Pandas, and Matplotlib Exercises

• Classification and Regression with Scikit-learn:

  • Some Regression tasks
  • Classification on Cifar10 Dataset
  • Classification on Cifar100 Dataset
  • Classification on MNIST Dataset
  • Regression on California Housing Dataset
  • Classification on Titanic Dataset
  • Classification on Student Dataset

• Classification and Regression Using SVM:

• • Classification on Cifar10 Dataset Using SVM
  • Classification on Cifar100 Dataset Using SVM
  • Classification on Fashion MNIST Dataset Using SVM
  • Classification on Pima Indian Diabetes Dataset Using SVM
  • Regression on Abalone Dataset Using SVM
  • Regression on California Housing Dataset Using SVM

• Clustering:

  • Clustering on Car Evaluation Dataset

• PCA and SVD:

  • Some PCA and SVD tasks

✏️ Important Concepts

• Train, Validation and Test Sets in Machine Learning:

  • What is the Difference Between Test and Validation Datasets? by Jason Brownlee
  • Why exactly using a test set for model evaluation is a bad idea?

• Algorithms and Models in Machine Learning:

  • Difference Between Algorithm and Model in Machine Learning by Jason Brownlee

• Evaluation Metrics or Loss Functions for Regression:

  • Evaluation Metrics for Regression models- MAE Vs MSE Vs RMSE vs RMSLE by Akhilendra

• Datasets and Projects in Machine Learning:

  • Famous Machine Learning Datasets You Need to Know by Uniqtech
  • 10 Standard Datasets for Practicing Applied Machine Learning by Jason Brownlee
  • 5 Data Science Projects That Will Get You Hired in 2020
  • 5 free resources every data scientist should start using today by Yitaek Hwang

• Good Resources to Find Datasets:

  • UCI Machine Learning Repository
  • Kaggle
  • Elite data science
  • Dataset search

• Some Useful Blogs:

  • Seaborn Heatmaps: 13 Ways to Customize Correlation Matrix Visualizations by Okoh Anita
  • Breaking the curse of small datasets in Machine Learning: Part 1 by Jyoti Prakash Maheswari
  • Breaking the curse of small datasets in Machine Learning: Part 2 by Jyoti Prakash Maheswari
  • Imbalanced Data : How to handle Imbalanced Classification Problems

✏️ Notes and Experiences

• Anaconda:

  • Anaconda Installation

  • How to Install Anaconda on Ubuntu 20.04

  • Install Miniconda3 on Linux:

    • Download the latest shell script:

      wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
      

    • Make the miniconda installation script executable:

        chmod +x Miniconda3-latest-Linux-x86_64.sh
      

    • Run miniconda installation script:

        ./Miniconda3-latest-Linux-x86_64.sh
      

  • Conda (Starting and managing)

    1️⃣ conda --version (in a terminal command line) ➡️ Conda version number (if it is installed properly).

    2️⃣ conda update conda and then y (if needed) ➡️ Update conda to the current version.

    • Anaconda Prompt: 1️⃣ Start menu 2️⃣ search for and open "Anaconda Prompt".
    • Windows Command Prompt: 1️⃣ Start menu 2️⃣ search for and open "cmd".
      • When I typed conda --version in the Command Prompt, I encountered conda is not recognized as an internal or external command, operable program or batch file. This issue was resolved by Method 3 of this page.

  • Managing environments

    • Some useful terminal commands:

      1️⃣conda create --name envname or conda create -n envname ➡️ Create the environment "envname".

      2️⃣ conda activate envname➡️ Activate the environment "envname".

      3️⃣ conda info --envs➡️ A list of all your environments (active environment with an asterisk (*)).

      4️⃣ conda install pkgname ➡️ Install the package "pkgname" in the active environment.

      5️⃣ conda list -n envname or conda list➡️ A list of all packages installed in the active environment.

      6️⃣ conda list -n envname pkgname➡️ To see if the package "pkgname" is installed in "envname".

      7️⃣ conda install --revision=0 or conda install --rev 0➡️ Restore active environment to the default version.

      8️⃣ conda deactivate ➡️ Deactivate environment.

      9️⃣ conda remove --name envname --all➡️ Remove the environment "envname".

    • If you encontered the Error 403 while conda update conda or conda create -n envname:

      conda config --add channels conda-canary
      conda config --remove channels defaults
      

      or refer to Troubleshooting (403 Error).

    • Conda cheat sheet

  • Python and Jupyter Lab

    • Python Installation (by a terminal command line): Type conda install python=vnumber to install version "vnumber" of Python in the active environment.

    • Jupyter Lab Installation (by a terminal command line): Type conda install jupyterlab to install Jupyter Lab in the active environment.

    • Use jupyter lab to start Jupyter Lab.

    • Installing the IPython kernel

      • Terminal command: python -m ipykernel install --user --name envname --display-name "Python (envname)"

  • conda-forge

    • Installing packages from conda-forge:

      • conda config --add channels conda-forge ➡️ Register the conda-forge channel as a package source for conda

      • conda config --set channel_priority strict ➡️ Activate the strict channel priority.

• Python:

  • GitHub Repository: Practical Python

  • Python Cheat Sheet

  • Python Cheat Sheet-Complete

  • Installation of python libraries:

    • Installing libraries with the command conda install "libname":

    Some libraries could not be installed with the above command line. Then, you can either do `pip install 
    

    "libname"` or download the source and install that manually as follows.

    • Installing libraries manually:

      Download the source file of the library containing the file setup.py ➡️ Launch the anaconda prompt and

    navigate to the folder that contains the extracted downloaded files , e.g., with the command cd /d d:\anaconda3\tflearn- master ➡️ Run python setup.py install

• Colab:

  • What is Colaboratory?

  • A good approach to import local datasets into the Colab:

      1️⃣ Compress the dataset, i.e., as a `zip` file 2️⃣ Upload the compressed file to your Google drive 3️⃣ Mount your Google drive into the colab 4️⃣ Unzip the compressed file in Colab.
    

  • A good approach to unzip dataset files from Google drive into the content folder in colab:

      ```
      import os
      if not os.path.exists("/content/dataset"):
        print("unzip files!")
        !unzip -q "/content/drive/My Drive/dataset.zip"
      ```
    

  • To prevent Google Colab from disconnecting:

    Google Colab notebooks have an idle timeout of 90 minutes and absolute timeout of 12 hours. This means, if user does not interact with his Google Colab notebook for more than 90 minutes, its instance is automatically terminated. Also, maximum lifetime of a Colab instance is 12 hours.

      To prevent Google Colab from disconnecting, Open developer settings in your web browser with `Ctrl+Shift+I` ➡️ Click on console tab  ➡️  Type the following code block in the console prompt:
    

        function ClickConnect(){
     console.log("Working"); 
     document.querySelector("colab-toolbar-button").click() 
     }setInterval(ClickConnect,60000)
    

• Markdown:

  • Mastering Markdown
  • LaTeX Math Symbols Cheat Sheet

• Git:

  • Installation
  • Documentation
  • Git Handbook (10 minute read)
  • Git Cheat Sheet
  • Videos (Persian): Git by Amir Hasan Azimi
  • Videos (English): GitHub Ultimate-Master Git and GitHub by Jason Taylor

• Books:

  • Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David
  • Hands‑On Machine Learning with Scikit‑Learn, Keras, and TensorFlow 2 by Aurélien Geron

• Other good resources:

  • Free online ebook: Forecasting: principles and practice by Rob J Hyndman, and George Athanasopoulos
  • Brief visual explanations of machine learning concepts with diagrams: Machine Learning Glossary
  • Made With ML: A collection of the best ML tutorials, toolkits and research organized by topic

• Other useful links:

  • GitHub Repository: Tools in Data Science by Hossein Hajiabolhassan
  • GitHub Repository: Applied Machine Learning by Hossein Hajiabolhassan