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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

How to maintain your GitHub Profile?

Deepak
Deepak Last Updated : 13 Dec, 2021
6 min read

This article was published as a part of the Data Science Blogathon

Objective

Hope you all know about GitHub profile? In this blog, you will learn about different GitHub activities. And, how you can manage it?

Prerequisites

  1. Github Account and basic GIT knowledge
  2. A blog profile (for hands-on, not compulsory as it can be replicated to other workflows)

Read this article to know what is Git and GitHub.

Introduction

There is always a need to manage source code repositories such as Github or bitbucket, and then to use that in downstream tasks to manage the integration and delivery pipelines in a project using different services.

Now instead of using different services for Continous Integration and Continous Delivery tasks, we can directly use the Github Actions to execute our development workflows.

Automate, customize, and execute your software development workflows right in your repository with GitHub Actions. You can discover, create, and share actions to perform any job you’d like, including CI/CD, and combine actions in a completely customized workflow.

Source: article

Github Actions

GitHub Actions are packaged scripts to automate tasks in a software development workflow in GitHub. It can be configured to trigger complex workflows that meet our organization’s needs each time developers check(push/pull/merge) new source code into a specific branch, at timed intervals, or manually. The result is a reliable and sustainable automated workflow which leads to a significant decrease in development time.

We access ”Actions”, we can click on the Actions tab in the repository in which we need to set up the workflow.

In the Actions tab page, we will find different workflows to select whether we want to build and test container and push it to the Google container registry or Amazon container registry, or even IBM and Alibaba. We can even create our own actions as per workflow requirements.

Types of Github Actions

  • Container Actions: The environment is part of the action’s code. These actions can only be run in a Linux environment that GitHub hosts. Container actions support many different languages
  • JavaScript Actions: It doesn’t include the environment in the code. We will have to specify the environment to execute these actions. We can run in a VM in the cloud or on-premises. JavaScript actions support Linux, macOS, and Windows environments

Features

  1. Linux, macOS, Windows, ARM, and containers: Hosted runners for every major OS make it easy to build and test all your projects. Run directly on a VM or inside a container. Use your own VMs, in the cloud or on-prem, with self-hosted runners
  2. Matrix builds: Save time with matrix workflows that simultaneously test across multiple operating systems and versions of your runtime
  3. Any language: GitHub Actions supports Node.js, Python, Java, Ruby, PHP, Go, Rust, .NET, and more. Build, test, and deploy applications in your language of choice
  4. Live logs: See your workflow run in real-time with color and emoji. It’s one click to copy a link that highlights a specific line number to share a CI/CD failure
  5. Built-in secret store: Automate your software development practices with workflow files embracing the Git flow by codifying it in your repository
  6. Multi-container testing: Test your web service and its DB in your workflow by simply adding some docker-compose to your workflow file

GitHub Actions workflow

A GitHub Actions workflow is a process that we set up in our repository to automate software development life-cycle tasks, including GitHub Actions. With a workflow, we can build, test, package, release, and deploy any project on GitHub.
To create a workflow, we add actions to a .yml file in the .github/workflows directory in your GitHub repository.

Example:

name: A Hello World workflow
on: push
jobs:
  build:
    name: Hello world action
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v1
      with:
        MY_NAME: "XYZ"

name: Used to give a name to the workflow

on: Used to specify the trigger when to run the workflow, It can be events such as push, pull or an event configuration map that schedules a workflow or restricts the execution of a workflow to specific files, tags, or branch changes

jobs: A job is associated with a runner, a workflow must have at least one job. A runner can be GitHub-hosted or self-hosted and the job can run on a machine or in a container

runs-on: Used to specify runner, in the above example we specify to run the job on ubuntu-latest

steps: Used to specify what tasks a job has to complete. In our example, the step uses the action actions/checkout@v1 to check out the repository

with: Used to specify any variable value for the workflow, if the action required some variable then ‘with’ is used to set those variable value

Latest Blog Post Workflow

Let’s take the concepts that we have learned to apply to a real situation. Let say we have a blog site or we write and post blogs to some websites such as Analytics Vidhya, and suppose we need to update our GitHub profile with our latest blog posts. One way is to manually update the GitHub profile when we publish some posts however GitHub Actions can help us to remove the manual intervention.

First of all, to update our GitHub profile we need to create one. We need to create one repository with the same name as our GitHub username, in that repository create a readme.md file and copy the below content and commit the file 

### ⚡ Latest Blog Posts
<!-- BLOG-POST-LIST:START -->
<!-- BLOG-POST-LIST:END -->

Once we have created the repository and the readme file, then follow the below steps

  1. Create a .github/workflows directory in the above-created repository
  2. In the .github/workflows directory, create a file named blog-post-workflow.yml
  3. Add below workflow in the file
name: Latest blog post workflow
on:
  schedule: # Run workflow automatically
    - cron: '0 0 * * 1' # Runs every monday
  workflow_dispatch: # Run workflow manually (without waiting for the cron to be called), through the Github Actions Workflow page directly
jobs:
  update-readme-with-blog:
    name: Update this repo's README with latest blog posts
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - uses: gautamkrishnar/blog-post-workflow@master
        with:
          feed_list: "https://www.analyticsvidhya.com/blog/author/dmoonat/feed"
          max_post_count: "5"

Most of the attributes we have already discussed, others such as

  • schedule: Used to schedule the workflow based on a cron job, here the cron job is set to trigger the workflow every Monday. For more info on Cron Jobs, check this link
  • workflow_dispatch: Used to run the workflow manually also from the Github Actions page directly

With this workflow, we will update our readme file with our latest blog posts. The workflow runs on ubuntu-latest, it has 2 steps, first, it will check out our code, and then run the blog-post-workflow from gautamkrishnar repository (Person who have created the workflow). We need to pass some values to the workflow variables such as

  1. feed_list: URL from where to fetch the blogs, in this case, I have added my Analytics Vidhya blogs page link with ‘/feed’ appended to it
  2. max_post_count: Maximum posts to fetch and  display

For other options, check this out.

Once we have edited the file, we can commit it. Committing the workflow file in the repository will trigger the push event and run our workflow. To view our workflow, we need to head to the ‘Actions’ tab and click the workflow we want to monitor, we can check the logs of the workflow and can debug if it fails based on it.

GitHub Profile | Workflow
Workflow dispatch

Source: Author [GitHub account]

 

Once the workflow is successful, It will display the blog posts links in the readme file which is displayed on the homepage of the GitHub account. 

GitHub latest blog posts
Source: Author [GitHub account]

 

End Notes

The workflow that we have seen in this blog is a very basic one, with GitHub profile, maintenance actions we can implement advanced workflow for our software development or for our machine learning tasks.

References

https://docs.microsoft.com/en-in/learn/modules/github-actions-automate-tasks/2-github-actions-automate-development-tasks

https://docs.github.com/en/actions/quickstart

https://github.com/gautamkrishnar/blog-post-workflow

The media shown in this article is not owned by Analytics Vidhya and are used at the Author’s discretion. 

Deepak
Deepak

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