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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

Data Science Blogathon Winners are Here and Announcing Blogathon 2!

Pranav Dar
Pranav Dar Last Updated : 20 Oct, 2020
5 min read

The Data Science Blogathon is Over! Long Live the Data Science Blogathon II!

Take a deep breath. The first Data Science Blogathon was an overwhelming success with aspiring data science professionals and industry experts coming together to produce high-quality machine learning, deep learning, and analytics articles that our community loved.

We received over 100 articles from our community as writers poured their hearts out into words with the aim of getting their work published in front of a 500,000+ audience! The topics ranged from business analytics and machine learning algorithms (hello, Gradient Descent) to tackling COVID-19 and transforming the energy industry. It was a BROAD range of topics and we loved going through your work and parsing through your thoughts.

The community loved the articles – the number of views were through the roof as the leaderboard showcases, and the comments numbered in the hundreds. We love hosting such impactful articles that not only teach the audience about data science concepts but help them get a move on in their own careers.

So what can you do after such a successful Data Science Blogathon?

You host another grander Blogathon!

That’s right – we are back with the second installment of the Data Science Blogathon and it’s already live. You can register right now and get started on that article you’ve been wanting to write. 🙂

I’ll talk a bit more about this soon but first..

 

Announcing the Data Science Blogathon Winners

Along with this launch, we are delighted to announce the winners of the first Data Science Blogathon.

Every article that met Analytics Vidhya’s standards was published on the Analytics Vidhya blog. This was a one-of-a-kind opportunity to reach out to a HUGE audience and embellish your data science profile and your own brand. And our community embraced this!

So without any further ado, here are the winners in the three categories.

data_science_blogathon_prizes

1. The Most Popular Article Award

The top 3 winners are based on the number of unique pageviews on their articles.

 

2. The Most Engaging Article Award

The top 3 winners are based on the number of unique page views they generated from social media (Facebook, LinkedIn, Twitter, and Instagram).

 

3. Editorial Award

The top 3 winners have been judged by the Analytics Vidhya editorial team based on our internal metrics.

Congratulations to all the winners!

 

So, About the Data Science Blogathon II

If you haven’t registered yet, what are you waiting for? Head over here to get started. There are plenty of prizes on offer again. Every article that meets Analytics Vidhya’s standards will be published on the Analytics Vidhya blog.

There are lucrative prizes on offer as well, in three categories:

1. The Most Popular Article Award

The top 3 winners will be judged based on the number of unique pageviews on their articles. You can track this on the competition leaderboard.

  1. 1st Prize: INR 7500
  2. 2nd Prize: INR 3000
  3. 3rd Prize: INR 1500


2. The Most Engaging Article Award

The top 3 winners in this category will be judged based on the number of unique page views they generate from social media (Facebook, LinkedIn, Twitter, and Instagram).

  1. 1st Prize: INR 6000
  2. 2nd Prize: INR 2500
  3. 3rd Prize: INR 1500


3. Editorial Award

The top 3 winners will be judged by the Analytics Vidhya editorial team based on our internal metrics.

  1. 1st Prize: INR 4500
  2. 2nd Prize: INR 2000
  3. 3rd Prize: INR 1500

 

And that’s not all! We always appreciate the extra effort our community members put in, so if your article generates:

  • More than 15000 views, you get INR 2,000 as a bonus,
  • More than 5000 views, you get INR 1,000 as a bonus

regardless of where you finish in the above three categories!

 

When Will the Winners be Declared?

The winners for all 3 categories will be declared on November 16, 2020.
Even though you need to submit your article by 11.59 pm on November 8th, the views for your article will count until November 15th, 11.59 pm. So keep sharing your article after it’s been published to increase your chances of winning!
Head over to the Data Science Blogathon page and start writing!

 

Why you Should Participate in the Data Science Blogathon

If you’re still on the fence about writing, here are a few more added benefits:

  1. Learn and master the concept – Learning and writing go hand-in-hand. I learned this trick in my college – I used to make notes and explain them to my backbencher friends with simple examples and this helped me register all the hard concepts in my mind along with their applications. You’ll definitely learn much more by following the same approach 🙂
  2. Improve your storytelling skills – I will share a little secret with you here. Two of the most important characteristics of a great data scientist are storytelling skills and a structured thinking mindset. This is not an inherent quality but is developed by working closely on problem statements. Writing a blog improves both of these qualities
  3. Build up your resume – One of the biggest advantages of writing a data science blog is that it makes you stand out from the crowds. It actually reflects your passion and knowledge for this field. Writing blogs will definitely help your catch the eyes of the recruiter.
  4. Get attention and networking opportunities within the community – While gaining knowledge through the process of blog writing is important but getting a chance to network with industry leaders and like-minded people is priceless. Analytics Vidhya has 2.5 million monthly users including some of the well-renowned personalities so there’s a good chance of reaching potential people and employers.
  5. Win cash prizes – All of this is great but we all crave a reward for our efforts. Isn’t it? Don’t worry, cash prizes worth thousands are waiting for you. Read ahead to learn about all the rewards. 😉

 

I’ll leave you with the words of the great Benjamin Franklin:

“Either write something worth reading or do something worth writing.”

blogathonblogathon winnersdata science blogathondata science writing
Pranav Dar
Pranav Dar

Senior Editor at Analytics Vidhya.Data visualization practitioner who loves reading and delving deeper into the data science and machine learning arts. Always looking for new ways to improve processes using ML and AI.

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Responses From Readers

Clear

Priyanka ks
Priyanka ks

share a list of all submissions in the blogathon.. I am sure there are some interesting reads !

123

Pranav Dar
Pranav Dar

Here you go: https://www.analyticsvidhya.com/blog/tag/blogathon/

123
software company
software company

great

123

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Nikhil Mishra’s Journey to Becoming a Kaggle Grandmaster
Nikhil Mishra’s Journey to Becoming a Kaggle Grandmaster
The Data Science Blogathon is in Full Swing – Participate and Build your Brand!
The Data Science Blogathon is in Full Swing – Participate and Build your Brand!

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