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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

The Ultimate Data Science and Machine Learning Blogathon – More than $2500 up for grabs!

Pranav Dar
Pranav Dar Last Updated : 07 May, 2019
3 min read

Introduction

“If you want to change the world, pick up a pen and write.” — Martin Luther

We are delighted to announce the launch of Analytics Vidhya’s Blogathon, the ultimate competition which combines your writing prowess with your machine learning skills!

Analytics Vidhya has always been at the forefront of knowledge sharing, and we want to continue this trend among our community members. We will not only publish the best articles on Analytics Vidhya’s Medium page, but also provide feedback to every writer on their article.

Along with recognition in front of a broad audience, there are lucrative prizes to be won! The focus of the articles can be on any data related field — data science, machine learning, deep learning, Artificial Intelligence, Business Analytics, etc.

The blogathon starts today, 25th August, and will conclude on 23rd September. Beyond this, you will be given an extra week to work on any feedback we have provided on an already submitted article.

We will announce the winners in each category on 30th September.

 

How do I Participate?

To enter the competition, you need to fill in your details here. We will then add you as a writer to our Medium publication and you can start sending your drafts to us. It really is that simple!

 

Lucrative Prizes to be won

Every article which meets Analytics Vidhya’s standards will get published on our Medium page. There are $2500 + bonuses to be won! Prizes will be given in 3 categories:

Most popular article: This will be decided by the number of unique fans the article gets.

  • 1st prize — $500
  • 2nd prize — $250
  • 3rd prize — $150

Most number of articles with more than 10 unique fans: As the name suggests, the more articles you submit and the more fans you gather, the better your chance of grabbing this prize!

  • 1st prize — $500
  • 2nd prize — $250
  • 3rd prize — $150

Editor’s Prize: This category will be judged by AV’s in-house editing team and the winner will receive $500!

There’s more..

The top 25 bloggers will each receive free access for 6 months to AV’s ‘Computer Vision using Deep Learning’ course! It is a one-of-a-kind course that will introduce you to the world of CV and ensure you come out a master of the field.

The top 50 participants will get access to DataHack 2017 day 1 and day 2 talks— a collection of all the sessions that happened in India’s premier analytics conference last year. Hear from business leaders, domain experts, senior data scientists and other eminent personalities in the analytics domain. An unmissable opportunity!

For those articles which do not meet our standards, we will ensure you are provided with feedback on how to make those articles better in the future.

 

Terms & Conditions

  • The content of your article(s) should be unique and not be published anywhere else. Each article will be strictly checked for plagiarism
  • You are free to make as many submissions as you want. But, don’t make multiple submissions of the same article
  • The submissions are already open and will close on 23rd September 2018, 23:59 IST (GMT + 5:30 hrs)
  • We will provide an extra week to work on any feedback we give, provided the article has been submitted before the deadline
  • You are not allowed to re-publish any of your articles elsewhere until and unless discussed with Analytics Vidhya
  • Analytics Vidhya owns the right to disqualify / reject any submission without any explanation
  • The decision for all the awards, as declared by Analytics Vidhya, would be final

 

Sounds too good an opportunity to pass up? That’s the attitude we’re looking for — go ahead and fill in your details here and we will add you as a writer on our Medium publication. Happy writing!

 

blogathondata sciencemachine learningwriting
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

John
John

Excited about the contest and the opportunity to contribute to the zeitgeist. Question about the T&Cs. Does one surrender their rights to the content they publish by virtue of participation, in perpetuity? I plan to ultimately publish the content on a website and I think this point needs to be made crystal. What specifically is being asked in terms of rights to the content?

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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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