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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 Data Science Blogathon is in Full Swing – Participate and Build your Brand!

Ram Dewani
Ram Dewani Last Updated : 05 Oct, 2020
4 min read

The Power of the Written Word + Machine Learning Knowledge = Data Science Blogathon

What if you could turn your machine learning knowledge into a superpower?

Imagine you’re given the opposrunity to put all your machine learning knowledge to the test by displaying your skillset in the form of the written word? And that you would stand a chance to showcase your work to a community of 500,000+ data scientists?

That sounds too good to be true, right?

Well, that’s the Data Science Blogathon for you!

Analytics Vidhya has always been at the forefront of knowledge sharing, and we want to continue this trend among our community members. The Data Science Blogathon is an initiative to give the writing power over to you, our community, and let you share your thoughts about this wonderful field.

The Data Science Blogathon is the ultimate competition that combines your writing prowess with your machine learning skills! The competition is already live and finishes on October 11th at 11.59pm. You can check out the leaderboard here.

 

So, What is the Data Science Blogathon?

The best way to learn any concept, especially in data science, is by writing about it. That not only helps you understand what you learned in more detail but sharing it with the community helps others understand how a particular data science idea works.

We will not only publish the best articles on Analytics Vidhya’s blog 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.

 

Why you Should Participate in the Blogathon

You might be wondering – what is in it for you to write a data science blog? Well, there are plenty of benefits! Let’s discuss some of them:

  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 opportunity 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 for a reward for our efforts. Isn’t it? Don’t worry, cash prize worth thousands are waiting for you. Read ahead to learn about all the rewards. 😉

 

How do I Participate in the Data Science Blogathon?

To enter the competition, just head over here and press the register button above. All the details, including where to write and how to submit your article, are included there.

If you face any issues or have any questions, use the discuss tab on the above link to reach out to us.

Note: We have received a few emails regarding uploading images on the writing platform. If you face any such issues, just upload your images to a Google Drive folder, name the images as ‘Image 1’, ‘Image 2’, etc. and mention in the article where you want to place the images. We’ll take care of that for you.


What are the Prizes on Offer?

Every article that meets Analytics Vidhya’s standards will be published on the Analytics Vidhya blog. This offers you a one-of-a-kind opportunity to reach out to a HUGE audience and embellish your data science profile and your own brand.

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 5000 views, you get INR 1,000 as a bonus, regardless of whether you fall in the above three categories!

 

When Will the Winners be Declared?

The winners for all 3 categories will be declared on October 20, 2020.
Even though you need to submit your article by 11.59 pm on October 11th, the views for your article will count until October 19, 11.59 pm. So keep sharing your article after it’s been published to increase your chances of winning!
So head over to the Data Science Blogathon page and start writing!
Ram Dewani
Ram Dewani

Product Growth Analyst at Analytics Vidhya. I'm always curious to deep dive into data, process it, polish it so as to create value. My interest lies in the field of marketing analytics.

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