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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 24th Edition

M
Meetu_Arora Last Updated : 09 Sep, 2022
5 min read

Gaining knowledge is the first step to wisdom. Sharing it with the world is the first step to humanity.

 

Introduction

There are plenty of competitions and knowledge-sharing platforms in the sea of data science and technological advancement. However, no one does it quite like us! Well, our numbers say so. Analytics Vidhya is back with yet another edition of the World’s largest data science knowledge competition – Data Science Blogathon 24th Edition. After a successful and insightful streak of our previous Blogathons, we are thrilled to announce the commencement of Blogathon 24th Edition.

Blogathon 24

CLICK HERE TO REGISTER!

The contest is up and running live from today. The most anticipated data science knowledge competition awaits your registration and submission to share with the rest of the world. If you’re a veteran (that’s what we like to call the regular authors), you know the drill already. If you’re a newbie and are eager to make a positive knowledge contribution, we are delighted to have you.

If you missed out on participating in the previous Blogathon editions, here’s your chance to redeem yourself. Put on your wisdom cap and start this satiating and rewarding journey of sharing your data science knowledge with others.

 

Analytics Vidhya – Mission & Story Behind It

While you’ve heard Blogathon and its reviews from fellow community people, let’s take you to the story behind it. The founders of Analytics Vidhya are data science mavericks, and their mission and goal are to make this abundance of knowledge available to the rest of the community.

Blogathon was conceived and spearheaded by Analytics Vidhya with a single focal mission – to bring all data science nerds across the globe under one shared community and to actuate the process of knowledge sharing with everyone aspiring to get into data science.

What initiated a humble effort to start sharing data science knowledge with people has now transpired into a massive community of over 700 authors and a valuable data bank of 4000+ articles. The numbers are growing by the day, and the community is only getting thicker and more credible with each waking moment.

The world of data science and machine learning can get complex and daunting for anyone without adequate guidance and knowledge material. This keeps the fire in us alive to make Blogathon the biggest and most trusted data science professionals’ community that reckons across the globe.

 

Benefits for Authors

If diluting your knowledge to benefit others is what kicks you, Blogathon is your place to be. You might wonder what’s in store if you contribute to the community. Well, apart from the massive pacification, there are some amazing benefits that we have reiterated here for your understanding:

  • Strengthen Your Data Science Knowledge

Knowledge acquired and not shared with the world is what seems a shallow approach. What you’ve learned and understood over the years, slogging into the data science field, should be shared with the community to get benefitted from. Not just it makes you a better professional, but it also strengthens what you’ve learned. Writing about the principles, formulas, and other stuff will only make you the “Subject Matter Expert” in your branch.

  • Solve Your Problem Statements in a Newer Way

The power of storytelling can help you resolve your problem statements in a structured manner. Getting problem statements is one of the most common issues in data science, and Blogathon resolves it beautifully.

  • Level Up Your Professional Portfolio

Recruiters are always on the lookout for conscious professionals who, after acquiring learning, are inclined towards giving it back to society. What better way to do it than Blogathon? Share your knowledge with the world and demonstrate the same on your resume, professional profiles, portfolio, and LinkedIn to get picked up by prolific recruiters.

  • Engage With & Solidify Analytics Vidhya Community

Analytics Vidhya’s Data Science community is all about professionals and authors waiting to do their bit in educating the world. Blogathon gives you a niche platform to participate and engage with the community. You can expect some great conversations with a monthly user base of 2500+.

  • Monetary Rewards, Bonus & Chance to Win an iPad

Although education is priceless, we believe in rewarding educators for their gregarious efforts in sharing knowledge with the community. Expect some lucrative cash rewards, bonus rewards, and a chance to win an iPad every month.

 

How to Get Started with Participation?

The competition starts on 8th September. To enter the competition, just press the register button on the Blogathon page. Once you are registered, gather your thoughts and start writing. Here are a few pointers to make your writing and submission journey smoother:

  • It takes 36-48 hours to review and provide feedback on each article.
  • Once you see ‘Shortlisted’ on the Editor, give it up to 48 hours for the article to reflect on the Analytics Vidhya blog.

There is a detailed video to help you with the registration process and a walk-through of the editor platform to make your writing journey smoother. Do check it out!

 

Topics to Choose for Data Science Blogathon

Here is an exhaustive list of topics to help you set a foot forward on the writing journey at Blogathon. Pick any based on your niche and start penning down your knowledge.

Data Science Blogathon 24 Topics

Out of the above topics, some special topics attract higher rewards over others. Such topics include MLOps, Web 3.0, Blockchain, Interview Questions, and Data Engineering.

Prizes Galore to Reward Our Distinguished Authors

Here’s what each published article in Blogathon gets you:

Data Science Blogathon 24 Prize

Bonus Rewards Based on Views

Data Science Blogathon 24 Bonus Rewards

Points for Writing Guides

Guides are the best format for you if writing resource-specific topics and detailed articles is your forte. These comprehensive, feature-length content formats carry nearly 2500+ words, specifically in Machine Learning, Deep Learning, Data Engineering, etc. Please note that only one guide will be published under every topic.

Guide Topics

Rewards for Top 3 Articles & Guides 

Rewards

Stand a Chance to Win – A Glitzy New iPad

iPad

This must undoubtedly be the most exciting of all rewards – a glitzy new iPad. Yes, you saw it right! An iPad is waiting to be taken home monthly for the ‘Best Author of the Month’. If you want to be that one lucky winner, don’t wait further and register yourself for Data Science Blogathon 24th Edition.

Conclusion

As we said before, knowledge is priceless and sharing it is even more valuable. If you’re one of those who are a data science geek, have bundle knowledge brimming to share with others, and want to be rewarded lucratively, Blogathon is the platform for you. Upskill, share, earn, and grow to be a better professional and an even better human!

Would you like to read the blogs that our lovely authors contribute? Go check it out!

DATA SCIENCE BLOGATHON 24 IS LIVE NOW! REGISTER!

M
Meetu_Arora

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