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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

Improvising Hackathon platform, Blogathon, Profile pages, Points and much more

Kunal Jain
Kunal Jain Last Updated : 25 Nov, 2016
8 min read

Continuous improvement is better than delayed perfection!

-Mark Twain

I can’t stop smiling and deeply appreciating this statement from Mark Twain. He has summarized the life of an entrepreneur in a statement as simple as this!

Some time mid last year, we decided to conduct an online data science competition for our community. The only experience we had was by conducting a few offline hackathons and Kaggle competitions we had participated in the past. There were a lot of uncertainties attached to the execution of an online data science competition. We faced several questions – how to distribute data? How to decide the winner?

We decided to keep things simple and functional. This is how the first hackathon screen looked after the overnight hack I had done:

megastar

I am sure the designers and UI / UX specialists out there would discern at this screen, but there was some thing magical about it. It just did what it was supposed to – tell people the accuracy of there solutions through a simple interface. The simplicity and responsiveness of platform clicked with the users and we had close to thousand submissions over a 2 day period!

We haven’t looked back since! This is how the platform looks today.

 

hacka_updated

I am glad to announce our First Update of Hackathon Platform for the year. It has come after some toil at our end. But it has come in good time – just before last man standing. And this particular update is very close to my heart. Why?

The short answer is that this unifies everything we do at Analytics Vidhya. I’ll come back to this again. For now, let’s look at the list of updates which are added in this version.

 

New features of our DataHack platform:

All right! Ahem! Mike testing – Hello 1, 2, 3! Yep – this is working.

Now that I am at the center of this stage, let me reveal the updates briefly!

  1. Functionality to participate in teams in AV hackathons: The biggest center point of our hackathons is the instant learning which happens through participating. We believe that this learning will increase multi-fold when you participate in a team. Participating in teams will not only enable you to learn on your own, but also enable you to brainstorm with a peer group.
  2. Badges for activities on Analytics Vidhya: Our discussion portals used to provide badges on desirable activities from people. We just took this a couple of steps forward – now people get badges for hackathons and for blogging as well.
  3. Point system for activities on Analytics Vidhya: Whether you have written a good answer or an awesome blog or have just finished a hackathon, there are points which you get for all these activities. You can consider these points as a skill score – the more the better.
  4. Rankings based on points: All your points end up contributing to your rank on Analytics Vidhya. Goes with out saying – the more your points, the better your ranking.
  5. Profile pages – Now, each user has a dedicated profile page on Analytics Vidhya. You can use this to showcase your skills and your contributions to the larger world.
  6. Competitions of different formats – Data Science is not about solving a single kind of problems – different roles in the industry requires different skills. In order to make sure we address this point, we will have several kinds of competitions on the platform. This includes blogathons, MCQs, assessment tests, multi round competitions and many more. You will see the flavor of these in coming days.

 

Let us look at details of each of these new features:

 

1. Functionality to participate in teams in AV hackathons

This was definitely one of the most awaited feature on our hackathon platform. We had received several requests for enabling this and now we have this feature in the platform.

How does this help you?

When I look back at the projects where I had the highest amount of learning in data science, most of them happened to be in team. We were a team of analysts fighting out a business problem or the other with tight deadline. And this is what we have simulated on our platform.

If you believe you learnt a lot in any of the past hackathon – wait and watch what a team can do for you. The math is very simple – your prize splits and your learning / chances of winning increase multi-fold. You need to select the right partner, of course.

A few specifics about participating as a team:

  • Once you have formed a team during a hackathon, it can’t be undone. So, if you are forming a team, make sure you have thought about it. You (and we) will not be able to reverse this process.
  • You can merge teams, if the sponsor of the hackathon has allowed so. The final team leader would be the team leader of the team sending the merge request.
  • You can only send invites to people who are eligible for team formation. Here are the rules which define the eligibility:
    • The participant should have registered for the hackathon.
    • The person should not have formed a team with any one else.
    • You can only send invites to a few people above you in rank and any one below you in rank. If you want to team up with some one above you outside your reach, try and get them to send you an invite.
  • When you participate in a team, your points as a result of this hackathon will get divided.

Wow – I can’t wait to see teams in action during our upcoming hackathon.

 

2. Badges for activities on Analytics Vidhya

Badges will be provided for all activities on Analytics Vidhya – starting from blogging, discussion portals and hackathons. Here is the complete list of badges for each of these:

  1. Blog – Nice Blog, Good Blog and Great BlogBlog_badges
  2. Discussion – You can view the entire set of badges here
  3. Hackathon – Winner, Top 3, Top 10, Top 25Hackathon_badges

Over time, the best badges would be able to define your profile in a crisp manner.

 

3. Point system for activities on Analytics Vidhya

While badges define your AV profile at a macro level, points is what differentiates the best among the good ones. Points are meant to push ourselves to achieve more than what we would normally do.

Here is how the point system is defined currently:

  • Each like on discussion portal gets 10 points
  • Each blog published on Analytics Vidhya gets 100 points
  • Participants of a hackathon get points as per following formula:
Points = ( (Total Points / SQRT(Number of TeamMates) / (log(Rank)+1)) * (Percentile ^ 4)

where,

Total Points: The numbers of points allocated to a contest. This can vary depending on the contest. The bigger the stage, higher the points. All signature hackathons till now carry 1,000 points each.
Number of TeamMates: The number of members in a team
Rank: Your team final standing on private leader board
Percentile: Your team relative position to other participants

Note: As of now, the maximum number of teammates allowed in a team are 2.

 

4. Rankings based on points

  • You can access the rankings of people by visiting this page
  • The top profiles will also be displayed across all blog pages and hackathon pages.
  • Over time people with higher rankings will have different privilages on the site.

Here is how rankings page looks on our most visited page:

top_users

5. Profile pages

Profile Pages have been carefully designed to reveal the true credibility of a participant. This includes rank, badges earned, points, last activity, hackathon participation, likes etc. In this update, the focus has been kept more on presenting important metrics of a participant. We will continue to evolve this to make it more useful for you.

Currently, this is how the profile page looks:

popo

 

6. Competitions of different formats

To ensure a thorough learning, we have introduced various forms of competition suited for people of different competitive level. For example, if you are a beginner, you might find hackathons intimidating, instead you can participate in practice problems. Needless to say, the advanced user would love to test their water in the real battlefield. In addition, we have MCQ challenges, assessment test, blogathon to keep you engaged.

 

How do these changes sound? If you are thinking this was a lot of work in small time – we bet! If you have feedback, thoughts, suggestions – we are all ears. Drop a comment, reach out to us, post it on our FB page, tweet us and we will respond. If you like love the changes we have made – leave a high five in the comments below.

 

Above all, Thank You So Much Avians!

While I have done most of the talking writing, I wanted to present stories and testimonials from some of the users. It gives us immense satisfaction and deep pleasure that we are able to impact more and more people across the globe every day.

This journey would not have been possible with out support of our community, the team at Analytics Vidhya, our mentors, families and friends. I can’t thank them enough and I promise that the fun has just begun!

With out much delay here is what some AVians had to say about Analytics Vidhya:

Sudalai Rajkumarsrk

AV is one of the best knowledge portal for Data Science I know about and certainly the best in India. It caters to the need of all sorts of people, from beginners to well experienced professionals. The articles are in-depth in nature and cover wide-variety as well which is very difficult to find else where. Discuss portals help people to clarify their doubts while hackathons give a flavor of real world experience. Thank you AV and keep up the good work.!

 

Vikashvikash

AV is the best community driven DS group out there.
Whether its data-hacks, meetups or forums, the community is always there to help.
Have learnt a lot being an AVian. Thanks guys 🙂

 

 

Kumar Abhijeetabhi

The blog posts are very informative, the community is very supportive and the regular hackathons are arguably the best short-term data science competitions on the internet. I recommend Analytics Vidhya to everyone interested in data scienceThe blog posts are very informative, the community is very supportive and the regular hackathons are arguably the best short-term data science competitions on the internet. I recommend Analytics Vidhya to everyone interested in data science

Get Involved!

What are you waiting for? There hasn’t been a better time in history to be a data science professional. We believe, there hasn’t been a knowledge platform better than Analytics Vidhya to learn. And remember – the journey has just begun!

Come, join the community. Register for our signature hackathon – last man standing, blogathon or participate in the practice problems out there.

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page.

Kunal Jain
Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their Al ambitions through his visionary approach to Al education and community building. Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, Al, and fostering a thriving community of data science professionals.

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Responses From Readers

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Shuvayan Das
Shuvayan Das

High Five..Way to go AV!!

123

Muthu
Muthu

Hi-fi, Loved the features. Great going AV!!!

123

HighSpirits
HighSpirits

Hi-Five....nice to see our portal growing ..

123

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