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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

Kaggle Grandmaster Series – Exclusive Interview with Kaggle Notebooks Grandmaster Tarun Paparaju (#Rank 25)

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Analytics Vidhya Last Updated : 08 Mar, 2021
4 min read

Welcome back to Kaggle Grandmaster Series!

Today we are thrilled to be joined by Tarun Paparaju to share his journey with us.

Tarun is a Kaggle Notebooks Grandmaster. He ranks 25th in the category and has 18 Gold Medals to his name. Also, he is an Expert in the Kaggle Competitions Category.

Currently, Tarun is a Computer Science undergraduate student at the University of Massachusetts. Despite being this young his notebooks are highly respected in the Kaggle Community.

Note: This is a video interview. We have provided the synopsis below in text. Since this interview was recorded in January, some Kaggle ranking figures differ from what we have mentioned above.

 

Tarun’s Data Science Journey

The following are some highlights from Tarun’s interview. For detailed answers, watch the video.

Kaggle grandmaster series tarun

Analytics Vidhya(AV): What sparked your interest in the field of ML and Research?

Tarun Paparaju(TP): When I was in 7th or 8th grade I was very much into mathematics other than any subject. I would often buy books from outside school and study them. So I was a mathematician before anything else.

The 3-4 years back my father, who is also a Data Scientist introduced me to Kaggle and that was the beginning.

 

AV: What was your motivation behind starting to compete in Kaggle?

TP: He never had a fixed goal while participating in Kaggle. He was in love with the process and was trying to learn each and every aspect of data science, from data collection to model building. There was no goal to reach the top. He was fine even at the bottom 10% of a Kaggle Competition as long as he was able to learn.

He spent his spare time searching for random datasets to practice on.

 

AV: Since you are still a student, a Computer Science Freshman at the University of Massachusetts, how has your education at school and college helped you in your Kaggle competitions or any other Data Science hackathons?

TP: The general route that a normal data scientist takes is – From understanding computer science core to understanding Machine Learning Core Algorithms and, then understanding the maths. He started his journey the other way round.

Tarun feels that the gaps his journey has will be filled by his graduation program.

 

AV: What resources helped you in Studying Machine Learning?

TP: Tarun suggests these 3 resources-

  1. Kaggle

    Kaggle is a great resource not only to practice on random data sets but also to learn from the discussions.

  2. Open Source Contributions and Github

    This is one of the best ways to contribute to open-source projects and get your work checked and optimized by multiple people.

  3. MIT OpenCourseWare

    This is one of the best platforms for free resources in Mathematics which serve as the core towards data science.

 

AV: You are a Kaggle Notebooks Grandmaster and you achieved this title at the age of 17 years and you currently Rank 19th. This is something really amazing? So what were the challenges you faced as a 16-17 y/o during this journey and how did you overcome them.

TR:  The major challenge Tarun faced was the time management pressure brought by his academic schedule. With weekdays in school and weekends in coaching centers, it was really difficult to find time for Kaggle.

What he did was he put all the academics and social expectations put on him behind and kept his hobby at the forefront. He made sure he prioritized Kaggle over anything.

 

AV: What is your procedure for creating a good notebook after selecting a dataset? Is there a checklist of must-do tasks to always perform?

TR: In the first 2 years, even after making any kernels no one was paying heed to it. It was getting too frustrating for him as he was putting all his efforts into it.

Then one day, he posted about his frustration on The Kaggle forum, and to his luck, Kaggle Legend Andrey Lukyanenko replied to him. He said the notebooks should be unique and has to solve problems in a way that the reader wants and not what the content creator perceives. That one reply changed his perspective and he started making better kernels.

 

AV: Since you are an NLP researcher as well, could you name some lesser-known NLP Frameworks which you feel everyone should know?

TP:  Tarun mentions 2 libraries that are lesser-known, but has huge utilities-

  1. Polyglot

    This is one of the best NLP libraries for multiple languages. Does the work of language detection superbly.

  2. Pattern

    Pattern offers a neat API for classic NLP utilities.

 

AV: How do you keep yourself updated with all the rapid advancements being made in the field of deep learning since it is a broad area in itself?

TP: Tarun is pretty motivated about following the right people on Twitter. 99% of the time he says, that majority of the things he wants to know about comes from Twitter only. The benefits of having tightly knit social media are clearly visible if you follow the right people and institutions.

In fact, some of his researches are also inspired by the papers and developments he gets to know on Twitter.

 

End Notes

His thoughts and words are enough to get anyone to begin and stay focused on their data science journey. I hope this edition of the Kaggle Grandmaster Series with Tarun adds value to your data science journey.

This is the 22nd interview in the Kaggle Grandmasters Series. You can read the previous few in the following links-

What did you learn from this interview? Are there other data science leaders you would want us to interview for the Kaggle Grandmaster Series? Let me know in the comments section below!

 

A
Analytics Vidhya

Analytics Vidhya Content team

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