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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

DataHack Radio #16: Kaggle Grandmaster SRK’s Journey and Advice for Data Science Competitions

Pranav Dar
Pranav Dar Last Updated : 13 Jun, 2019
5 min read

 

Introduction

Ladies and gentlemen, presenting Analytics Vidhya’s top community member and Kaggle Grandmaster – Sudalai Rajkumar, aka SRK!

He is an inspiration and enigma in the data science community. I had the pleasure of meeting him at DataHack Summit 2018 and was blown away by his humility and intelligence. I jumped at the first chance of getting him on our DataHack podcast.

Whether it’s his analytical acumen, wealth of experience, or down-to-earth demeanour, we could all learn something from SRK. In this episode, we cover:

  • SRK’s background and his first brush with analytics
  • His transition into data science
  • The advantages of participating in data science competitions
  • How competitions help in a day-to-day industry role
  • Advice to aspiring data scientists from a Kaggle Grandmaster

And much, much more!

I have picked out the highlights of SRK’s discussion with Kunal in this article. Happy listening!

DataHack Radio is now on all the popular podcast platforms. Subscribe today and listen to this and all previous episodes below:

 

SRK’s Background

“Finding patterns in data is something I love to do.”

SRK’s background and journey into data science wasn’t a straightforward one. He completed his B.E. in Mechanical Engineering from PSG College of Engineering in 2010. During his final year there, he received two offers – one from the mechnical field and one from an analytics company.

Remember, this was back when the term “data science” hadn’t been coined yet. It was plain old analytics. After weighing up both offers, SRK went with the analytics option. Working with data and finding patterns piqued his curiosity and that led to his first foray into this field.

But he didn’t start working on an analytics project straight off the bat. His was more of a software engineering role (quite a lot of our community will relate to that!). SRK’s responsibilities included writing codes to put models built by analysts into production. He worked with Python at that time, which, as he admits, was a lucky break in hindsight.

We might be familiar with pandas and numpy nowadays, but back then SRK worked exclusively with native Python functions (lists, tuples, dictionaries, etc.).

 

Transition from Software Engineering to Data Science

A year in the job is enough to ask the question – where do I go from here? Most of us have been there and SRK faced up to it in 2011. He got a switch within the same organization and moved into an analytics role.

Which tools were considered state-of-the-art at that point? The experienced readers among you will recall that Python wasn’t close to what it is now. Quite a lot of the packages we use these days did not exist eight years ago. Instead, R was primarily used to perform the exploratory analysis and build models. Of course, SAS also had a big market share. Deployment and production was done in Python. A neat segregation of tasks, right?

During his role transition, SRK worked with R for the first 1 year or so. He then switched to Python for the modeling part of his job as well.

His work was majorly in the financial domain at that point. He worked with different kinds of credit risk models, weight risk models, marketing models, etc. Initially his team worked with linear and logistic regression models. But the focus slowly shifted to more advanced machine learning techniques, like random forest and GBMs.

This was the period when deep learning had started to penetrate into mainstream applications. Given his penchant for patterns and trying new things, SRK started experimenting with a few deep learning models using libraries like Theano and PyBrain.

 

Participating in Data Science Competitions

During his first year in the analytics project, SRK started reading and learning about analytics outside his working environment. He’s candid enough to admit that at one point, he thought analytics was only about linear and logistic regression! 🙂 Goes to show how big a role self-learning plays in our life.

He was exposed to the world of machine learning algorithms during his learning phase. But his day-to-day role didn’t involve anything outside building linear models. That’s when SRK discovered the usefulness of data science competitions.

Participating in these competitions was far more difficult then as compared to now. There were no blogs or MOOCs teaching one how to crack competitions. SRK relied on a library’s official documentation page and his own analytical acumen to figure out the way forward. It did take a lot of time to experiment and learn but it helped him immensely in his learning.

But here’s where the best are separated from the rest – SRK spent, on average, 3-5 hours every day after work to learn new concepts or brush up old ones. That is the level of hard work we need to put in to succeed in this field.

 

Does Participating in Data Science Competitions Help in an Industry Role?

We’ve heard this question come up plenty of times. Who better to answer it than a Kaggle Grandmaster?

SRK’s first role in analytics was in the R&D side of things. So he had leeway to experiment with new algorithms. The knowledge and experience he gained through data science competitions came in handy for him here. Instead of learning on the job, it became more about experimenting on the job. Something we all can strive for more!

Admittedly, not all of us will get the chance to play around with new algorithms in our day-to-day roles. But the learning we gain through these hackathons is invaluable. It isn’t limited to just applying new techniques. Critical thinking, brainstorming, working with numbers (data intuition), structured thinking, ability to experiment – these are characteristics that will help you in making business decisions. You might not see it happening straightaway but patience is key to success, especially in data science.

 

The Secret Behind Making the First Submission within Hours of a Competition Launch

This has always intrigued me. Whenever a new competition page goes up on Analytics Vidhya or Kaggle, SRK usually has his first submission ready within a few hours. How is that even possible?

As it turns out, most folks who regularly participate in these competitions have a generic code base ready before the dataset is released. They just modify the code based on the problem statement. The hyperparameter tuning and all other experiments are done post that.

 

Advice to Aspiring Data Scientists

  • Get Practical Experience: Knowing the theory and intuition behind algorithms is good, but getting hands-on practical experience is where the goldmine lies. Try to get your hands on a real-world dataset. See what you can wring out of it. This will be invaluable when you are sitting in an interview setting
  • Participate in Hackathons: Participating in competitions helps you understand where you stand among the community
  • Try to Frame a Problem Statement on your own: I really liked this advice. We don’t get industry experience in a hackathon setting. Instead, you can try to come up with a problem that you feel might help someone. Then build on that by collecting data around it. Solve the problem and showcase it via blogs or GitHub. It’s hard work but that translates to results sooner than you might expect
  • Pick a Domain: This is critical. So many aspiring data scientists aimlessly wander about applying to jobs in domains where they don’t hold any experience (or even interest). Pick a domain that is of interest to you and try to find datasets to work on

 

End Notes

Acing data science competitions is a tough proposition. I have struggled to crack the top 10 in any popular competition I’ve taken part in. SRK gave us a lot of practical advice in this episode which should help you streamline your learning process.

What was your favorite part about this episode? And if you’ve ever had the fortune of meeting SRK, I would love to know your experience.

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

Amit Kumar
Amit Kumar

This was great pranav , it was just like FM radio interview Amazing interview with Mr Suddalai Rajkumar and his story to success. I am inspired by him and your work.

123

Pranav Dar
Pranav Dar

Thanks for the kind words, Amit! And you're right, SRK is an inspiration - it was a pleasure meeting him at DataHack Summit.

123
praneeth
praneeth

It's always inspiring to hear from @SRK. I got a chance to meet him in one of the H2O meetups.

123

Abhishek
Abhishek

For Aspiring data scientist like me. You did a great job.😊 Thank you for inspiration.

123

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