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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

Interview Excerpts with Kaggle Grandmaster Ruchi Bhatia – A Sneak Peek Into Her Triple Grandmaster Journey on Kaggle

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Guest Blog Last Updated : 22 Dec, 2022
7 min read

Introduction

Welcome to a thrilling and impactful conversation with Ruchi Bhatia, where she spills the beans on her Triple Grandmaster Journey on Kaggle. She is also the youngest to be a 3x GM and the 2nd woman ever to attain this title. Before jumping into the interview, let’s get to know Ruchi. She is one of the 29 Kaggle Datasets Grandmasters and currently ranks 3rd with 12 Gold Medals and 15 Silver Medals in 44 of her total Datasets. She is also Kaggle Notebooks and Discussion Grand Master with current rank 61 and 30 respectively.

With a Bachelor of Technology(BTech) degree in Computer Engineering from KJ Somaiya College of Engineering in the year 2020 and a professional repertoire of being an Executive Associate at Colgate-Palmolive, she is also a Data Science Global Ambassador for Z by HP and NVIDIA.

She has been kind enough to be a regular at our Grandmaster series interviews in the past. You can catch them here.

Interview with Ruchi Bhatia

Interview Excerpts with Ruchi Bhatia

AV: Ruchi, you have inspired Gen Zs! You have become a 3X Kaggle Grandmaster and are the youngest person and 2nd woman to attain this title in the Data Science Industry. How do you feel about this accomplishment? 

RB: I am absolutely thrilled about this accomplishment. It is a testament to my dedication and determination to hone my Data Science skills. I have had the pleasure of working with some truly talented and inspiring individuals along the way as well.

 

AV: How do you manage to maintain a balance between finding time to share your work on Kaggle, your Master’s degree program at CMU, your Data Science ventures, and your personal life? Most of us are surprised by your utmost discipline and dedication in life. Please shed some light on this aspect, especially for women who still have some reservations with respect to the art of balancing work and personal life. 

RB: I definitely faced a number of challenges along the way to this breakthrough, including trying to balance my full-time Masters’ program at Carnegie Mellon University with my passion for Data Science. Managing my time effectively was crucial, as I had to make sure I had enough time to complete my coursework and contribute on Kaggle. As I participated in new Kaggle competitions, I needed to also brush up on the latest developments in the field by reading a lot of research papers.

However, I never let these challenges get in the way of my motivation to learn and grow as a Data Scientist. I actively sought out opportunities to improve my skills and network with others in the industry, and I made sure to stay proactive and focused on my goals. 

I believe that the key to overcoming challenges in any field is to stay motivated and continue learning and growing as a professional. With the right mindset and a willingness to put in the work, you can achieve anything you set your mind to. So, never give up on your dreams!

 

AV: Data Science is still at a nascent stage, and not many women are part of this industry. Please share some career advice for girls who are planning to start their careers or want to transition into this field.

RB: First and foremost, it is essential to continuously learn and develop your skills. The field of Data Science is constantly evolving, and it is essential to stay up-to-date with the latest tools and techniques. You could even consider getting a degree in a related field to ensure you have the skills you need to succeed.

Networking and building relationships are also crucial in Data Science. By joining professional organizations and attending industry events, you can connect with other professionals and learn about potential job opportunities. Don’t be afraid to ask for help when you need it – consider seeking guidance from experienced professionals or finding a mentor to help you navigate the field.

Finally, be persistent and don’t give up. Breaking into any new field can be challenging, and Data Science is no exception. However, with dedication, you can build a rewarding career in this field. So don’t let setbacks or challenges deter you – stay focused on your goals and keep working towards them.

 

AV: Do you recall when you decided to be part of the Data Science and Analytics world? Did you receive any guidance from people?

RB: Data Science can play a massive role in changing the present. It is a rapidly evolving field with a lot of scope for research to address the day-to-day challenges people face. I’ve always been interested in the intricacies of languages, and being multilingual has added to my inclination toward Natural Language Processing and its powerful applications today. This is what influenced me to pick Data Science as my career choice.

Mentors are undoubtedly crucial for everyone to gain a sense of direction, be it in any domain. We need a bubble of positivity and just the right amount of encouragement to reach our goals.

My mother has played a huge role in encouraging me to pursue my dreams right from the early years of my life when I dreamed of becoming a Computer Engineer, to this day, in a field where we’re constantly faced with new challenges to solve!

 

AV: Which women in DS inspire you?

RB: Daphne Kohler because of her ground-breaking contributions in the Data Science and Machine Learning space and co-founding Coursera, a platform I’m sure most of us have turned to.

I’ve earned a lot from her Probabilistic Graphical Models course on Coursera. She has phenomenal knowledge and an excellent grasp of concepts which she passes on to us.

 

AV: You’re currently a Data Science Global Ambassador at HP and the Leader of Data Science at OpenMined. These are prestigious positions. Would you like to share some of your learnings along the way?

RB: As a Data Science Global Ambassador at HP, I have been fortunate enough to have access to local GPUs that have greatly enhanced my Data Science workflows. These resources allow me to run a large number of experiments concurrently without any time constraints, which is especially valuable as data size continues to increase and cost and time become critical considerations. The high-quality equipment I have been provided has significantly increased my productivity and efficiency by enabling me to run more experiments and save time. Additionally, it allowed me to expand my professional network immensely and provided me the opportunity to work with some of the best minds in the field! It’s also an enabler for keeping me motivated to actively contribute to the community.

Through OpenMined, I was able to dive into the world of Private AI, and I worked on privacy-preserving machine learning and artificial intelligence technologies. Last year, our team also collaborated with Twitter to allow third parties to access non-public Twitter data using privacy-preserving technologies, enabling them to investigate topics such as the amplification of political content by Twitter’s recommendation systems without exposing any user’s private data or any of Twitter’s models or intellectual property. All in all, it was a fascinating initiative and a valuable learning experience for me!

 

AV: We would like you to share some tips and tricks that have worked for you in the past. With people who are aspiring to build a career in the Data Science Industry. 

RB: Some tips and tricks that worked for me in the past were: 

Finding my area of interest – This helped me to stay motivated and persevere through the challenges I faced as a beginner.

Making reading a regular part of my routine – Setting small targets helped me a lot, whether it was reading a certain number of blogs or research papers each day or week.

Staying up to date on technology trends by reading relevant sources, such as the MIT Technology Review – Keeping track of articles I’ve enjoyed reading by creating a personal document sorted by category. This helped me whenever I wanted to refer back to something in the future and saved me plenty of time.

Project-based learning – I preferred a more hands-on approach to learning important concepts.

 

AV: I believe, Kaggle has been a backbone to achieving this milestone in your life. Please share how it helped you to strengthen your portfolio.

RB: I attribute my success to a combination of consistency, patience, and curiosity, which are essential qualities for anyone looking to achieve their goals. I am grateful to both Kaggle and Z by HP for providing me with the resources and opportunities to develop these qualities and put them into practice.

Kaggle has been particularly instrumental in my journey. It has provided me with a safe and supportive space for exploring and learning about a wide range of concepts and techniques that cannot be found in traditional courses. By focusing on project-based learning after joining Kaggle, I have been able to apply my knowledge and skills in a real-world context rather than just learning about a subject in a theoretical or abstract way.

 

AV: As we close this conversation, We would love to hear some words of inspiration for ‘Women in Data Science’ from you. 

RB: Imposter syndrome is a common experience among beginners, especially with the prevalence of social media highlighting the successes of others. It’s easy to fall into the trap of comparing ourselves to others and forming an idea of what success should look like based on what others have achieved. But the truth is, we all have our own unique stories and motivations that have brought us to where we are today, and that’s what makes us each capable of pursuing our passions and goals.

Don’t be afraid to speak up and share your thoughts and ideas. Your opinion is just as valuable as anyone else’s, and it’s important to let your merit and abilities define you.

Remember to cultivate a positive, growth mindset and tell yourself that you can achieve anything you set your mind to. Don’t let fear or self-doubt hold you back from pursuing your dreams and reaching your full potential.

 

Conclusion

Parting ways, I can’t help but reiterate the fact that gender has got nothing to do if you have the grit and zeal to make it big in your niche. All you need is perseverance and a deep-rooted passion for your subject, and the sky’s the limit! We would love to hear your thoughts and learnings from this interview. Let us know in the comments section below!

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Guest Blog

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