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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

Vidhya Chandrasekaran’s Journey from Kitchen to Google

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Guest Blog Last Updated : 29 Mar, 2024
3 min read

At Analytics Vidhya, we’re celebrating the Women of Data Science by highlighting their remarkable journeys and achievements on our blog throughout March. We believe their stories can inspire, uplift, and empower others. Today, we have the privilege of featuring Vidhya Chandrasekaran.

Let’s delve into her inspiring story!

Vidhya Chandrasekaran’s Journey in her own Words

I started my career that was associated with extreme responsibility but paid extraordinary dividends right after my under graduation. Yes, I started as a home-maker and took care of my family and my kids full time.  While I relished my experience as a home maker, in parallel I also started preparing for my next avatar by doing my masters in parallel and keeping myself updated with the ever growing tech world by doing tech courses and training.

Finding Passion in Data

After a few years, I found a job that gave me an entry and I did not look back after that.  Started in Database programming and transitioned later to a database architect my experience with Data gave me an immense interest in Data science. The organizations I was associated with and the leaders I worked with enabled me to become a Leader in the space of Machine Learning.

Reflections on a Rewarding Path

Looking back at my career journey as a woman I am humbled with the opportunities I was presented with and also recall Learnings along the way.   Women leaders can have a transformative impact on organizations and society. They can bring different perspectives, decision-making styles, and leadership behaviors, contributing to diversity of thought, innovation, and resilience. 

The Keys to my Success

Here are the top 5 things that have helped my journey 

  • Curiosity and Continuous Learning: While it is hard work to learn continuously, it has always given me new perspective and has helped me stay abreast of industry trends, and embrace diverse ideas
  • Growing People: Investing time on Developing   people around us and not restricted to ones own team alone has always given me utmost satisfaction and fulfillment.  Women leaders need to leverage on this skill that comes very naturally to them
  • Mentors and Sponsors: Sponsors and mentors are crucial for women leaders because they provide guidance, support, and opportunities for advancement in male-dominated environments. They offer valuable advice, help navigate challenges, and advocate for their proteges, aiding in skill development and career progression. Good Managers can be great mentors as well as they know the context more than others.  In my case most of my managers have been mentors as well 
  • Risk Taking: Successes in my career is directly proportional to the risks that I had taken along the way.  All or most of the time opportunities come masked as challenges and taking calculated risks on taking them upon is crucial for moving ahead.  By confronting challenges head-on, leaders demonstrate courage and determination, inspiring others to persevere in the face of adversity. Moreover, overcoming obstacles and learning from failures builds valuable experience and skills, equipping leaders with the confidence and insight to navigate future uncertainties

Learnings from her Experience

Looking back at my career journey if I have start over again, I would first on the following things in the same order

  • Imposter Syndrome: Women leaders including myself have awaited until we tick all the boxes where in most case opportunities would have moved on.  
  • Your Work would not talk for itself: Marketing ones work is as important as the work itself. Even excellent work can go unnoticed if it’s not adequately communicated or promoted.  Especially if you are a leader this is even more critical
  • Navigating organizational politics: Most women may be less inclined to participate in organisational political activities Organizational politics are often an unavoidable aspect of working in any organization. Therefore, it’s essential to develop the skills necessary to navigate these dynamics effectively including building relationships, understanding power structures, and advocating for one’s interests while also maintaining integrity and ethical standards. 

I recently joined Google to lead Software Engineering teams.  In my previous role with PayPal, I managed  AI product Management and ML Science teams to lead AI personalization for PayPal Merchants.  

About Vidhya Chandrasekaran

Vidhya Chandrasekaran, a seasoned leader in Data, ML, and Business, boasts 19 years of experience in Data and 8 in Leadership. Known for spearheading change, she excels in building high-performance teams and driving ML solutions. Leading Software Engineering teams at Google, she remains a lifelong student and leader.

Connect with her on LinkedIn.

Summing-up

Vidhya Chandrasekaran’s journey exemplifies resilience, dedication, and leadership in the realm of data science. Her story is an inspiration for aspiring professionals, showcasing the transformative power of continuous learning, mentorship, and embracing challenges. Vidhya’s impact resonates not only in her achievements but also in her commitment to empowering others.

You can also share your story or nominate someone who has inspired you by filling out this form. 

Let’s continue to uplift and empower each other in the journey towards a more inclusive and innovative future.

International women's dayVidhya Chandrasekaranwomen in AI
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