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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

Kavana Venkatesh’s AI Journey from Dreams to Data

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Guest Blog Last Updated : 13 Mar, 2024
4 min read

At Analytics Vidhya, we are dedicating March to celebrate women in AI. Join us as we highlight the remarkable journey of Kavana Venkatesh, a trailblazer in the field. Stay tuned for her inspiring story and more empowering narratives throughout the month!

Let’s look at her story of inspiration! 

Kavana

Kavana Venkatesh Story in her own Words

Growing up in a small town in India, my family surrounded me, championing the virtues of confidence, learning, and integrity. My mother, a retired headmistress whose resilience and kindness knew no bounds, and my sister who is unapologetically herself were my role model.

Her dedication to community development and her unwavering support inspired me to dream big, right from my childhood. My dad, who I recently lost, taught me to always be true to myself and to never shy away from voicing my opinions. I imbibed his infectious love for the people around him and for the self.

As a high achiever, I always felt a strong sense of purpose, fueled by the mentorship and encouragement from teachers, well-wishers, and even kind strangers. This support system was my backbone, pushing me to explore and reach heights beyond my imagination.

I was hungry for knowledge, self-awareness, success and I always grabbed all the opportunities in my capacity with gratitude and hope. So, at 5, I wrote my first poem, at 9 I was an All-India-Radio artist, and at 14, I published my first book.

I wanted to be a writer, a singer, a lawyer, a doctor and so many other things. Computer Science was never on my list, but destiny had its own plan for me.

Over-Coming Challenges

When I decided to venture into data science and pursue my master’s in the U.S., others reacted with mixed emotions to my ambitions. Concerns about being “overqualified” or “too independent” surfaced, echoing the traditional societal fears that an accomplished woman might struggle to find personal happiness.

My demeanor was often misinterpreted – my politeness seen as a weakness and my assertiveness as bossiness. Even my ambition to nourish my growth in my career was questioned. I was told I was too young to be mentoring or asking for promotions.

These little experiences underscored a harsh reality: being a woman comes with its unique set of challenges, often overlooked or underestimated by others. Yet, these very challenges have shaped my resolve and my approach to my career in AI and life. In the realm of technology, I have observed the distinctive and crucial role women play.

Our willingness to go the extra mile, our relentless pursuit of our dreams, and our ability to lead and inspire are just a few attributes that define women in tech. Despite our passion and hard work, we often find ourselves having to prove our worth, fighting against the tide of societal expectations and biases.

Empowering Positivity

Amidst these trials, however, I have also encountered incredible individuals who have supported and believed in me. Their encouragement has been a testament to the power of community and mentorship in overcoming obstacles and achieving one’s goals. Today, as an ambassador for Women in Data Science Worldwide, I am part of an empowering global community.

This role allows me to contribute to a field that is not just transforming industries but also society at large. The advent of AI presents an exciting era of innovation and possibility, and I am thrilled to be part of this journey, contributing to a future where technology is developed responsibly and inclusively.

Insights from Experiences

My experiences have taught me that there is no predefined box to fit into. As a woman in data science, I commit to continuous growth, both professionally and personally, eager to support and uplift other women in the field. I am determined to help build a future where women are encouraged to chase their dreams, regardless of their size or scope.

Through this journey, I have learned the importance of staying true to oneself, as my dad always showed me by example, embracing every challenge as an opportunity for growth, and the profound impact of supporting one another. My story is a message to every woman out there: Your dreams are valid, your voice is important, and your contributions are invaluable.

Together, we can pave the way for a more equitable, diverse, and innovative future. On this International Women’s Day, I could not be prouder and more hopeful of the incredible way women keep breaking barriers to run after their dreams and I cannot wait to grow under the shadow of innumerable super women in every realm of life. Thank you for inspiring!

About Kavana Venkatesh

Kavana Venkatesh, a data scientist in Boston, MA, embodies resilience and determination. Inspired by family values, she pursued a diverse path from writing to data science. Despite societal skepticism, she persevered, becoming an ambassador for Women in Data Science Worldwide. Her story speaks of embracing challenges, supporting others, and paving the way for an equitable, innovative tech future. On this International Women’s Day, Kavana stands proud, alongside countless superwomen breaking barriers and inspiring others to chase their dreams.

Click on her LinkedIn profile here.

Summing-up

Kavana Venkatesh’s journey is a testament to resilience and determination. Her story inspires us to embrace our passions, break barriers, and empower 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.

G
Guest Blog

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