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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

Rose Missier’s Journey: From Humble Beginnings to Data Science Leadership

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Guest Blog Last Updated : 19 Mar, 2024
4 min read

Join us as we celebrate women in AI this March on Analytics Vidhya! Excited to start our series with Rose Missier, an AI visionary with an impressive journey and achievements. Stay tuned to learn about her trailblazing work and impactful contributions in the field of data science.

Let’s look at her story of inspiration! 

Rose Missier

Rose Missier’s Story in her own Words

From the sun-kissed lands of South India, where the whirring of computers and the vast expanse of the internet seemed like distant dreams, my journey into the world of technology began. Unlike the digitally saturated landscape of today’s youth, my early years were characterized by a scarcity of exposure to computers and the internet. Yet, fueled by an insatiable curiosity and a relentless drive for knowledge, I embarked on a journey that would lead me to unexpected heights despite having a rocky road ahead of me. With perseverance and dedication, I carved a path that led me to numerous academic accolades, utilizing every opportunity that came my way to the fullest.

Early Professional Years

Like many others, I commenced my professional journey as a Software Engineer in Java, delving into the realms of desktop and web applications. It was a realm I found solace in, relishing the challenges it presented. However, life took a turn when I had to relocate to Australia. Determined to continue my pursuit of knowledge, I delved into data during the pre-AI era as an international student.

Challenges and Resilience

The road was challenging; juggling long campus nights, work, and studies while establishing myself as a woman of color in STEM abroad. I encountered experiences that threatened to undermine my confidence and hinder my progress. Yet, with unwavering determination and resilience, I refused to be deterred by the obstacles in my path. Through academic excellence, scholarships, and relentless dedication, I managed to pave my way back into the Software Engineering industry in Australia, albeit at a lower position than before but in an interesting field. And in hindsight, it proved to be a blessing in disguise, opening doors to a plethora of opportunities.

Delving into Data

My foray into data began with search engine optimization, a domain I found immense passion in. This paved the way for me to delve deeper, exploring CMU Speech Recognition kits, where I undertook the task of building Italian speech recognition for translation applications. Despite the seemingly disconnected nature of these projects, each served as a stepping stone towards my next endeavor – Gesture recognition using Kinect. The diversity in my experiences culminated in a role that bridged Software Engineering and Product Development in a startup Samvit, with a primary focus on Optical Character Recognition (OCR).

Transition to Enterprises

This stint proved to be invaluable, exposing me to a myriad of technical and non-technical challenges, shaping me into a well-rounded professional. From there, I transitioned into working with large enterprises, where I immersed myself in the wealth of data-centric experiences, encompassing product analytics, datalakes, AWS, and more. I further supplemented this by pursuing postgraduate studies in Data Science, equipping myself with the necessary knowledge and skills to navigate the ever-evolving technology landscape.

Sharing Knowledge and Impact

With a wealth of experience under my belt, I felt compelled to share my knowledge and insights with others. This led me to engage in teaching roles at various universities and speaking engagements at global conferences. However, it was a pivotal moment when individuals began reaching out to me for guidance on transitioning into the field of data science that I realized my calling lay beyond mere dissemination of knowledge. Thus, I embarked on a journey to broaden my impact, starting with writing on platforms like Medium, culminating in the founding of Cocoabytes, a publication aimed at fostering dialogue and learning in the tech community.

Current Roles and Initiatives

As Director at Samvit and Product at AI startup 6clicks, I lead an AI initiative at the intersection of art and AI. Through my endeavors, I aim to push technology boundaries and inspire lifelong learning. Embracing opportunities is key to growth and success.

Reflecting on the Journey

In retrospect, my journey into data science has been a testament to the power of perseverance, adaptability, and a willingness to embrace the unknown. My advice to aspiring professionals echoes this sentiment – seize the best in every opportunity, whether good or bad, as each experience is a stepping stone towards personal and professional growth. Never underestimate the importance of continuous learning, and always remain open to the guidance of mentors who can illuminate the path ahead. After all, it is not the destination that defines us, but the journey we undertake to reach it.

About Rose Missier

Rose Missier brings diverse software product creation experience from large enterprises to start-ups across India, ANZ, USA, Canada, and EMEA. She fosters collaboration at Samvit Software Solutions, ensuring successful product delivery and customer satisfaction. Rose Missier’s passion for Computer Science drives her to teach at universities, write visual blogs on Medium, and speak at conferences, including founding the CocoaBytes publication. She spends her free time trying to marry Art, Machine learning, and Product.

Connect with Rose on Linkedin, Medium and Follow CocoaBytes

Summing-up

Rose Missier’s journey exemplifies resilience, learning passion, and challenge embrace from Software Engineer to Director at Samvit Software Solutions. Through her diverse experiences, including bridging Software Engineering and Product Development, she emphasizes continuous learning and sharing knowledge. Currently spearheading AI initiatives at 6clicks, Rose aims to inspire others in embracing technology’s potential. Connect with her on Linkedin and Medium for insights into the intersection of art, machine learning, and product development.

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.

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Guest Blog

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