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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

Financial Crime, Content Creation, and AI Trends with Danny Butvinik

Nitika Sharma
Nitika Sharma Last Updated : 27 Dec, 2023
4 min read

In this episode of Leading with Data, Danny Butvinik, Chief Data Scientist at NICE Actimize, takes us from his early fascination with math and chess to groundbreaking advancements in financial crime detection. Butvinik shares insights into unbiased AI models, the potential of quantum computing, and the joy of knowledge-sharing. Join us as we uncover the essence of data science—a universal language shaping the future, guided by a visionary leader at the forefront of AI and data innovation.

You can listen to this episode of Leading with Data on popular platforms like Spotify, Google Podcasts, and Apple. Pick your favorite to enjoy the insightful content!

Key Insights From our Conversation with Danny Butvinik

  • Data science is a dialect of the mathematical language used to understand the universe.
  • Detecting financial crime involves understanding complex human behavior and developing unbiased AI models.
  • Quantum computing is anticipated to significantly enhance financial crime detection by speeding up data processing.
  • Consistently creating valuable content for the data science community requires discipline, consistency, resilience, and adaptability.
  • Storytelling is a vital skill in data science for effectively communicating complex ideas.
  • The future of AI includes advancements in climate modeling, cognitive AI systems, and generative AI, with potential improvements from quantum computing.
  • Generative AI is expected to become more prevalent in applications, improving its ability to control and analyze itself.

Join our upcoming Leading with Data sessions for insightful discussions with AI and Data Science leaders!

Now, let’s look at Danny Butvinik’s responses to the questions asked in the Leading with Data.

How did your passion for mathematics and chess lead you to data science?

From a young age, I was drawn to the beauty and complexity of mathematics and chess. My journey into data science began with my fascination for Pierre de Fermat’s Last Theorem and the potential of creating cognitive artificial intelligence that could not just solve but prove mathematical problems. This initial curiosity sparked my interest in machine learning, AI, and eventually led me to data science, which I view as a dialect of the mathematical language we use to articulate our understanding of the universe.

Can you share a pivotal moment in your journey that had a lasting impact on you?

Certainly, my ‘aha’ moment came when I realized the potential of analyzing digital traces left by devices to understand human behavior. By studying these patterns, we can move from understanding to predicting and ultimately to prescribing actions to prevent issues like fraud, diseases, and natural disasters. This epiphany solidified my commitment to data science and its profound implications for our world.

How have you approached the challenge of detecting financial crime in your work?

Detecting financial crime is about understanding complex human behavior behind fraudulent activities. My focus has been on developing models that accurately detect fraud without inherent biases against certain groups. Ensuring that our models don’t perpetuate existing prejudices has been a key challenge, requiring careful data analysis and transparent model training processes.

What methodologies have you found effective in creating unbiased AI models?

Addressing bias in AI is complex, as it requires identifying and measuring biases that are often subtle and deeply ingrained. My work has involved categorizing biases and developing metric spaces to encapsulate them. I’ve also focused on enhancing model transparency and explainability, particularly with neural networks, by investigating existing approaches and creating ensemble methods to better interpret these models.

How do you envision the role of quantum computing in financial crime detection in the coming years?

Quantum computing holds the promise of revolutionizing financial crime detection by exponentially increasing the speed and capacity of data processing. I foresee its integration into AI and financial crime analysis, enabling us to analyze more complex datasets and transactions much faster than is currently possible.

What motivates you to share your knowledge with the data science community?

I find great satisfaction in explaining complex concepts in a concise and intuitive manner. My drive comes from the joy of helping others understand intricate ideas, which often lack clear and concise explanations in existing literature. By providing intuitive explanations, I aim to make these concepts stick with aspiring data scientists and practitioners.

How do you manage to consistently create and share content with the community?

I guide my approach with three principles: focusing on fundamentals, adopting a parsimonious approach to explaining complex topics, and always infusing a sense of intuition in my content. I rely on mental muscles—discipline, consistency, resilience, and adaptability—to maintain a regular pace in content creation and learning new concepts.

How has your personal growth journey evolved in the last couple of years?

Over the past few years, I’ve become a better storyteller, which is crucial in data science for explaining concepts, presenting to stakeholders, and engaging with clients. I’ve also increased my output of academic articles and patents, particularly in the field of online machine learning. Additionally, my knowledge of financial crime has deepened significantly, placing me in a very different position than I was a few years ago.

I’m excited about the advancements in climate modeling, cognitive AI systems, and generative AI. These areas are set to benefit greatly from quantum computing, which will improve our ability to create more accurate models. Generative AI, in particular, will become embedded in various applications, improving its self-analysis capabilities and content quality.

Summing Up 

Danny Butvinik’s narrative underscores the evolution and vitality of data science. From deciphering human behavior to envisioning the quantum-powered future of financial crime detection, his journey epitomizes innovation. The commitment to unbiased AI, effective storytelling, and continuous knowledge dissemination unveils the dynamic landscape of data science. As we embrace trends in climate modeling, cognitive AI, and generative AI, propelled by quantum computing, Butvinik’s vision echoes a future where data science not only anticipates but shapes the trajectory of progress. In this illuminating conversation, we witness the unfolding narrative of a data science luminary, charting the course for a transformative future.

For more engaging sessions on AI, data science, and GenAI, stay tuned with us on Leading with Data.

Check our upcoming sessions here.

AIArtificial IntelligenceDanny ButvinikKunal jainLeading With Datamachine learning
Nitika Sharma
Nitika Sharma

Hello, I am Nitika, a tech-savvy Content Creator and Marketer. Creativity and learning new things come naturally to me. I have expertise in creating result-driven content strategies. I am well versed in SEO Management, Keyword Operations, Web Content Writing, Communication, Content Strategy, Editing, and Writing.

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