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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

Charting the Entrepreneurial AI Journey with Patrick Bangert

Nitika Sharma
Nitika Sharma Last Updated : 04 Jan, 2024
4 min read

In our recent Leading with Data session, Patrick Bangert, a seasoned technology executive renowned for his expertise in artificial intelligence and data science, shared invaluable insights into his journey, key strengths, and the evolving landscape of the data science field. From reshaping Samsung’s AI team to pioneering innovations and transitioning from academia to entrepreneurship, Bangert’s experiences offer a rich tapestry of lessons for professionals in the data science realm.

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 Patrick Bangert

  • The integration of theoretical knowledge and practical application is key to successful data science projects.
  • Building trust and understanding client needs are more critical than the technology itself in data science entrepreneurship.
  • The evolution of data science tools, from databases to cloud computing, has significantly expanded the potential applications of AI.
  • Generative AI’s impact lies in its user-friendly interface, which has made the technology more accessible to a broader audience.
  • Enterprise search and summarization are promising use cases for generative AI, offering the potential to revolutionize how businesses process and analyze data.
  • The future of AI in healthcare looks promising, with potential applications ranging from administrative automation to improved patient care.
  • Communication skills, including public speaking and creating presentations, are essential for data science professionals aspiring to leadership roles.

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

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

How did your journey in data science begin, and what led you to this field?

I remember being fascinated by the cosmos as a child, thanks to the astronomy books my father had. This curiosity led me to pursue a physics degree, where I was introduced to the world of data analysis. My ‘aha’ moment came during my PhD in theoretical physics when I stumbled upon neural networks in the late 1990s. Despite the skepticism surrounding AI after the ’80s hype and subsequent ‘AI winter,’ I saw potential in these models. My successful application of neural networks to physics problems marked the beginning of my data science journey.

Can you share an experience from your early days that shaped your approach to data science?

My foray into the practical application of data science began with the petrochemical industry. They faced two major challenges: equipment failure prediction and optimization of operational set points. I spent 15 years developing solutions for these problems, using neural networks and domain knowledge to predict equipment failures and recommend optimal settings. This experience taught me the importance of combining theoretical approaches with practical applications to derive meaningful insights from data.

How did your transition from academia to entrepreneurship influence your work in data science?

Leaving academia to start my own company was a pivotal moment. I realized that to apply applied mathematics effectively, one needs to step out of the university setting. My company focused on bringing neural network solutions to the petrochemical industry. This shift from research to practical application and business taught me the value of building trust with clients and understanding their pain points, which is crucial for any data science endeavor.

What were some of the technological advancements you witnessed over the years in data science?

The evolution of tools and technologies has been remarkable. From the early days of recording data in databases to the advent of cloud computing, the landscape has changed dramatically. We’ve seen sensors become ubiquitous, databases become more sophisticated, and machine learning models become more powerful. The introduction of imaging hardware and the rise of computer vision have opened new avenues for AI applications, such as safety monitoring using AI vision algorithms.

How did your role at Samsung and your current role at Searce Inc differ from your entrepreneurial experience?

At Samsung, I led the AI division, which was a broader role than my entrepreneurial venture but still focused on product development and sales. The experience taught me how large corporations function and the importance of navigating corporate processes. At Searce Inc, I lead the AI and data analytics business units, focusing on project-based work rather than product development. This has introduced me to the consulting side of AI and cloud technologies, emphasizing the importance of understanding customer needs and deploying solutions that address their core problems.

What are your thoughts on the recent developments in generative AI, such as ChatGPT?

The innovation of generative AI, particularly large language models like ChatGPT, lies not in the technology itself but in the interface that allows users to interact with it conversationally. While these models have been around for years, the user-friendly interface has brought them into the limelight. However, most current usage is exploratory rather than practical. The real challenge lies in finding profitable and scalable applications for these models in the enterprise.

What are some promising use cases for generative AI that you’ve encountered?

Enterprise search and summarization are two use cases where generative AI can make a significant impact. Enterprise search can transform the way we access and utilize internal company data by providing holistic answers to complex queries, while summarization can help businesses analyze vast amounts of recorded data, such as customer service calls, to extract actionable insights. These applications can save time and resources, making generative AI a valuable tool for businesses.

Looking ahead, what do you foresee for the future of AI in the next few years?

In the immediate future, I expect businesses to focus on monetizing existing AI technologies rather than developing new ones. I hope to see more practical deployments of AI in various industries, particularly healthcare, where AI can automate paperwork processes and improve patient outcomes. The potential for AI to free healthcare professionals from administrative tasks and enhance patient care is immense.

What advice would you give to mid-career professionals in data science and those looking to enter the field?

For those already in data science, focus on communication skills as you move up the management ladder. Creating effective slide decks and public speaking are crucial skills for leadership roles. For those new to data science, gaining a foundational understanding of data analysis is essential. Certifications can be valuable in demonstrating your knowledge and ability to apply data science principles in decision-making processes.

Summing Up

Patrick Bangert’s journey in data science, marked by transformative leadership and a seamless blend of theoretical knowledge with practical applications, provides a roadmap for aspiring and seasoned professionals alike. As the data science landscape continues to evolve, Bangert’s perspectives on generative AI, promising use cases, and the future of AI in healthcare underscore the dynamic nature of the field. With a focus on communication skills and a commitment to addressing real-world challenges, his insights offer valuable guidance for navigating the exciting and ever-expanding realm of data science.

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

Check our upcoming sessions here.

AIAI JourneyArtificial Intelligencedata scienceKunal jainLeading With Datamachine learningPatrick Bangert
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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