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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

A Journey of Entrepreneurship & Storytelling with Joshua Starmer

Nitika Sharma
Nitika Sharma Last Updated : 08 May, 2024
4 min read

In an era where data science and machine learning are reshaping our world, Joshua Starmer stands out as a leading educator and innovator. With a unique background in computer science and a passion for biology, he has carved a path that merges these fields seamlessly. Through his journey, he identified a niche in data analytics and machine learning, integrating his computational skills with biological research. Starmer’s story and insights offer a fascinating glimpse into the world of education, adaptation, and the power of merging diverse skill sets.

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 Joshua Starmer

  • The integration of computational skills with biological research led to a unique niche in data analytics and machine learning.
  • The creation of StatQuest was driven by a need to understand statistics deeply and to communicate these concepts effectively to non-experts.
  • The evolution from creating content for a specific lab to addressing a global audience required a shift in approach and a broader understanding of data science applications.
  • The learning process for new data science concepts is iterative and can be lengthy, emphasizing the importance of patience and persistence in education.
  • Running a successful educational platform involves a balance between content creation and business management.
  • Generative AI serves as a useful starting point for content creation, turning the daunting task of beginning from scratch into a more manageable editing process.
  • The upcoming book on neural networks reflects a focused effort to provide comprehensive coverage of a complex and highly relevant topic in data science.

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

Let’s look into the details of our conversation with Joshua Starmer!

How did your journey into data science and machine learning begin?

My journey into data science and machine learning began with a fascination for computers and programming that I nurtured since childhood. However, it wasn’t until much later, after earning a degree in computer science and working in a hospital on database work, that I took a biology course that completely captivated me. This newfound interest in biology led me to explore how I could merge my computational skills with biological research.

Eventually, I pursued a PhD in bioinformatics, which is essentially the application of statistics to biological data. My goal was to conduct biological research, but I ended up in a genetics laboratory at the University of North Carolina, where I realized my true passion lay in data analytics. This realization led to the creation of my YouTube channel, StatQuest, as a means to teach myself statistics and share that knowledge with others.

What inspired you to start your YouTube channel, StatQuest?

StatQuest was born out of my desire to better understand statistics and to communicate complex analytical concepts to my colleagues in a relatable and understandable way. Initially, the channel was intended for a small audience—my coworkers in the lab. I used examples from our research on mice to explain statistical methodologies. The channel’s success among my colleagues encouraged me to continue creating content, and eventually, it caught the attention of a broader audience. A video on principal component analysis marked a turning point, expanding my reach and solidifying my role as an educator in the field of data science.

How has the process of creating educational content evolved for you over time?

In the early days, my content was driven by the immediate needs of my lab colleagues. As I transitioned to full-time content creation, I had to abstract from my direct lab experience and anticipate the broader needs of the data science community. I began conducting workshops and consulting to stay connected with real-world applications of data science. This hands-on experience has been invaluable in creating content that is not only informative but also grounded in practical use cases.

What challenges do you face when learning and teaching new data science concepts?

The biggest challenge is often starting from a place of confusion. I dive deep into reading and coding to understand new concepts like state space models. This process can be time-consuming, with some videos taking years to produce. However, my goal is to distill complex ideas into simple, visual explanations that resonate with a wide audience. I strive to create content that is exceptional and above average, which means constantly refining and updating my approach.

How do you balance the demands of running a business with content creation?

Running a business involves much more than just creating videos. I handle customer service, website maintenance, and various administrative tasks, which can limit the time I spend on actual content creation. Despite these demands, I’m exploring ways to streamline business operations to potentially return to consulting or lab work part-time. This would allow me to stay connected with the practical side of data science and continue to improve as an educator.

What has been your experience with generative AI, and how do you use it in your work?

Generative AI has been useful for generating rough drafts, whether it’s for programming or explaining concepts. It helps transform the intimidating “blank page problem” into an editing problem, giving me a starting point to refine and tailor the content for teaching purposes. While I don’t rely on generative AI extensively, it serves as a helpful tool for brainstorming and overcoming initial creative hurdles.

Can you share some insights about your upcoming book on neural networks?

I’m currently working on a book dedicated entirely to neural networks. My first book, “The StatQuest Illustrated Guide to Machine Learning,” provided a broad overview of machine learning techniques. However, given the popularity and complexity of neural networks, I felt they deserved a book of their own. I aim to release this new book by the end of the year, and it will cover neural networks in depth, with the same visual and accessible approach that characterizes my other educational materials.

What changes would you like to see in the way educational content is delivered on platforms like YouTube?

I wish there was a way to update educational content on YouTube more seamlessly. Just like new editions of books replace old ones on bookstore shelves, I’d like to see a system where updated videos can easily take the place of their outdated versions. This would ensure that learners always have access to the most current and relevant information without having to navigate through multiple versions of the same content.

Summing-up

Joshua Starmer’s journey showcases the power of merging diverse skill sets and a passion for education. Through StatQuest, he has not only filled a gap in data science education but also inspired a global audience to embrace complex topics. His iterative learning process, patient persistence, and innovative use of tools like generative AI offer valuable lessons for educators and content creators. As Starmer continues to evolve his craft, consulting, and exploring new avenues, his impact on the field of data science education will undoubtedly leave a lasting legacy.

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

Check our upcoming sessions here.

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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