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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

DataHack Radio #11: Decision Intelligence with Google Cloud’s Chief Decision Scientist, Cassie Kozyrkov

Pranav Dar
Pranav Dar Last Updated : 13 Jun, 2019
4 min read

Introduction

What is decision intelligence? How does it tie into the world of data science? And what does Google have to do with it all? Click on the above SoundCloud link and find out!

Welcome to episode #11 of DataHack Radio, where we were joined by Google Cloud’s Chief Decision Scientist, Cassie Kozyrkov! Cassie is a well-known speaker in the data science sphere, and often pens down her thoughts in articulate fashion in this field. She takes us on a journey into her life at Google and how she went from being a Statistician at Google to her current role.

This is a short summary of Cassie in conversation with Kunal. Give the podcast a listen and find out how a top Google scientist thinks, works, and structures her thoughts! As a bonus, there are some brilliant quotable quotes in this episode, which you will find yourself chuckling and nodding to.

Subscribe to DataHack Radio NOW and listen to this, as well as all previous episodes, on any of the below platforms:

 

Journey from MATLAB to R

The story behind Cassie’s shift from using MATLAB to R is a fascinating one. While working with MATLAB, she wanted to make a particular kind of chart. After spending hours trying to figure it out, she decided to try her hand at R.

It took her half an hour to design the plot she wanted! And the rest, as they say, is history. She’s a big R fan, and turns to Python if absolutely necessary.

 

Initial Challenges at Google

Cassie joined Google in 2014 as a Statistician and one of her early projects was getting rid of duplicate entries in Google Maps. It was a far more challenging task than one might think. How do you actually define duplicate entries? You need to define good processes for measuring and verifying each duplicate entry. On a global scale, this is not a straightforward task.

There were a lot of statistical techniques involved in this process, like hypothesis testing. But she wasn’t the only statistician on board this project, which meant coordinating and collaborating with others. Just getting people to agree on one definition of a duplicate entry was a long winding process (anyone who has worked on a project staffed with over 50 employees will be able to relate to this!).

 

Cassie’s Thoughts on her Current Role & Decision Intelligence

“It’s quite risky for data scientists to join teams that don’t quite know what they’re doing.”

Decision Intelligence (DI) augments data science with theory from social science, decision theory, and managerial science, among other applications. DI provides a framework for best practices in organizational decision-making and processes for applying machine learning and AI at scale.

Cassie finds herself these days working on multiple projects, especially in the initial stages. This way she can assign the task of particular things to the correct people, instead of letting projects get bogged down due to teams not being aware of what the next step should be.

If you just rely on data science in a project, the whole thing just might flop. You need to add some extra muscle, which decision intelligence supplies. This is quite a fascinating concept and worth listening to in the podcast.

She also aims to help the outside world (outside Google, that is) do some of this stuff, in a more organised and better manner. Check out some of her talks in various global forums to get an idea of what she means by that.

 

Democratizing Artificial Intelligence & Machine Learning (and their future)

“When it comes to AI, I think the whole world is making a mistake of talking about it as some form of holy water, when it’s just water.”

What Cassie means by the above quote is that when you start thinking about it as holy water, it instantly means it’s accessible only to a select few. This is absolutely not true for AI and there’s no special magic attached to it.

“Think of it as a different way of communicating your wishes to a computer.” You can use it to power your business and improve your results, without needing to rely on intuition and good old luck. Cassie encourages everyone to explore programming and machine learning, at least at a basic level. It’s such a wonderful gateway to a whole new world where you have the power to change results, so why shouldn’t you leverage that?

Coming to the future of this field, Cassie is most excited about the applied side of machine learning and AI. She uses her popular analogy of a microwave and other kitchen appliances to explain this – a truly innovative way of thinking about this domain!

 

End Notes

I personally had only vaguely heard about decision intelligence before listening to this podcast, so it was quite an eye-opener. It’s a intriguing discipline and one I feel anyone in the data science field should explore.

This is one those podcasts you just don’t want to end, it has so much knowledge packed into an hour! I hope you enjoy listening to it as much as I did.

AIdata science podcastDataHack Radiogooglemachine learning
Pranav Dar
Pranav Dar

Senior Editor at Analytics Vidhya.Data visualization practitioner who loves reading and delving deeper into the data science and machine learning arts. Always looking for new ways to improve processes using ML and AI.

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Responses From Readers

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PRITESH S
PRITESH S

Good job putting up these awesome interviews guys !! Keep them coming !!

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Pranav Dar
Pranav Dar

Glad you're enjoying our podcasts, Pritesh. :)

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