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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 #18: Andriy Burkov’s Journey to Writing the Ultimate 100-Page Machine Learning Book

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

Introduction

Have you seen most of the recommended books on Machine Learning only to feel overwhelmed by their thickness and the amount of effort it will take to read those books?

If you feel that way – don’t worry! You are not alone. A lot of people face this situation but do very little about it. Not Andriy Burkov! Andriy saw this and thought that the ideal Machine Learning book for beginners should be written within 100 pages.

More importantly – he wrote the book and published it. His recently launched ‘Hundred-Page Machine Learning Book‘ has quickly ascended the bestseller list and is perched at #1 on Amazon (under the ‘machine learning’ category). The book has even been endorsed by the great Peter Norvig!

It was our pleasure hosting him on episode #18 of DataHack Radio. Kunal and Andriy had a rich discussion on several topics, including:

  • Andriy’s foray into machine learning and artificial intelligence
  • Industry experience, including NLP projects at Gartner
  • His idea behind writing the book
  • The challenges he faced while trying to condense topics into this crisp format
  • Advice to aspiring data scientists

All DataHack Radio episodes are available on the below podcast platforms. Subscribe today!

 

Andriy Burkov’s Background & Foray into Artificial Intelligence

Andriy’s professional career began in Ukraine at the turn of the millennium. He created his own online startup while doing his graduation in Computer Engineering and Networking. But after working on this for 3 years, the dotcom bubble burst and his investor decided to withdraw.

Andriy wasn’t about to give up on his dreams. The fire within him to build another online startup continued to burn bright. This wasn’t possible in Ukraine back then though, given the economic situation. Attracting another investor was proving to be impossible.

The first thought Andriy had was to move to Europe with his family. They considered France but eventually settled on Quebec, Canada. The primary reason was immigration purposes and Quebec seemed a good fit as it has a French dwelling community.

Once in Canada, Andriy spent some time looking for jobs before finally settling on doing his Master’s in computer science and artificial intelligence. He converted this into a Ph.D, choosing multi-agent systems as his primary topic. His thesis – Leveraging Repeated Games for Solving Complex Multi-agent Decision Problems.

Want to see how much the field of AI has changed in the last decade? Here’s an eye-opening anecdote Andriy told us:

“One of my ex-colleagues, when he finished his Ph.D in Quebec City (several years before me), couldn’t find a job in artificial intelligence. It was such an exotic field.”

It’s mind-blowing how quickly technology changes our lives.

 

Gaining Valuable Industry Experience Post-Ph.D

Two paths opened up once Andriy finished his Ph.D – research or teaching. The former appealed to him far more than becoming a full-time professor. So he decided to dip his toes into an industry role with Fujitsu, a Japanese multinational company. He worked with Fujistsu for 2 years before moving on.

From there, Andriy shifted to WANTED Technologies, a job announcement portal for companies. His role was primarily about turning terabytes of job announcements from online job boards into structured knowledge. An excellent experience that has played a big part in his professional career.

That was followed by a move to a company that was acquired twice, the second time by Gartner. In Andriy’s own words:

“I survived two acquisitions!”

There was an understandable seed of doubt – will working for such a huge organization stifle creativity? But Gartner’s work culture soon put any doubts to rest.

Andriy leads a team working on the research and development side of a product called Talent Neuron. It is a portal that “combines big data and statistical insights to provide global talent, location and competitive intelligence for any industry or function”.

 

Andriy’s Interest and Work in NLP

A quick glance at Andriy’s LinkedIn profile tells us he is a Natural Language Processing (NLP) expert. That’s a topic we at Analytics Vidhya are very passionate about. So picking Andriy’s brains on NLP felt like a natural fit!

His team’s role at Gartner is geared more towards applied text analytics, rather than core computational linguistics. I have mentioned a few intriguing tasks the team works on (or has worked on):

  • Parsing of free-form résumé to detect and extract candidate name, employers, skills, certifications, employment history
  • Parsing of job descriptions to detect and extract job title, salary, employer name, assign a standard occupation code
  • Automated noisy and multi-language data normalization, cleanup, de duplication
  • Novel data detection
  • Language detection / text segmentation by language
  • Sequence labeling (queries, addresses, salaries)

This is one of the most absorbing sections in this episode, especially for NLP enthusiasts. Andriy spoke about the complexities and nuances of language modeling with several examples. A must-listen!

 

Idea behind the ‘Hundred-Page Machine Learning Book’

How did the idea of writing this book come about? It definitely wasn’t the usual route! The thought hadn’t even crossed Andriy’s mind initially.

There’s a huge following on LinkedIn Andriy has built up over the years. He posts some superb stuff related to machine learning everyday (you should definitely give him a follow).

“I have a ton of machine learning and artificial intelligence books at home. But I never end up finishing any of them. I asked myself why?”

A huge reason for that is we’ve become increasingly busy. We barely have time to finish a book, especially one that runs over 500 pages. He put his thoughts in a LinkedIn post, saying that if he ever wrote a book on machine learning, it wouldn’t be more than 100 pages long.

He had no intention of actually writing it though! The post went viral – hundreds of likes, tons of comments. The majority of comments could be divided into two categories:

  • The nay-sayers: It’s impossible to condense so much knowledge into 100 pages
  • The enthusiasts: Andriy, you can write the book. Go ahead and do it!

“I took a week to think about it. I told myself – “I will try to write several chapters. If it goes well, we’ll see. If it doesn’t go well, I’ll stop.””

The first three chapters were written over a weekend and the response? Overwhelmingly positive. And while the number of pages ran a shade above hundred, Andriy still managed to pack in all the fundamentals and essential knowledge a data scientist should have. A tremendous achievement!

One of the most prominent things about the book is that it’s available to read online for anyone. If you like it or found it useful, you should buy the paperback or hardcover edition. I really appreciate Andriy’s thought process behind doing this.

 

Challenges and Trade-offs while Writing the Book

Given the crisp nature of the book, leaving certain concepts out was inevitable. But which ones? And to what degree? Those were critical questions Andriy addressed in this section.

The idea, as we discussed earlier, was to include all the fundamentals, such as the mathematics behind core machine learning algorithms. So topics like reinforcement learning and back propagation were either not included or just brushed upon. A fair trade-off, in my opinion.

 

Advice to People Entering Data Science (Aspiring Data Science Professionals)

Here’s a brief summary of the key points Andriy spoke about:

  • Learn the basics of computer science
  • Learn to be a good programmer
  • Find an interesting problem and work your socks off to solve it (there are plenty of platforms to find datasets)

 

End Notes

Just hearing Andriy talk about his book made me appreciate how hard the journey must have been. I have personally read the book and couldn’t recommend it enough. It is a gem and will help thousands of aspiring data scientists make the leap into this field.

Have you read the book yet? Let me know in the comments section below!

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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bikram kachari
bikram kachari

I have read his book. Its a great book. It doesn't delve much into the nitty gritty details, but explains the materials in a very lucid manner. A beginner with no previous knowledge of machine learning can even pick up his book and enjoy. The price too is affordable at $20

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