Tarek Amr

Machine Learning Data Scientist

Tarek Amr

Hands-On Machine Learning with Scikit-Learn and Scientific Python Toolkits by Tarek Amr

Order my new book: Hands-On Machine Learning with Scikit-Learn

📖 Machine learning is applied everywhere, from business to research and academia, and scikit-learn is a versatile library machine learning practitioners have to learn and use everyday. It implements the commonly used supervised and unsupervised machine learning algorithms in Python.

👉 This book is your practical guide to bootstrap your data science career, and to start buildning hands-on machine learning solutions with scikit-learn and its related ecosystem.

📬 Order it from the following links.

Amazon GoodReads
Practical D3.js by Tarek Amr

Order my book: Practical D3

👉 This is your guide to mastering the efficient use of D3.js in professional-standard data visualization projects.

You will learn what data visualization is, how to work with it, and how to think like a D3.js expert, both practically and theoretically. You will learn how to get the data, how to clean and refine it, and how to display it in the best charts and layouts. The book is for experience Front End JavaScript Developers, as well as for Data Journalists who who have basic knowledge of HTML/CSS and some JavaScript.

📖 I co-authored this book with Rayna Stamboliyska. Order it from any of the following links

Apress Amazon
Bayesian Experimentation

Bayesian AB-Tests

🔮 Throughout the course of your career, you may need to quantify the effect of changes you make to a product or service. These change can be the introduction of a new design to a website, the creation of a new medicine, or even choosing one football player to take a penalty kick for your team over another in the World Cup final.

🎲 Traditionally, the frequentist approach prevails. However, the Bayesian approach is more intuitive and easy to explain to your stakeholders.

In this article I wrote about the Bayesian hypothesis testing approach.

Oracle Blog

URL-Based Web Page Classification using Language Models (MSc. Dissertation)

In today’s world, millions of web links are being shared every day in emails or on social media web sites. Thus, there is a number of contexts in which it is important to have an efficient and reliable way to classify a web-page by its URL, without the need to visit the page itself. 🕸 For example, a social media website may need to quickly identify status updates linking to malicious websites to block them. Additionally, they can use the classification results in marketing researches to predict users’ preferences and interests.

🎓 Thus, the target of this research is to be able to classify web pages using their URLs only.

Dissertation

Quick Introduction to Reinforcement Learning

Reinforcement Learning (RL) is not as commonly used as Supervised Machine Learning. Yet, it is important to know its basics and have it as a part of your own ML toolkit

🎥 These slides have a quick introduction to RL using the Exploding Kittens Card Game.

Reinforcement Learning Slides

Behavioural Economics

After Richard Thaler's book, Misbehaving, I decided to summarise it. This also serves as my very quick introduction to Behavioural Economics

🎥 This slide deck have a quick introduction Behavioural Economics and its concepts

Behavioural Economics Slides

About Me

🐍 I have more than 8 years of experience in data science and machine learning, and 15 years of experience in Python programming.

🎓 After finishing my postgraduate degree at 🇬🇧 The University of East Anglia (UEA), I worked in a number of startups and scale-up companies in 🇪🇬 Egypt and 🇳🇱 The Netherlands.

🐾 In previous lives I used to work as an Information Security Consultant and Presales Manager. I previously volunteerd in Global Voices Online (GVO) and the Open Knowledge Foundation (OKFN).

🔥 I am trying to challenge the old saying, "Jack of all trades, master of none".

LinkedIn Profile

Jack of all trades, Master of Machine Learning