NeetCode: A better way to prepare for coding interviews

The best free resources for Coding Interviews. Period.
- Organized study plans and roadmaps (Blind 75, Neetcode 150).
- Detailed video explanations.
- Public Discord community with over 30,000 members.
- Sign in to save your progress.

Links:
- Roadmap
- Practice (Core Skills, Blind 75, Neetcode 150, Neetcode All)
- Algorithms and Data Structures for Beginners (course) paid
- Advanced Algorithms (course) paid

Navigational hashtags: #armknowledgesharing #armsites #armtutorials
General hashtags: #leetcode #python #algorithms #datastructures #interviewpreparation #technicalinterview

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Write faster Python code, and ship your code faster

Faster and more memory efficient data
- Articles: Learn how to speed up your code and reduce memory usage.
- Products: Observability and profiling tools to help you identify bottlenecks in your code.

Docker packaging for Python
- Articles: Learn how to package your Python application for production.
- Products: Educational books and pre-written software templates.

Navigational hashtags: #armknowledgesharing #armsites
General hashtags: #python #development #docker

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The Hitchhiker’s Guide to Python. Python Best Practices Guidebook by Kenneth Reitz, Tanya Schlusser

The Hitchhiker's Guide to Python takes the journeyman Pythonista to true expertise. More than any other language, Python was created with the philosophy of simplicity and parsimony. Now 25 years old, Python has become the primary or secondary language (after SQL) for many business users. With popularity comes diversity and possibly dilution.

This guide, collaboratively written by over a hundred members of the Python community, describes best practices currently used by package and application developers. Unlike other books for this audience, The Hitchhiker's Guide is light on reusable code and heavier on design philosophy, directing the reader to excellent sources that already exist.

Links:
- Site
- Book

Navigational hashtags: #armknowledgesharing #armbooks #armsites
General hashtags: #python #development

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Models Demystified
A Practical Guide from Linear Regression to Deep Learning
by Michael Clark & Seth Berry

This book is designed to guide readers on a comprehensive journey into the world of data science and modeling. For those just beginning their exploration, it offers:
- A solid foundation in the basics of modeling, presented from a practical and accessible perspective.
- A versatile toolkit of models and concepts that can be immediately applied to real-world problems.
- A balanced approach that integrates both statistical and machine learning methodologies.

For readers already experienced in modeling, the book provides:
- Deeper context and insights into familiar models.
- An introduction to new and advanced models that expand your knowledge.
- Enhanced understanding of how to select the most appropriate model for a given task and where to focus your efforts.

Above all, this book aims to highlight the common threads that connect different models, offering readers a clear and intuitive understanding of how they function and interrelate. Whether you're a beginner or a seasoned practitioner, this resource is crafted to deepen your expertise and broaden your perspective on the art and science of modeling.

Table of contents:

Preface
1  Introduction
2  Thinking About Models
3  The Foundation
4  Understanding the Model
5  Understanding the Features
6  Model Estimation and Optimization
7  Estimating Uncertainty
8  Generalized Linear Models
9  Extending the Linear Model
10  Core Concepts in Machine Learning
11  Common Models in Machine Learning
12  Extending Machine Learning
13  Causal Modeling
14  Dealing with Data
15  Danger Zone
16  Parting Thoughts

Link: Site

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General hashtags: #ml #machinelearning #r #python #linear #metrics #featureengineering #optimization #mle #glm #dl #deeplearning

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Full Speed Python by João Miguel Jones Ventura

This book aims to teach the Python programming language using a practical approach. Its method is quite simple: after a short introduction to each topic, the reader is invited to learn more by solving the proposed exercises.

These exercises have been used extensively in author's web development and distributed computing classes at the Superior School of Technology of Setúbal. With these exercises, most students are up to speed with Python in less than a month. In fact, students of the distributed computing course, taught in the second year of the software engineering degree, become familiar with Python's syntax in two weeks and are able to implement a distributed client-server application with sockets in the third week.

Link: Book

Navigational hashtags: #armbooks
General hashtags: #python #programming

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python-patterns

A collection of design patterns and idioms in Python.

Remember that each pattern has its own trade-offs. And you need to pay attention more to why you're choosing a certain pattern than to how to implement it.

Link: GitHub

Navigational hashtags: #armsite
General hashtags: #python #programming #patterns #development #engineering

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Problem Solving with Algorithms and Data Structures using Python by Brad Miller and David Ranum, Luther College

This textbook is about computer science. It is also about Python. However, there is much more.

The study of algorithms and data structures is central to understanding what computer science is all about. Learning computer science is not unlike learning any other type of difficult subject matter. The only way to be successful is through deliberate and incremental exposure to the fundamental ideas. A beginning computer scientist needs practice so that there is a thorough understanding before continuing on to the more complex parts of the curriculum. In addition, a beginner needs to be given the opportunity to be successful and gain confidence.

This textbook is designed to serve as a text for a first course on data structures and algorithms, typically taught as the second course in the computer science curriculum. Even though the second course is considered more advanced than the first course, this book assumes you are beginners at this level. You may still be struggling with some of the basic ideas and skills from a first computer science course and yet be ready to further explore the discipline and continue to practice problem solving.

Authors cover abstract data types and data structures, writing algorithms, and solving problems. They look at a number of data structures and solve classic problems that arise. The tools and techniques that you learn here will be applied over and over as you continue your study of computer science.

Links:
- Site
- Book

Navigational hashtags: #armbooks #armcourses
General hashtags: #python #algorithms #datastructures #programming #cs #computerscience

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CS50’s Introduction to Programming with Python by Harvard

An introduction to programming using a language called Python. Learn how to read and write code as well as how to test and “debug” it. Designed for students with or without prior programming experience who’d like to learn Python specifically.

Learn about functions, arguments, and return values (oh my!); variables and types; conditionals and Boolean expressions; and loops. Learn how to handle exceptions, find and fix bugs, and write unit tests; use third-party libraries; validate and extract data with regular expressions; model real-world entities with classes, objects, methods, and properties; and read and write files.

Hands-on opportunities for lots of practice. Exercises inspired by real-world programming problems.

No software required except for a web browser, or you can write code on your own PC or Mac.

Link: Course

Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #python

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Interpreting Machine Learning Models With SHAP. A Guide With Python Examples And Theory On Shapley Values by Christoph Molnar

Machine learning is transforming fields from healthcare diagnostics to climate change predictions through their predictive performance. However, these complex machine learning models often lack interpretability, which is becoming more essential than ever for debugging, fostering trust, and communicating model insights.

Introducing SHAP, the Swiss army knife of machine learning interpretability:
- SHAP can be used to explain individual predictions.
- By combining explanations for individual predictions, SHAP allows to study the overall model behavior.
- SHAP is model-agnostic – it works with any model, from simple linear regression to deep learning.
- With its flexibility, SHAP can handle various data formats, whether it’s tabular, image, or text.
- The Python package shap makes the application of SHAP for model interpretation easy.

This book will be your comprehensive guide to mastering the theory and application of SHAP. It starts with the quite fascinating origin in game theory and explores what splitting taxi costs has to do with explaining machine learning predictions. Starting with using SHAP to explain a simple linear regression model, the book progressively introduces SHAP for more complex models. You’ll learn the ins and outs of the most popular explainable AI method and how to apply it using the shap package.

In a world where interpretability is key, this book is your roadmap to mastering SHAP. For machine learning models that are not only accurate but also interpretable.

Links:
- Paperback
- eBook

Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #ml #machinelearning #shap #interpretability #python #shapley #shapleyvalues

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A new perspective on Shapley values, part I: Intro to Shapley and SHAP by Edden Gerber

This post is the first in a series of two posts about explaining statistical models with Shapley values.

There are two main reasons you might want to read it:
1. To learn about Shapley values and the SHAP python library.
This is what this post is about after all. The explanations it provides are far from exhaustive, and contain nothing that cannot be gathered from other online sources, but it should still serve as a good quick intro or bonus reading on this subject.
2. As an introduction or refresher before reading the next post about Naive Shapley values.
The next post is my attempt at a novel contribution to the topic of Shapley values in machine learning. You may be already familiar with SHAP and Shapley and are just glancing over this post to make sure we’re on common ground, or you may be here to clear up something confusing from the next post.

Link: Post

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General hashtags: #shap #shapley #interpretation #ml #python

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A new perspective on Shapley values, part II: The Naïve Shapley method by Edden Gerber

Why should you read this post?
1. For insight into Shapley values and the SHAP tool.
Most other sources on these topics are explanations based on existing primary sources (e.g. academic papers and the SHAP documentation). This post is an attempt to gain some understanding through an empirical approach.
2. To learn about an alternative approach to computing Shapley values, that under some (limited) circumstances may be preferable to SHAP.
If you are unfamiliar with Shaply values or SHAP, or want a short recap of how the SHAP explainers work, check out the previous post. In a hurry? The author has emphasized the key sentences in bold to assist your speed-reading.

Link: Post

Navigational hashtags: #armknowledgesharing #armsites
General hashtags: #shap #shapley #interpretation #ml #python

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