StatQuest with Josh Starmer
"Statistics, Machine Learning and Data Science can sometimes seem like very scary topics, but since each technique is really just a combination of small and simple steps, they are actually quite simple. My goal with StatQuest is to break down the major methodologies into easy to understand pieces. That said, I don't dumb down the material. Instead, I build up your understanding so that you are smarter."
This is how Joshua Starmer PhD describes his channel and I completely agree with him!
I watch his videos to understand the meaning of the algorithms before going into details, and I encourage you to do the same!
YouTube: https://www.youtube.com/@statquest/videos
Website: https://statquest.org/
Book: https://www.amazon.com/StatQuest-Illustrated-Guide-Machine-Learning/dp/B09ZCKR4H6
#machinelearning #datascience #algorithms #statistics #phd
#armknowledgesharing #armyoutube
@data_science_weekly
"Statistics, Machine Learning and Data Science can sometimes seem like very scary topics, but since each technique is really just a combination of small and simple steps, they are actually quite simple. My goal with StatQuest is to break down the major methodologies into easy to understand pieces. That said, I don't dumb down the material. Instead, I build up your understanding so that you are smarter."
This is how Joshua Starmer PhD describes his channel and I completely agree with him!
I watch his videos to understand the meaning of the algorithms before going into details, and I encourage you to do the same!
YouTube: https://www.youtube.com/@statquest/videos
Website: https://statquest.org/
Book: https://www.amazon.com/StatQuest-Illustrated-Guide-Machine-Learning/dp/B09ZCKR4H6
#machinelearning #datascience #algorithms #statistics #phd
#armknowledgesharing #armyoutube
@data_science_weekly
StatQuest!!!
An epic journey through statistics and machine learning
Machine Learning Simplified:
A gentle introduction to supervised learning
The underlying goal of "Machine Learning Simplified" is to develop strong intuition for ML inside you. We would use simple intuitive examples to explain complex concepts, algorithms or methods, as well as democratize all mathematics behind machine learning.
After reading this book, you would understand everything that comes into the scope of Supervised ML, and would be able to not only understand nitty-gritty details of mathematics behind the scene, but also explain to anyone how things work on a high level.
The book is free, but you can purchase EPUB version through Amazon or show your appreciation to the author and purchase PDF through Leanpub.
Table of contents:
I. FUNDAMENTALS OF SUPERVISED LEARNING
Chapter 1. Introduction
Chapter 2. Overview of Supervised Learning
Chapter 3. Model Learning
Chapter 4. Basis Expansion & Regularization
Chapter 5. Model Selection
Chapter 6. Feature Selection
Chapter 7. Data Preparation
II. ADVANCED SUPERVISED LEARNING ALGORITHMS (WIP)
Chapter 1. Regression Models
Chapter 2. Logit Models
Chapter 3. Bayesian Models
Chapter 4. Maximum Margin Models
Chapter 5. Tree-Based Models
Chapter 6. Ensemble Models
Chapter 7. Algorithms Selection
Chapter 8. Hyperparameter Tuning
Chapter 9. Evaluation Metrics
Read for free: https://themlsbook.com/read
Buy on Amazon: https://www.amazon.com/dp/B0B216KMM4/qid=1653304321
Buy on LeanPub: https://leanpub.com/themlsbook
Repository: https://code.themlsbook.com/
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #machinelearning #ml #algorithms #learning #book
@data_science_weekly
A gentle introduction to supervised learning
The underlying goal of "Machine Learning Simplified" is to develop strong intuition for ML inside you. We would use simple intuitive examples to explain complex concepts, algorithms or methods, as well as democratize all mathematics behind machine learning.
After reading this book, you would understand everything that comes into the scope of Supervised ML, and would be able to not only understand nitty-gritty details of mathematics behind the scene, but also explain to anyone how things work on a high level.
The book is free, but you can purchase EPUB version through Amazon or show your appreciation to the author and purchase PDF through Leanpub.
Table of contents:
I. FUNDAMENTALS OF SUPERVISED LEARNING
Chapter 1. Introduction
Chapter 2. Overview of Supervised Learning
Chapter 3. Model Learning
Chapter 4. Basis Expansion & Regularization
Chapter 5. Model Selection
Chapter 6. Feature Selection
Chapter 7. Data Preparation
II. ADVANCED SUPERVISED LEARNING ALGORITHMS (WIP)
Chapter 1. Regression Models
Chapter 2. Logit Models
Chapter 3. Bayesian Models
Chapter 4. Maximum Margin Models
Chapter 5. Tree-Based Models
Chapter 6. Ensemble Models
Chapter 7. Algorithms Selection
Chapter 8. Hyperparameter Tuning
Chapter 9. Evaluation Metrics
Read for free: https://themlsbook.com/read
Buy on Amazon: https://www.amazon.com/dp/B0B216KMM4/qid=1653304321
Buy on LeanPub: https://leanpub.com/themlsbook
Repository: https://code.themlsbook.com/
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #machinelearning #ml #algorithms #learning #book
@data_science_weekly
End to End Machine Learning (FREE Courses)
The best way to learn new concepts is to use them to build something. These courses are structured to build foundational knowledge (100 series), provide in-depth applied machine learning case studies (200 series), and embark on project-driven deep-dives (300 series).
- 111. Getting ready to learn Python, Mac edition
- 112. Getting ready to learn Python, Windows edition
- 201. Intro to Python
- 211. Decision Trees with Python and Pandas
- 212. Time-Series Analysis
- 213. Nonlinear Modelling and Optimization
- 221. The k-nearest neighbours algorithm
- 311. Neural Network Visualization
- 312. Build a Neural Network Framework
- 313. Advanced Neural Network Methods
- 314. Neural Network Optimization
- 321. Convolutional Neural Networks in One Dimension
- 322. Convolutional neural networks in two dimensions
Come have a look around and try one out today!
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #machinelearning #ml #algorithms #learning #course #python #decisiontrees #pandas #timeseries #nonlinear #knn #neuralnetworks #neuralnetwork #convolutionalneuralnetworks #optimization #analysis #visualization
@data_science_weekly
The best way to learn new concepts is to use them to build something. These courses are structured to build foundational knowledge (100 series), provide in-depth applied machine learning case studies (200 series), and embark on project-driven deep-dives (300 series).
- 111. Getting ready to learn Python, Mac edition
- 112. Getting ready to learn Python, Windows edition
- 201. Intro to Python
- 211. Decision Trees with Python and Pandas
- 212. Time-Series Analysis
- 213. Nonlinear Modelling and Optimization
- 221. The k-nearest neighbours algorithm
- 311. Neural Network Visualization
- 312. Build a Neural Network Framework
- 313. Advanced Neural Network Methods
- 314. Neural Network Optimization
- 321. Convolutional Neural Networks in One Dimension
- 322. Convolutional neural networks in two dimensions
Come have a look around and try one out today!
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #machinelearning #ml #algorithms #learning #course #python #decisiontrees #pandas #timeseries #nonlinear #knn #neuralnetworks #neuralnetwork #convolutionalneuralnetworks #optimization #analysis #visualization
@data_science_weekly
Teachable
End to End Machine Learning
Dive into Deep Learning
- Interactive deep learning book with code, maths, and discussions.
- Implemented with PyTorch, NumPy/MXNet, JAX, and TensorFlow.
- Adopted at 400 universities from 60 countries.
Content and Structure
The book can be divided into roughly three parts, focusing on preliminaries, deep learning techniques, and advanced topics focused on real systems and applications:
Part 1: Basics and Preliminaries. Section 1 offers an introduction to deep learning. Then, in Section 2, we quickly bring you up to speed on the prerequisites required for hands-on deep learning, such as how to store and manipulate data, and how to apply various numerical operations based on basic concepts from linear algebra, calculus, and probability. Section 3 and Section 5 cover the most basic concepts and techniques in deep learning, including regression and classification; linear models; multilayer perceptrons; and overfitting and regularization.
Part 2: Modern Deep Learning Techniques. Section 6 describes the key computational components of deep learning systems and lays the groundwork for our subsequent implementations of more complex models. Next, Section 7 and Section 8 introduce convolutional neural networks (CNNs), powerful tools that form the backbone of most modern computer vision systems. Similarly, Section 9 and Section 10 introduce recurrent neural networks (RNNs), models that exploit sequential (e.g., temporal) structure in data and are commonly used for natural language processing and time series prediction. In Section 11, we introduce a relatively new class of models based on so-called attention mechanisms that has displaced RNNs as the dominant architecture for most natural language processing tasks. These sections will bring you up to speed on the most powerful and general tools that are widely used by deep learning practitioners.
Part 3: Scalability, Efficiency, and Applications. In Section 12, we discuss several common optimization algorithms used to train deep learning models. Next, in Section 13, we examine several key factors that influence the computational performance of deep learning code. Then, in Section 14, we illustrate major applications of deep learning in computer vision. Finally, in Section 15 and Section 16, we demonstrate how to pretrain language representation models and apply them to natural language processing tasks. This part is available online.
Navigational hashtags: #armknowledgesharing #armbooks #armcourses
General hashtags: #deeplearning #dl #tensorflow #pytorch #jax #numpy #computervision #naturallanguageprocessing #attention #neuralnetworks #algorithms
@data_science_weekly
- Interactive deep learning book with code, maths, and discussions.
- Implemented with PyTorch, NumPy/MXNet, JAX, and TensorFlow.
- Adopted at 400 universities from 60 countries.
Content and Structure
The book can be divided into roughly three parts, focusing on preliminaries, deep learning techniques, and advanced topics focused on real systems and applications:
Part 1: Basics and Preliminaries. Section 1 offers an introduction to deep learning. Then, in Section 2, we quickly bring you up to speed on the prerequisites required for hands-on deep learning, such as how to store and manipulate data, and how to apply various numerical operations based on basic concepts from linear algebra, calculus, and probability. Section 3 and Section 5 cover the most basic concepts and techniques in deep learning, including regression and classification; linear models; multilayer perceptrons; and overfitting and regularization.
Part 2: Modern Deep Learning Techniques. Section 6 describes the key computational components of deep learning systems and lays the groundwork for our subsequent implementations of more complex models. Next, Section 7 and Section 8 introduce convolutional neural networks (CNNs), powerful tools that form the backbone of most modern computer vision systems. Similarly, Section 9 and Section 10 introduce recurrent neural networks (RNNs), models that exploit sequential (e.g., temporal) structure in data and are commonly used for natural language processing and time series prediction. In Section 11, we introduce a relatively new class of models based on so-called attention mechanisms that has displaced RNNs as the dominant architecture for most natural language processing tasks. These sections will bring you up to speed on the most powerful and general tools that are widely used by deep learning practitioners.
Part 3: Scalability, Efficiency, and Applications. In Section 12, we discuss several common optimization algorithms used to train deep learning models. Next, in Section 13, we examine several key factors that influence the computational performance of deep learning code. Then, in Section 14, we illustrate major applications of deep learning in computer vision. Finally, in Section 15 and Section 16, we demonstrate how to pretrain language representation models and apply them to natural language processing tasks. This part is available online.
Navigational hashtags: #armknowledgesharing #armbooks #armcourses
General hashtags: #deeplearning #dl #tensorflow #pytorch #jax #numpy #computervision #naturallanguageprocessing #attention #neuralnetworks #algorithms
@data_science_weekly
👍1
CS 329S: Machine Learning Systems Design
This course aims to provide an iterative framework for developing real-world machine learning systems that are deployable, reliable, and scalable.
It starts by considering all stakeholders of each machine learning project and their objectives. Different objectives require different design choices, and this course will discuss the tradeoffs of those choices.
Students will learn about data management, data engineering, feature engineering, approaches to model selection, training, scaling, how to continually monitor and deploy changes to ML systems, as well as the human side of ML projects such as team structure and business metrics.
Link: https://stanford-cs329s.github.io/index.html#overview
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #mlsystemdesign #systemdesign #machinelearningsystemdesign #machinelearning #algorithms #design #architecture #engineering #software
@data_science_weekly
This course aims to provide an iterative framework for developing real-world machine learning systems that are deployable, reliable, and scalable.
It starts by considering all stakeholders of each machine learning project and their objectives. Different objectives require different design choices, and this course will discuss the tradeoffs of those choices.
Students will learn about data management, data engineering, feature engineering, approaches to model selection, training, scaling, how to continually monitor and deploy changes to ML systems, as well as the human side of ML projects such as team structure and business metrics.
Link: https://stanford-cs329s.github.io/index.html#overview
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #mlsystemdesign #systemdesign #machinelearningsystemdesign #machinelearning #algorithms #design #architecture #engineering #software
@data_science_weekly
Основы алгоритмов
С помощью этого хендбука вы научитесь проектировать, оптимизировать, комбинировать и отлаживать алгоритмы — причём без привязки к какому-либо языку программирования. Кроме теории мы собрали и практические задания разного уровня сложности, а также подготовили систему автоматической проверки эффективности алгоритмов — всё это поможет вам закрепить и отточить новые навыки.
Link: https://academy.yandex.ru/handbook/algorithms
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #algorithms #datastructures #datastructuresandalgorithms #python
@data_science_weekly
С помощью этого хендбука вы научитесь проектировать, оптимизировать, комбинировать и отлаживать алгоритмы — причём без привязки к какому-либо языку программирования. Кроме теории мы собрали и практические задания разного уровня сложности, а также подготовили систему автоматической проверки эффективности алгоритмов — всё это поможет вам закрепить и отточить новые навыки.
Link: https://academy.yandex.ru/handbook/algorithms
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #algorithms #datastructures #datastructuresandalgorithms #python
@data_science_weekly
education.yandex.ru
Основы алгоритмов — Хендбук от Яндекс Образования
С точки зрения проектирования и реализации алгоритмов есть много хороших, ставших классическими, учебных пособий, в нашем хендбуке мы пробуем подходить к выявлению общих принципов через решение задач.
👍1
Algorithmic concepts by Afshine Amidi and Shervine Amidi
This guide is a concise and illustrated guide for anyone who wants to brush up on their fundamentals in the context of coding interviews, computer science classes or to satisfy their own curiosity.
It is divided into 4 parts
- Foundations: main types of algorithms and related mathematical concepts
- Data structures: arrays, strings, queues, stacks, hash tables, linked lists and associated theorems and tricks
- Graphs and trees: graph concepts and graph traversal algorithms along with important types of trees
- Sorting and search: common, efficient sorting and search algorithms
Link
https://superstudy.guide/algorithms-data-structures/foundations/algorithmic-concepts
Navigational hashtags: #armknowledgesharing #armtutorials
General hashtags: #algorithms #datastructures #datastructuresandalgorithms #graphs #trees #sorting #search
@data_science_weekly
This guide is a concise and illustrated guide for anyone who wants to brush up on their fundamentals in the context of coding interviews, computer science classes or to satisfy their own curiosity.
It is divided into 4 parts
- Foundations: main types of algorithms and related mathematical concepts
- Data structures: arrays, strings, queues, stacks, hash tables, linked lists and associated theorems and tricks
- Graphs and trees: graph concepts and graph traversal algorithms along with important types of trees
- Sorting and search: common, efficient sorting and search algorithms
Link
https://superstudy.guide/algorithms-data-structures/foundations/algorithmic-concepts
Navigational hashtags: #armknowledgesharing #armtutorials
General hashtags: #algorithms #datastructures #datastructuresandalgorithms #graphs #trees #sorting #search
@data_science_weekly
Machine Learning for Everyone. In simple words. With real-world examples. Yes, again.
Machine Learning is like sex in high school. Everyone is talking about it, a few know what to do, and only your teacher is doing it. If you ever tried to read articles about machine learning on the Internet, most likely you stumbled upon two types of them: thick academic trilogies filled with theorems (I couldn’t even get through half of one) or fishy fairytales about artificial intelligence, data-science magic, and jobs of the future.
A simple introduction for those who always wanted to understand machine learning. Only real-world problems, practical solutions, simple language, and no high-level theorems. One and for everyone. Whether you are a programmer or a manager.
Link: https://vas3k.com/blog/machine_learning/
Navigational hashtags: #armknowledgesharing #armarticles
General hashtags: #ml #machinelearning #data #features #algorithms #classification #regression #neuralnets #deeplearning #dl #supervised #unsupervised
@data_science_weekly
Machine Learning is like sex in high school. Everyone is talking about it, a few know what to do, and only your teacher is doing it. If you ever tried to read articles about machine learning on the Internet, most likely you stumbled upon two types of them: thick academic trilogies filled with theorems (I couldn’t even get through half of one) or fishy fairytales about artificial intelligence, data-science magic, and jobs of the future.
A simple introduction for those who always wanted to understand machine learning. Only real-world problems, practical solutions, simple language, and no high-level theorems. One and for everyone. Whether you are a programmer or a manager.
Link: https://vas3k.com/blog/machine_learning/
Navigational hashtags: #armknowledgesharing #armarticles
General hashtags: #ml #machinelearning #data #features #algorithms #classification #regression #neuralnets #deeplearning #dl #supervised #unsupervised
@data_science_weekly
Harvard CS50 (2023) – Full Computer Science University Course
This is CS50, Harvard University’s introduction to the intellectual enterprises of computer science and the art of programming, for concentrators and non-concentrators alike, with or without prior programming experience. (Two thirds of CS50 students have never taken CS before.) This course teaches you how to solve problems, both with and without code, with an emphasis on correctness, design, and style. Topics include computational thinking, abstraction, algorithms, data structures, and computer science more generally. Problem sets inspired by the arts, humanities, social sciences, and sciences. More than teach you how to program in one language, this course teaches you how to program fundamentally and how to teach yourself new languages ultimately. The course starts with a traditional but omnipresent language called C that underlies today’s newer languages, via which you’ll learn not only about functions, variables, conditionals, loops, and more, but also about how computers themselves work underneath the hood, memory and all. The course then transitions to Python, a higher-level language that you’ll understand all the more because of C. Toward term’s end, the course introduces SQL, via which you can store data in databases, along with HTML, CSS, and JavaScript, via which you can create web and mobile apps alike. Course culminates in a final project.
Course Contents
⌨️ Lecture 0 - Scratch
⌨️ Lecture 1 - C
⌨️ Lecture 2 - Arrays
⌨️ Lecture 3 - Algorithms
⌨️ Lecture 4 - Memory
⌨️ Lecture 5 - Data Structures
⌨️ Lecture 6 - Python
⌨️ Lecture 7 - SQL
⌨️ Lecture 8 - HTML, CSS, JavaScript
⌨️ Lecture 9 - Flask
⌨️ Lecture 10 - Emoji
⌨️ Cybersecurity
Links:
- https://cs50.harvard.edu/x
- https://www.youtube.com/watch?v=LfaMVlDaQ24
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #cs #computerscience #harvard #algorithms #datastructures #datastructuresandalgorithms #python #sql #C #arrays
@data_science_weekly
This is CS50, Harvard University’s introduction to the intellectual enterprises of computer science and the art of programming, for concentrators and non-concentrators alike, with or without prior programming experience. (Two thirds of CS50 students have never taken CS before.) This course teaches you how to solve problems, both with and without code, with an emphasis on correctness, design, and style. Topics include computational thinking, abstraction, algorithms, data structures, and computer science more generally. Problem sets inspired by the arts, humanities, social sciences, and sciences. More than teach you how to program in one language, this course teaches you how to program fundamentally and how to teach yourself new languages ultimately. The course starts with a traditional but omnipresent language called C that underlies today’s newer languages, via which you’ll learn not only about functions, variables, conditionals, loops, and more, but also about how computers themselves work underneath the hood, memory and all. The course then transitions to Python, a higher-level language that you’ll understand all the more because of C. Toward term’s end, the course introduces SQL, via which you can store data in databases, along with HTML, CSS, and JavaScript, via which you can create web and mobile apps alike. Course culminates in a final project.
Course Contents
⌨️ Lecture 0 - Scratch
⌨️ Lecture 1 - C
⌨️ Lecture 2 - Arrays
⌨️ Lecture 3 - Algorithms
⌨️ Lecture 4 - Memory
⌨️ Lecture 5 - Data Structures
⌨️ Lecture 6 - Python
⌨️ Lecture 7 - SQL
⌨️ Lecture 8 - HTML, CSS, JavaScript
⌨️ Lecture 9 - Flask
⌨️ Lecture 10 - Emoji
⌨️ Cybersecurity
Links:
- https://cs50.harvard.edu/x
- https://www.youtube.com/watch?v=LfaMVlDaQ24
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #cs #computerscience #harvard #algorithms #datastructures #datastructuresandalgorithms #python #sql #C #arrays
@data_science_weekly
👍2
Exceptional Resources for Data Science Interview Preparation. Part 1: Live Coding
In this article, we will understand what a live coding interview is and how to prepare for it.
This blog-post will primarily be useful to Data Scientists and ML engineers, while some sections, for example, Algorithms and Data Structures, will be suitable for all IT specialists who will have to go through the live coding section.
Table of contents
- Preparing for an Algorithmic Interview
- Resources
- Algorithms and Data Structures
- Programming in Python
- Solving a Practical Data Science Problem
- Hybrid
- Learning How to Learn
- Let’s sum it up
- What’s next?
NB:
I'm the author of the article.
It was initially published in Russian (on habr.com), then I added additional resources in English to make up for deleting resources in Russian language and published it on medium.com.
So, for Russian speakers I recommend to read Russian version, for English speakers I recommend to read English version and both will benefit from starring the repository, which will be maintained and updated when new resources become available.
Links:
- Medium (eng)
- Habr (rus)
Navigational hashtags: #armknowledgesharing #armarticles
General hashtags: #interview #interviewpreparation #livecoding #leetcode #algorithms #algorithmsdatastructures #datastructures #python #sql #kaggle
@data_science_weekly
In this article, we will understand what a live coding interview is and how to prepare for it.
This blog-post will primarily be useful to Data Scientists and ML engineers, while some sections, for example, Algorithms and Data Structures, will be suitable for all IT specialists who will have to go through the live coding section.
Table of contents
- Preparing for an Algorithmic Interview
- Resources
- Algorithms and Data Structures
- Programming in Python
- Solving a Practical Data Science Problem
- Hybrid
- Learning How to Learn
- Let’s sum it up
- What’s next?
NB:
I'm the author of the article.
It was initially published in Russian (on habr.com), then I added additional resources in English to make up for deleting resources in Russian language and published it on medium.com.
So, for Russian speakers I recommend to read Russian version, for English speakers I recommend to read English version and both will benefit from starring the repository, which will be maintained and updated when new resources become available.
Links:
- Medium (eng)
- Habr (rus)
Navigational hashtags: #armknowledgesharing #armarticles
General hashtags: #interview #interviewpreparation #livecoding #leetcode #algorithms #algorithmsdatastructures #datastructures #python #sql #kaggle
@data_science_weekly
👍8
Introduction To Algorithms by MIT
This is an introductory course covering elementary data structures (dynamic arrays, heaps, balanced binary search trees, hash tables) and algorithmic approaches to solve classical problems (sorting, graph searching, dynamic programming). Introduction to mathematical modeling of computational problems, as well as common algorithms, algorithmic paradigms, and data structures used to solve these problems. Emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems.
Link: Direct Link
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #algorithms #datastructures #mit
@data_science_weekly
This is an introductory course covering elementary data structures (dynamic arrays, heaps, balanced binary search trees, hash tables) and algorithmic approaches to solve classical problems (sorting, graph searching, dynamic programming). Introduction to mathematical modeling of computational problems, as well as common algorithms, algorithmic paradigms, and data structures used to solve these problems. Emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems.
Link: Direct Link
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #algorithms #datastructures #mit
@data_science_weekly
👍3
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)
- Advanced Algorithms (course)
Navigational hashtags: #armknowledgesharing #armsites #armtutorials
General hashtags: #leetcode #python #algorithms #datastructures #interviewpreparation #technicalinterview
@data_science_weekly
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
@data_science_weekly
👍3
Data Structures & Algorithms by Google
Familiarize yourself with common data structures and algorithms such as lists, trees, maps, graphs, Big-O analysis, and more!
Topics:
- Maps/Dictionaries
- Linked Lists
- Trees
- Stacks & Queues
- Heaps
- Graphs
- Runtime Analysis
- Searching & Sorting
- Recursion & DP
Link: Site
Navigational hashtags: #armknowledgesharing #armtutorials
General hashtags: #algorithms #leetcode #programming
@data_science_weekly
Familiarize yourself with common data structures and algorithms such as lists, trees, maps, graphs, Big-O analysis, and more!
Topics:
- Maps/Dictionaries
- Linked Lists
- Trees
- Stacks & Queues
- Heaps
- Graphs
- Runtime Analysis
- Searching & Sorting
- Recursion & DP
Link: Site
Navigational hashtags: #armknowledgesharing #armtutorials
General hashtags: #algorithms #leetcode #programming
@data_science_weekly
👍7
Grokking Algorithms. An illustrated guide for programmers and other curious people by Aditya Y. Bhargava
Grokking Algorithms is a friendly take on this core computer science topic. In it, you'll learn how to apply common algorithms to the practical programming problems you face every day.
You'll start with tasks like sorting and searching. As you build up your skills, you'll tackle more complex problems like data compression and artificial intelligence. Each carefully presented example includes helpful diagrams and fully annotated code samples in Python.
By the end of this book, you will have mastered widely applicable algorithms as well as how and when to use them.
Table of Contents:
1. Introduction to algorithms
2. Selection sort
3. Recursion
4. Quicksort
5. Hash tables
6. Breadth-first search
7. Dijkstras algorithm
8. Greedy algorithms
9. Dynamic programming
10. K-nearest neighbors
Link: Book
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #algorithms #datastructures #leetcode
@data_science_weekly
Grokking Algorithms is a friendly take on this core computer science topic. In it, you'll learn how to apply common algorithms to the practical programming problems you face every day.
You'll start with tasks like sorting and searching. As you build up your skills, you'll tackle more complex problems like data compression and artificial intelligence. Each carefully presented example includes helpful diagrams and fully annotated code samples in Python.
By the end of this book, you will have mastered widely applicable algorithms as well as how and when to use them.
Table of Contents:
1. Introduction to algorithms
2. Selection sort
3. Recursion
4. Quicksort
5. Hash tables
6. Breadth-first search
7. Dijkstras algorithm
8. Greedy algorithms
9. Dynamic programming
10. K-nearest neighbors
Link: Book
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #algorithms #datastructures #leetcode
@data_science_weekly
👍1
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
@data_science_weekly
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
@data_science_weekly
👍5