Kaggle Learn
Kaggle not only allows you to participate in Data Science competitions, but also provides access to its courses.
Each course focuses on a particular topic and has several lessons. Passing them is not difficult and does not take much time (several hours), but the courses are interesting and allow you to remember the basics (and maybe learn something new for yourself).
It is convenient that for each course it is indicated which courses you need to take before studying this particular course and which after.
I've finished Python, Intro to Machine Learning, Intermediate Machine Learning when they were introduced. Now I'm going through Machine Learning Explainability.
Some of the must-have courses:
- Feature Engineering (https://lnkd.in/e7xF_9-Z)
- Time Series (https://lnkd.in/eA-cYvHi)
- Data Cleaning (https://lnkd.in/edCfAkat)
- Intro to AI Ethics (https://lnkd.in/eBiT2YHM)
- Machine Learning Explainability (https://lnkd.in/eTnCWkFD)
The courses are free.
There are also guides, like Natural Language Processing Guide
https://www.kaggle.com/learn-guide/natural-language-processing
#armknowledgesharing #armcourses
#kaggle #python #machinelearning #datascience
@data_science_weekly
Kaggle not only allows you to participate in Data Science competitions, but also provides access to its courses.
Each course focuses on a particular topic and has several lessons. Passing them is not difficult and does not take much time (several hours), but the courses are interesting and allow you to remember the basics (and maybe learn something new for yourself).
It is convenient that for each course it is indicated which courses you need to take before studying this particular course and which after.
I've finished Python, Intro to Machine Learning, Intermediate Machine Learning when they were introduced. Now I'm going through Machine Learning Explainability.
Some of the must-have courses:
- Feature Engineering (https://lnkd.in/e7xF_9-Z)
- Time Series (https://lnkd.in/eA-cYvHi)
- Data Cleaning (https://lnkd.in/edCfAkat)
- Intro to AI Ethics (https://lnkd.in/eBiT2YHM)
- Machine Learning Explainability (https://lnkd.in/eTnCWkFD)
The courses are free.
There are also guides, like Natural Language Processing Guide
https://www.kaggle.com/learn-guide/natural-language-processing
#armknowledgesharing #armcourses
#kaggle #python #machinelearning #datascience
@data_science_weekly
Kaggle
Learn Python, Data Viz, Pandas & More | Tutorials | Kaggle
Practical data skills you can apply immediately: that's what you'll learn in these no-cost courses. They're the fastest (and most fun) way to become a data scientist or improve your current skills.
Python & ML tasks
Задачи по Python и машинному обучению
Today I want to share with you a telegram channel which will help you retain your knowledge of python and maybe learn something new.
Every day a question is posted and you can answer it using the quiz under the question.
If your answer is wrong, you can find out the correct one and read the explanation.
#armknowledgesharing #armtelegram #python
@data_science_weekly
Задачи по Python и машинному обучению
Today I want to share with you a telegram channel which will help you retain your knowledge of python and maybe learn something new.
Every day a question is posted and you can answer it using the quiz under the question.
If your answer is wrong, you can find out the correct one and read the explanation.
#armknowledgesharing #armtelegram #python
@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
Welcome!
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…
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…
Efficient Python Tricks and Tools for Data Scientists
"Why efficient Python? Because using Python more efficiently will make your code more readable and run more efficiently.
Why for data scientist? Because Python has a wide application. The Python tools used in the data science field are not necessarily useful for other fields, such as web development.
The goal of this book is to spread the awareness of efficient ways to do Python.
They include:
- efficient methods and libraries to work with iterator, dictionary, function, and class
- efficient methods to work with popular data science libraries such as pandas and NumPy
- efficient tools to incorporate in a data science project
- efficient tools to incorporate in any project
- efficient tools to work with Jupyter Notebook."
About The Author
Khuyen Tran wrote over 150 data science articles with 100k+ views per month on Towards Data Science. She also wrote 500+ daily data science tips at Data Science Simplified. Her current mission is to make open-source more accessible to the data science community.
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #python #pandas #datascientists #datascientist #datamanagement #datamining #pythonprogramminglanguage #datascience #jupyternotebook
@data_science_weekly
"Why efficient Python? Because using Python more efficiently will make your code more readable and run more efficiently.
Why for data scientist? Because Python has a wide application. The Python tools used in the data science field are not necessarily useful for other fields, such as web development.
The goal of this book is to spread the awareness of efficient ways to do Python.
They include:
- efficient methods and libraries to work with iterator, dictionary, function, and class
- efficient methods to work with popular data science libraries such as pandas and NumPy
- efficient tools to incorporate in a data science project
- efficient tools to incorporate in any project
- efficient tools to work with Jupyter Notebook."
About The Author
Khuyen Tran wrote over 150 data science articles with 100k+ views per month on Towards Data Science. She also wrote 500+ daily data science tips at Data Science Simplified. Her current mission is to make open-source more accessible to the data science community.
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #python #pandas #datascientists #datascientist #datamanagement #datamining #pythonprogramminglanguage #datascience #jupyternotebook
@data_science_weekly
Geographic Data Science with Python
This book provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data. Social media, new forms of data, and new computational techniques are revolutionizing social science. In the new world of pervasive, large, frequent, and rapid data, we have new opportunities to understand and analyse the role of geography in everyday life. This book provides the first comprehensive curriculum in geographic data science.
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #machinelearning #datascience #geospatial #geospatialdata #geographic #python #data #science
@data_science_weekly
This book provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data. Social media, new forms of data, and new computational techniques are revolutionizing social science. In the new world of pervasive, large, frequent, and rapid data, we have new opportunities to understand and analyse the role of geography in everyday life. This book provides the first comprehensive curriculum in geographic data science.
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #machinelearning #datascience #geospatial #geospatialdata #geographic #python #data #science
@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.
Learn PyTorch for Deep Learning: Zero to Mastery
Welcome to the second-best place on the internet to learn PyTorch (the first being the PyTorch documentation).
This is the online book version of the Learn PyTorch for Deep Learning: Zero to Mastery course.
This course will teach you the foundations of machine learning and deep learning with PyTorch (a machine learning framework written in Python).
The course is video based. However, the videos are based on the contents of this online book.
Links:
- https://www.learnpytorch.io/
- https://github.com/mrdbourke/pytorch-deep-learning
- https://zerotomastery.io/courses/learn-pytorch/
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #deeplearning #machinelearning #python #computervision #transferlearning #classification #modeldeployment #pytorch #torch
@data_science_weekly
Welcome to the second-best place on the internet to learn PyTorch (the first being the PyTorch documentation).
This is the online book version of the Learn PyTorch for Deep Learning: Zero to Mastery course.
This course will teach you the foundations of machine learning and deep learning with PyTorch (a machine learning framework written in Python).
The course is video based. However, the videos are based on the contents of this online book.
Links:
- https://www.learnpytorch.io/
- https://github.com/mrdbourke/pytorch-deep-learning
- https://zerotomastery.io/courses/learn-pytorch/
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #deeplearning #machinelearning #python #computervision #transferlearning #classification #modeldeployment #pytorch #torch
@data_science_weekly
Neural Networks: Zero to Hero by Andrej Karpathy
A course by Andrej Karpathy on building neural networks, from scratch, in code.
"We start with the basics of backpropagation and build up to modern deep neural networks, like GPT. In my opinion language models are an excellent place to learn deep learning, even if your intention is to eventually go to other areas like computer vision because most of what you learn will be immediately transferable. This is why we dive into and focus on language models."
Prerequisites:
- Solid programming (Python)
- Intro-level math (e.g. derivative, gaussian).
Current Syllabus:
- The spelled-out intro to neural networks and backpropagation: building micrograd
- The spelled-out intro to language modeling: building makemore
- Building makemore Part 2: MLP
- Building makemore Part 3: Activations & Gradients, BatchNorm
- Building makemore Part 4: Becoming a Backprop Ninja
- Building makemore Part 5: Building a WaveNet
- Let's build GPT: from scratch, in code, spelled out.
- ongoing...
Links:
- https://karpathy.ai/zero-to-hero.html
- https://github.com/karpathy/nn-zero-to-hero/tree/master
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #deeplearning #mlp #batchnorm #backprop #gpt #fromscratch #neuralnetworks #python
@data_science_weekly
A course by Andrej Karpathy on building neural networks, from scratch, in code.
"We start with the basics of backpropagation and build up to modern deep neural networks, like GPT. In my opinion language models are an excellent place to learn deep learning, even if your intention is to eventually go to other areas like computer vision because most of what you learn will be immediately transferable. This is why we dive into and focus on language models."
Prerequisites:
- Solid programming (Python)
- Intro-level math (e.g. derivative, gaussian).
Current Syllabus:
- The spelled-out intro to neural networks and backpropagation: building micrograd
- The spelled-out intro to language modeling: building makemore
- Building makemore Part 2: MLP
- Building makemore Part 3: Activations & Gradients, BatchNorm
- Building makemore Part 4: Becoming a Backprop Ninja
- Building makemore Part 5: Building a WaveNet
- Let's build GPT: from scratch, in code, spelled out.
- ongoing...
Links:
- https://karpathy.ai/zero-to-hero.html
- https://github.com/karpathy/nn-zero-to-hero/tree/master
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #deeplearning #mlp #batchnorm #backprop #gpt #fromscratch #neuralnetworks #python
@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
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
What the f*ck Python! 😱
Python, being a beautifully designed high-level and interpreter-based programming language, provides us with many features for the programmer's comfort. But sometimes, the outcomes of a Python snippet may not seem obvious at first sight.
Here's a fun project attempting to explain what exactly is happening under the hood for some counter-intuitive snippets and lesser-known features in Python.
While some of the examples you see below may not be WTFs in the truest sense, but they'll reveal some of the interesting parts of Python that you might be unaware of. I find it a nice way to learn the internals of a programming language, and I believe that you'll find it interesting too!
If you're an experienced Python programmer, you can take it as a challenge to get most of them right in the first attempt. You may have already experienced some of them before, and I might be able to revive sweet old memories of yours! 😅
Links:
- Interactive Website
- Interactive Notebook
- GitHub Version:
- ENG
- RUS
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #python #programming #coding
@data_science_weekly
Python, being a beautifully designed high-level and interpreter-based programming language, provides us with many features for the programmer's comfort. But sometimes, the outcomes of a Python snippet may not seem obvious at first sight.
Here's a fun project attempting to explain what exactly is happening under the hood for some counter-intuitive snippets and lesser-known features in Python.
While some of the examples you see below may not be WTFs in the truest sense, but they'll reveal some of the interesting parts of Python that you might be unaware of. I find it a nice way to learn the internals of a programming language, and I believe that you'll find it interesting too!
If you're an experienced Python programmer, you can take it as a challenge to get most of them right in the first attempt. You may have already experienced some of them before, and I might be able to revive sweet old memories of yours! 😅
Links:
- Interactive Website
- Interactive Notebook
- GitHub Version:
- ENG
- RUS
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #python #programming #coding
@data_science_weekly
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
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
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
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