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
The author of the channel is Valerii Babushkin. He is a Vice President (Data Science) at Blockchain.com.
He writes about Machine Learning, Deep Learning, AB Tests, Article Reviews, Job Interviews.
He has his own YouTube channel, and you can also search for videos and podcasts with him.
Telegram (rus version)
Youtube channel
#armknowledgesharing #armtelegram
@data_science_weekly
He writes about Machine Learning, Deep Learning, AB Tests, Article Reviews, Job Interviews.
He has his own YouTube channel, and you can also search for videos and podcasts with him.
Telegram (rus version)
Youtube channel
#armknowledgesharing #armtelegram
@data_science_weekly
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
Applying Machine Learning by Eugene Yan
"Applying machine learning is hard. Many organizations have yet to benefit from ML, and most teams still find it tricky to apply it effectively.
Though there are many ML courses, most focus on theory and students finish without knowing how to apply ML. Practical know-how is gained via hands-on experience and seldom documented—it's hard to find it in a textbook, class, or tutorial. There's a gap between knowing ML vs. applying it at work.
To fill this gap, ApplyingML collects tacit/tribal/ghost knowledge on applying ML via curated papers/blogs, guides, and interviews with ML practitioners. In a nutshell, it's 1/3 applied-ml, 1/3 ghost knowledge, and 1/3 Tim Ferriss Show. The intent is to make it easier to apply—and benefit from—ML at work."
Actually, the site contains 3 types of resources:
- Guides (teardowns, ml guides, non-ml guides)
- Interviews with machine learning practitioners
- Papers (curated list divided by topics)
Site: https://applyingml.com/
Personal site of Eugene Yan: https://eugeneyan.com/
#armknowledgesharing #armarticles
#machinelearning #ml #experience #production #datascience #blogs
@data_science_weekly
"Applying machine learning is hard. Many organizations have yet to benefit from ML, and most teams still find it tricky to apply it effectively.
Though there are many ML courses, most focus on theory and students finish without knowing how to apply ML. Practical know-how is gained via hands-on experience and seldom documented—it's hard to find it in a textbook, class, or tutorial. There's a gap between knowing ML vs. applying it at work.
To fill this gap, ApplyingML collects tacit/tribal/ghost knowledge on applying ML via curated papers/blogs, guides, and interviews with ML practitioners. In a nutshell, it's 1/3 applied-ml, 1/3 ghost knowledge, and 1/3 Tim Ferriss Show. The intent is to make it easier to apply—and benefit from—ML at work."
Actually, the site contains 3 types of resources:
- Guides (teardowns, ml guides, non-ml guides)
- Interviews with machine learning practitioners
- Papers (curated list divided by topics)
Site: https://applyingml.com/
Personal site of Eugene Yan: https://eugeneyan.com/
#armknowledgesharing #armarticles
#machinelearning #ml #experience #production #datascience #blogs
@data_science_weekly
Applyingml
ApplyingML - Papers, Guides, and Interviews with ML practitioners
Curated papers and blogs, ghost knowledge, and interviews with experienced ML practitioners on how to apply machine learning in industry.
👍1
CS229: Machine Learning
It is time to remember the basics!
This course provides a broad introduction to machine learning and statistical pattern recognition.
Topics include:
- Supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines);
- Unsupervised learning (clustering, dimensionality reduction, kernel methods);
- Learning theory (bias/variance tradeoffs, practical advice);
- Reinforcement learning and adaptive control.
The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.
Links:
- Lecture videos
- Lecture notes
- Course materials
- Main page for the course
- Cheatsheets
Navigational tags: #armknowledgesharing #armcourses
General tags: #machinelearning #supervisedlearning #neuralnetworks #svm #unsupervisedlearning #clustering #kernel #kernel #bias #variance #tradeoff #reinforcementlearning #cheatsheet #data #learning #patternrecognition #datamining
@data_science_weekly
It is time to remember the basics!
This course provides a broad introduction to machine learning and statistical pattern recognition.
Topics include:
- Supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines);
- Unsupervised learning (clustering, dimensionality reduction, kernel methods);
- Learning theory (bias/variance tradeoffs, practical advice);
- Reinforcement learning and adaptive control.
The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.
Links:
- Lecture videos
- Lecture notes
- Course materials
- Main page for the course
- Cheatsheets
Navigational tags: #armknowledgesharing #armcourses
General tags: #machinelearning #supervisedlearning #neuralnetworks #svm #unsupervisedlearning #clustering #kernel #kernel #bias #variance #tradeoff #reinforcementlearning #cheatsheet #data #learning #patternrecognition #datamining
@data_science_weekly
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
The Linux command line for beginners
The Linux command line is a text interface to your computer. Often referred to as the shell, terminal, console, prompt or various other names, it can give the appearance of being complex and confusing to use. Yet the ability to copy and paste commands from a website, combined with the power and flexibility the command line offers, means that using it may be essential when trying to follow instructions online, including many on this very website!
This tutorial will teach you a little of the history of the command line, then walk you through some practical exercises to become familiar with a few basic commands and concepts. We’ll assume no prior knowledge, but by the end we hope you’ll feel a bit more comfortable the next time you’re faced with some instructions that begin “Open a terminal”.
What you’ll learn
- A little history of the command line
- How to access the command line from your own computer
- How to perform some basic file manipulation
- A few other useful commands
- How to chain commands together to make more powerful tools
- The best way to use administrator powers
What you’ll need
- A computer running Ubuntu or some other version of Linux
Bonus Links:
- The Art of Command Line: https://github.com/jlevy/the-art-of-command-line
- Mind Map of Linux Commands: https://xmind.app/m/WwtB/
Navigational hashtags: #armknowledgesharing #armtutorials
General hashtags: #linux #terminal #shell #console #prompt #unix #bash
@data_science_weekly
The Linux command line is a text interface to your computer. Often referred to as the shell, terminal, console, prompt or various other names, it can give the appearance of being complex and confusing to use. Yet the ability to copy and paste commands from a website, combined with the power and flexibility the command line offers, means that using it may be essential when trying to follow instructions online, including many on this very website!
This tutorial will teach you a little of the history of the command line, then walk you through some practical exercises to become familiar with a few basic commands and concepts. We’ll assume no prior knowledge, but by the end we hope you’ll feel a bit more comfortable the next time you’re faced with some instructions that begin “Open a terminal”.
What you’ll learn
- A little history of the command line
- How to access the command line from your own computer
- How to perform some basic file manipulation
- A few other useful commands
- How to chain commands together to make more powerful tools
- The best way to use administrator powers
What you’ll need
- A computer running Ubuntu or some other version of Linux
Bonus Links:
- The Art of Command Line: https://github.com/jlevy/the-art-of-command-line
- Mind Map of Linux Commands: https://xmind.app/m/WwtB/
Navigational hashtags: #armknowledgesharing #armtutorials
General hashtags: #linux #terminal #shell #console #prompt #unix #bash
@data_science_weekly
Ubuntu
The Linux command line for beginners | Ubuntu
Ubuntu is an open source software operating system that runs from the desktop, to the cloud, to all your internet connected things.
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 229 ― Machine Learning Cheatsheet
Set of illustrated Machine Learning cheatsheets covering the content of the CS 229 class.
They can (hopefully!) be useful to all future students of this course, as well as to anyone else interested in Machine Learning.
Navigational hashtags: #armknowledgesharing #armcheetsheets
General hashtags: #machinelearning #students #content #supervisedlearning #unsupervisedlearning #deeplearning #tips #tricks #statistics #probability #calculus
@data_science_weekly
Set of illustrated Machine Learning cheatsheets covering the content of the CS 229 class.
They can (hopefully!) be useful to all future students of this course, as well as to anyone else interested in Machine Learning.
Navigational hashtags: #armknowledgesharing #armcheetsheets
General hashtags: #machinelearning #students #content #supervisedlearning #unsupervisedlearning #deeplearning #tips #tricks #statistics #probability #calculus
@data_science_weekly
stanford.edu
Teaching - CS 229
Teaching page of Shervine Amidi, Graduate Student at Stanford University.
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
Artem Ryblov’s Data Science Weekly pinned «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…»
Statistics and Probability (Khan Academy)
Learn statistics and probability for free - everything you'd want to know about descriptive and inferential statistics:
Unit 1: Analysing categorical data
Unit 2: Displaying and comparing quantitative data
Unit 3: Summarizing quantitative data
Unit 4: Modelling data distributions
Unit 5: Exploring bivariate numerical data
Unit 6: Study design
Unit 7: Probability
Unit 8: Counting, permutations, and combinations
Unit 9: Random variables
Unit 10: Sampling distributions
Unit 11: Confidence intervals
Unit 12: Significance tests (hypothesis testing)
Unit 13: Two-sample inference for the difference between groups
Unit 14: Inference for categorical data (chi-square tests)
Unit 15: Advanced regression (inference and transforming)
Unit 16: Analysis of variance (ANOVA)
Link: https://www.khanacademy.org/math/statistics-probability
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #statistics #testing #design #data #abtesting #abtest #probability #ttest
@data_science_weekly
Learn statistics and probability for free - everything you'd want to know about descriptive and inferential statistics:
Unit 1: Analysing categorical data
Unit 2: Displaying and comparing quantitative data
Unit 3: Summarizing quantitative data
Unit 4: Modelling data distributions
Unit 5: Exploring bivariate numerical data
Unit 6: Study design
Unit 7: Probability
Unit 8: Counting, permutations, and combinations
Unit 9: Random variables
Unit 10: Sampling distributions
Unit 11: Confidence intervals
Unit 12: Significance tests (hypothesis testing)
Unit 13: Two-sample inference for the difference between groups
Unit 14: Inference for categorical data (chi-square tests)
Unit 15: Advanced regression (inference and transforming)
Unit 16: Analysis of variance (ANOVA)
Link: https://www.khanacademy.org/math/statistics-probability
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #statistics #testing #design #data #abtesting #abtest #probability #ttest
@data_science_weekly
SQL Academy - SQL Interactive Course
A comprehensive SQL course designed to change the way you think about SQL forever. Together we will walk the path to understand how this language works and gain all the necessary skills to use it effectively at work.
Module 0 - Introduction
In this short module, we'll take a look at how this course's platform works and learn how to get the most out of it. And also get information about our community.
Module 1- Fundamentals
This module is designed to give you a basic understanding of databases and fill in potential gaps. Also in this module, we will get acquainted with the terminology of relational DBMS.
Module 2 - Basis of selection I
In this module we will learn how to write our first SQL queries, deal with such important concepts as conditional selection, sorting and data grouping.
Module 3 - Basis of selection II
We continue to write increasingly complex select queries: we learn how to get data from several tables, write subqueries, and get acquainted with a common table expression.
Module 4 - Data manipulation
In the previous modules, we learned how to write select-only queries, it's time to fool around more seriously: we get acquainted with adding, updating, and deleting records.
Module 5 - Databases and tables
It's time to work not only with ready-made databases, but also learn how to create your own.
Links:
- https://sql-academy.org/en
- https://sql-academy.org/en/trainer?sort=byId
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #sql #data #databases #database #tutorial #guide #onlinetraining #simulator
@data_science_weekly
A comprehensive SQL course designed to change the way you think about SQL forever. Together we will walk the path to understand how this language works and gain all the necessary skills to use it effectively at work.
Module 0 - Introduction
In this short module, we'll take a look at how this course's platform works and learn how to get the most out of it. And also get information about our community.
Module 1- Fundamentals
This module is designed to give you a basic understanding of databases and fill in potential gaps. Also in this module, we will get acquainted with the terminology of relational DBMS.
Module 2 - Basis of selection I
In this module we will learn how to write our first SQL queries, deal with such important concepts as conditional selection, sorting and data grouping.
Module 3 - Basis of selection II
We continue to write increasingly complex select queries: we learn how to get data from several tables, write subqueries, and get acquainted with a common table expression.
Module 4 - Data manipulation
In the previous modules, we learned how to write select-only queries, it's time to fool around more seriously: we get acquainted with adding, updating, and deleting records.
Module 5 - Databases and tables
It's time to work not only with ready-made databases, but also learn how to create your own.
Links:
- https://sql-academy.org/en
- https://sql-academy.org/en/trainer?sort=byId
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #sql #data #databases #database #tutorial #guide #onlinetraining #simulator
@data_science_weekly
SQL Academy
Interactive Online SQL Course — SQL Academy
Interactive online SQL course with exercises and tasks for writing SQL queries in MySQL. Perfect for beginner analysts, developers, and testers!
The System Design Primer. Learn how to design large-scale systems.
Learning how to design scalable systems will help you become a better engineer.
System design is a broad topic. There is a vast amount of resources scattered throughout the web on system design principles.
This repo is an organized collection of resources to help you learn how to build systems at scale.
Link: https://github.com/donnemartin/system-design-primer#the-system-design-primer
Navigational hashtags: #armknowledgesharing #armrepo
General hashtags: #systemdesign #softwareengineering #softwaredevelopment #engineer #learning #design #help
@data_science_weekly
Learning how to design scalable systems will help you become a better engineer.
System design is a broad topic. There is a vast amount of resources scattered throughout the web on system design principles.
This repo is an organized collection of resources to help you learn how to build systems at scale.
Link: https://github.com/donnemartin/system-design-primer#the-system-design-primer
Navigational hashtags: #armknowledgesharing #armrepo
General hashtags: #systemdesign #softwareengineering #softwaredevelopment #engineer #learning #design #help
@data_science_weekly
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
MACHINE LEARNING QUESTIONS
Bnomial publishes one machine learning question every day. It aims to teach you something new, one question at a time:
- The questions are practical.
- The answers are well explained, with a proper clarification of why the option is correct and why it is not.
- Reading resources are provided so one can learn more to clarify the topic.
Link: https://today.bnomial.com/
Navigational hashtags: #armknowledgesharing #armnewsletters
General hashtags: #machinelearning #deeplearning #ai #statistics #datascience #dataanalytics
@data_science_weekly
Bnomial publishes one machine learning question every day. It aims to teach you something new, one question at a time:
- The questions are practical.
- The answers are well explained, with a proper clarification of why the option is correct and why it is not.
- Reading resources are provided so one can learn more to clarify the topic.
Link: https://today.bnomial.com/
Navigational hashtags: #armknowledgesharing #armnewsletters
General hashtags: #machinelearning #deeplearning #ai #statistics #datascience #dataanalytics
@data_science_weekly
R2D3 is an experiment in expressing statistical thinking with interactive design.
The site contains several guides:
- A VISUAL INTRODUCTION TO MACHINE LEARNING
- Part 1: A Decision Tree
- Part 2: Bias and Variance
- MISC
- Design in a World where Machines are Learning
- Making Sense of COVID-19
Basically, they try to explain complex concepts using intuitive graphics.
Link: https://www.r2d3.us/
Navigational hashtags: #armknowledgesharing #armtutorials
General hashtags: #machinelearning #covid #learning #design #decisiontrees #bias #variance #visualization #eda
@data_science_weekly
The site contains several guides:
- A VISUAL INTRODUCTION TO MACHINE LEARNING
- Part 1: A Decision Tree
- Part 2: Bias and Variance
- MISC
- Design in a World where Machines are Learning
- Making Sense of COVID-19
Basically, they try to explain complex concepts using intuitive graphics.
Link: https://www.r2d3.us/
Navigational hashtags: #armknowledgesharing #armtutorials
General hashtags: #machinelearning #covid #learning #design #decisiontrees #bias #variance #visualization #eda
@data_science_weekly
The Most Comprehensive List of Kaggle Solutions and Ideas
This is a list of almost all available solutions and ideas shared by top performers in the past Kaggle competitions. This list gets updated as soon as a new competition finishes.
Link: https://farid.one/kaggle-solutions/
Navigational hashtags: #armknowledgesharing #armkaggle
General hashtags: #kaggle #datascience #machinelearning #competitions
@data_science_weekly
This is a list of almost all available solutions and ideas shared by top performers in the past Kaggle competitions. This list gets updated as soon as a new competition finishes.
Link: https://farid.one/kaggle-solutions/
Navigational hashtags: #armknowledgesharing #armkaggle
General hashtags: #kaggle #datascience #machinelearning #competitions
@data_science_weekly