Forwarded from Cool GitHub repositories
data-science
This is a path for those of you who want to complete the Data Science undergraduate curriculum on your own time, for free, with courses from the best universities in the World.
Creator: ossu
Stars ⭐️: 14.5k
Forked By: 2.6k
GithubRepo:https://github.com/ossu/data-science
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This is a path for those of you who want to complete the Data Science undergraduate curriculum on your own time, for free, with courses from the best universities in the World.
Creator: ossu
Stars ⭐️: 14.5k
Forked By: 2.6k
GithubRepo:https://github.com/ossu/data-science
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GitHub
GitHub - ossu/data-science: 📊 Path to a free self-taught education in Data Science!
📊 Path to a free self-taught education in Data Science! - ossu/data-science
When to Choose CatBoost Over XGBoost or LightGBM [Practical Guide]
Boosting algorithms have become one of the most powerful algorithms for training on structural (tabular) data.
I have been working with these 3 for years, even my bachelor thesis was comparison of these 3 algorithms alongside AdaBoost. This article explains when to use CatBoost over other ones.
https://neptune.ai/blog/when-to-choose-catboost-over-xgboost-or-lightgbm
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Boosting algorithms have become one of the most powerful algorithms for training on structural (tabular) data.
I have been working with these 3 for years, even my bachelor thesis was comparison of these 3 algorithms alongside AdaBoost. This article explains when to use CatBoost over other ones.
https://neptune.ai/blog/when-to-choose-catboost-over-xgboost-or-lightgbm
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neptune.ai
When to Choose CatBoost Over XGBoost or LightGBM [Practical Guide]
Compare CatBoost with XGBoost and LightGBM in performance and speed; a practical guide to gradient boosting selection.
Data Analysis free courses
The Analytics Edge (Spring 2017)
by MIT
🎬 193 video lessons
⏰ 16 hours worth of material
🔗 Courses link
Statistics and data literacy for non-statisticians
Rating ⭐️: 4.7 out of 5
Students 👨🎓: 13,320
Duration ⏰: 1h 36min
Teacher: Mike X Cohen
🔗 Courses link
Data Analysis with Python courses
by freeCodeCamp
[
Data Analysis with Python
🎬 28 video lessons
Numpy
🎬 9 video lessons
Data Analysis with Python Projects
🔖 5 projects
🔗 Courses link
]
Data Analysis w/ Python 3 and Pandas
by sentdex
🎬 6 video lessons
⏰ 2-3 hours worth of material
🔗 Course link
Master Data Analysis with Python - Intro to Pandas 2022
Rating ⭐️: 4.6 out of 5
Students 👨🎓: 3,828
Duration ⏰: 1hr 49min
Teacher: Ted Petrou
🔗 Courses link
Learn to code for data analysis
by OpenLearn
⏳ 8 weeks
🔗 Course link
Lecture notes from Statistical Thinking and Data Analysis
by MIT
🔗 Notes link
Python for Data Analysis
Rating ⭐️: 4.2 out of 5
Students 👨🎓: 14,168
Duration ⏰: 1h 10min
Teacher: Bob Wakefield
🔗 Courses link
Prepare data for analysis
by Microsoft
📁2 modules
Get data in Power BI - 12 Units
Clean, transform, and load data in Power BI - 10 Units
Duration ⏰: 3 hr 26 min
🔗 Course link
NOC:Data Analysis and Decision Making - I, IIT Kanpur
NOC:Data Analysis & Decision Making - II, IIT Kanpur
NOC:Data Analysis & Decision Making - III, IIT Kanpur
👨🏫 Prof. Raghunandan Sengupta
Each of 3 parts lasts ⏳12 weeks!
#datanalysis #dataanalysis #datascience #powerbi #dataanalytics
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The Analytics Edge (Spring 2017)
by MIT
🎬 193 video lessons
⏰ 16 hours worth of material
🔗 Courses link
Statistics and data literacy for non-statisticians
Rating ⭐️: 4.7 out of 5
Students 👨🎓: 13,320
Duration ⏰: 1h 36min
Teacher: Mike X Cohen
🔗 Courses link
Data Analysis with Python courses
by freeCodeCamp
[
Data Analysis with Python
🎬 28 video lessons
Numpy
🎬 9 video lessons
Data Analysis with Python Projects
🔖 5 projects
🔗 Courses link
]
Data Analysis w/ Python 3 and Pandas
by sentdex
🎬 6 video lessons
⏰ 2-3 hours worth of material
🔗 Course link
Master Data Analysis with Python - Intro to Pandas 2022
Rating ⭐️: 4.6 out of 5
Students 👨🎓: 3,828
Duration ⏰: 1hr 49min
Teacher: Ted Petrou
🔗 Courses link
Learn to code for data analysis
by OpenLearn
⏳ 8 weeks
🔗 Course link
Lecture notes from Statistical Thinking and Data Analysis
by MIT
🔗 Notes link
Python for Data Analysis
Rating ⭐️: 4.2 out of 5
Students 👨🎓: 14,168
Duration ⏰: 1h 10min
Teacher: Bob Wakefield
🔗 Courses link
Prepare data for analysis
by Microsoft
📁2 modules
Get data in Power BI - 12 Units
Clean, transform, and load data in Power BI - 10 Units
Duration ⏰: 3 hr 26 min
🔗 Course link
NOC:Data Analysis and Decision Making - I, IIT Kanpur
NOC:Data Analysis & Decision Making - II, IIT Kanpur
NOC:Data Analysis & Decision Making - III, IIT Kanpur
👨🏫 Prof. Raghunandan Sengupta
Each of 3 parts lasts ⏳12 weeks!
#datanalysis #dataanalysis #datascience #powerbi #dataanalytics
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Sorry I haven't forwarded it earlier, this post belongs to this channel as well. 👆
Python Machine Learning (3rd Ed.) Code Repository
Paperback: 770 pages
Publisher: Packt Publishing
Language: English
https://github.com/rasbt/python-machine-learning-book-3rd-edition
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Paperback: 770 pages
Publisher: Packt Publishing
Language: English
https://github.com/rasbt/python-machine-learning-book-3rd-edition
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GitHub
GitHub - rasbt/python-machine-learning-book-3rd-edition: The "Python Machine Learning (3rd edition)" book code repository
The "Python Machine Learning (3rd edition)" book code repository - rasbt/python-machine-learning-book-3rd-edition
Few Numpy Tutorials
Python NumPy Tutorial – Learn NumPy Arrays With Examples
https://www.edureka.co/blog/python-numpy-tutorial/
Python Numpy Tutorial (with Jupyter and Colab)
https://cs231n.github.io/python-numpy-tutorial/
NumPy fundamentals (official docs)
https://numpy.org/doc/stable/user/basics.html
#numpy
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Python NumPy Tutorial – Learn NumPy Arrays With Examples
https://www.edureka.co/blog/python-numpy-tutorial/
Python Numpy Tutorial (with Jupyter and Colab)
https://cs231n.github.io/python-numpy-tutorial/
NumPy fundamentals (official docs)
https://numpy.org/doc/stable/user/basics.html
#numpy
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Edureka
Python Numpy Tutorial | Learn Numpy Arrays With Examples | Edureka
This python numpy tutorial blog includes all the basics of Python, its various operations, special functions and why it is preferred over the list.
Top 8 Github Repos to Learn Data Science and Python
1. All algorithms implemented in Python
By: The Algorithms
Stars ⭐️: 135K
Fork: 35.3K
Repo: https://github.com/TheAlgorithms/Python
2. DataScienceResources
By: jJonathan Bower
Stars ⭐️: 3K
Fork: 1.3K
Repo: https://github.com/jonathan-bower/DataScienceResources
3. Playground and Cheatsheet for Learning Python
By: Oleksii Trekhleb ( Also the Image)
Stars ⭐️: 12.5K
Fork: 2K
Repo: https://github.com/trekhleb/learn-python
4. Learn Python 3
By: Jerry Pussinen
Stars ⭐️: 4,8K
Fork: 1,4K
Repo: https://github.com/jerry-git/learn-python3
5. Awesome Data Science
By: Fatih Aktürk, Hüseyin Mert & Osman Ungur, Recep Erol.
Stars ⭐️: 18.4K
Fork: 5K
Repo: https://github.com/academic/awesome-datascience
6. data-scientist-roadmap
By: MrMimic
Stars ⭐️: 5K
Fork: 1.5K
Repo: https://github.com/MrMimic/data-scientist-roadmap
7. Data Science Best Resources
By: Tirthajyoti Sarkar
Stars ⭐️: 1.8K
Fork: 717
Repo: https://github.com/tirthajyoti/Data-science-best-resources/blob/master/README.md
8. Ds-cheatsheets
By: Favio André Vázquez
Stars ⭐️: 10.4K
Fork: 3.1K
Repo: https://github.com/FavioVazquez/ds-cheatsheets
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*This channel belongs to @bigdataspecialist group
1. All algorithms implemented in Python
By: The Algorithms
Stars ⭐️: 135K
Fork: 35.3K
Repo: https://github.com/TheAlgorithms/Python
2. DataScienceResources
By: jJonathan Bower
Stars ⭐️: 3K
Fork: 1.3K
Repo: https://github.com/jonathan-bower/DataScienceResources
3. Playground and Cheatsheet for Learning Python
By: Oleksii Trekhleb ( Also the Image)
Stars ⭐️: 12.5K
Fork: 2K
Repo: https://github.com/trekhleb/learn-python
4. Learn Python 3
By: Jerry Pussinen
Stars ⭐️: 4,8K
Fork: 1,4K
Repo: https://github.com/jerry-git/learn-python3
5. Awesome Data Science
By: Fatih Aktürk, Hüseyin Mert & Osman Ungur, Recep Erol.
Stars ⭐️: 18.4K
Fork: 5K
Repo: https://github.com/academic/awesome-datascience
6. data-scientist-roadmap
By: MrMimic
Stars ⭐️: 5K
Fork: 1.5K
Repo: https://github.com/MrMimic/data-scientist-roadmap
7. Data Science Best Resources
By: Tirthajyoti Sarkar
Stars ⭐️: 1.8K
Fork: 717
Repo: https://github.com/tirthajyoti/Data-science-best-resources/blob/master/README.md
8. Ds-cheatsheets
By: Favio André Vázquez
Stars ⭐️: 10.4K
Fork: 3.1K
Repo: https://github.com/FavioVazquez/ds-cheatsheets
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*This channel belongs to @bigdataspecialist group
GitHub
GitHub - TheAlgorithms/Python: All Algorithms implemented in Python
All Algorithms implemented in Python. Contribute to TheAlgorithms/Python development by creating an account on GitHub.
The Data Science Interview Study Guide
Preparing for a job interview can be a full-time job, and Data Science interviews are no different. Here are 121 resources that can help you study and quiz your way to landing your dream data science job.
https://www.kdnuggets.com/2020/01/data-science-interview-study-guide.html
Preparing for a job interview can be a full-time job, and Data Science interviews are no different. Here are 121 resources that can help you study and quiz your way to landing your dream data science job.
https://www.kdnuggets.com/2020/01/data-science-interview-study-guide.html
KDnuggets
The Data Science Interview Study Guide
Preparing for a job interview can be a full-time job, and Data Science interviews are no different. Here are 121 resources that can help you study and quiz your way to landing your dream data science job.
Machine Learning with Python : COMPLETE COURSE FOR BEGINNERS
Complete Machine Learning Course with Python for beginners
Rating⭐️: 4.6 out 5
Students 👨🎓 : 18533
Duration ⏰ : 13 hours on-demand video
Teacher 👨🏫: Prashant Mishra
🔗 Course link
I have noticed this one is currently free (but only for first 1000 enrols !!!) so I thought some of you might be interested 😊
#machinelearning #pythoncourses #python
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Complete Machine Learning Course with Python for beginners
Rating⭐️: 4.6 out 5
Students 👨🎓 : 18533
Duration ⏰ : 13 hours on-demand video
Teacher 👨🏫: Prashant Mishra
🔗 Course link
I have noticed this one is currently free (but only for first 1000 enrols !!!) so I thought some of you might be interested 😊
#machinelearning #pythoncourses #python
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Udemy
Machine Learning with Python : COMPLETE COURSE FOR BEGINNERS
Complete Machine Learning Course with Python for beginners
The Best Data Science Approaches In The Data Mining World
One of the best approaches in the data mining world is called the CRISP-DM. This means Cross Industry Standard Process for Data Mining. It describes the data project as having six phases.
1) Business Understanding
a) What is the business objectives and situation assessment
b) Determine the data mining goal and create a project plan
2) Data Understanding
a) Collect Initial data and describe data
b) Explore data and verify data quality
3) Data Preparation
a) Get,Select and Clean the data set
b) Construct and Integrate data
4) Modeling
a) Select model technique and generate test design
b) Build and access model
5) Evaluation
a) Evaluate and review process
b) Determine the next steps
6) Deployment
a) Plan deployment
b) Plan monitoring and maintenance
c) Produce final report and review project
One of the best approaches in the data mining world is called the CRISP-DM. This means Cross Industry Standard Process for Data Mining. It describes the data project as having six phases.
1) Business Understanding
a) What is the business objectives and situation assessment
b) Determine the data mining goal and create a project plan
2) Data Understanding
a) Collect Initial data and describe data
b) Explore data and verify data quality
3) Data Preparation
a) Get,Select and Clean the data set
b) Construct and Integrate data
4) Modeling
a) Select model technique and generate test design
b) Build and access model
5) Evaluation
a) Evaluate and review process
b) Determine the next steps
6) Deployment
a) Plan deployment
b) Plan monitoring and maintenance
c) Produce final report and review project
Matplotlib Cheat Sheet
Contains essential MatPlotLib guide from beginners to pro
Contains essential MatPlotLib guide from beginners to pro
OU2-Difference-Between-ML-DL-AI.pdf
224 KB
Difference between AI, ML and DL
Contains a simplified guide to understanding the terms AI, ML and DL
Contains a simplified guide to understanding the terms AI, ML and DL