Intro to Machine Learning
by Kaggle
Learn the core ideas in machine learning, and build your first models.

1 How Models Work
The first step if you're new to machine learning.

2 Basic Data Exploration
Load and understand your data.

3 Your First Machine Learning Model
Building your first model. Hurray!

4 Model Validation
Measure the performance of your model, so you can test and compare alternatives.

5 Underfitting and Overfitting
Fine-tune your model for better performance.

6 Random Forests
Using a more sophisticated machine learning algorithm.

7 Machine Learning Competitions
Enter the world of machine learning competitions to keep improving and see your progress.

🔗 Course link

#machinelearning #ml
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The R Programming For Data Science A-Z Complete Diploma 2022
Rating ⭐️: 4.5 out of 5
Students 👨‍🎓: 38,584
Duration ⏰: 5h 6min
🔗 Course link

Free for first 1000 enrollments

Knowledge Graphs Course
Data Models, Knowledge Acquisition, Inference and Applications
Department of Computer Science, Stanford University, Spring 2021

⏳10 weeks, each week has slides and video lessons 📽

https://web.stanford.edu/class/cs520/

#datascience #machinelearning #tensorflow #scikitlearn #keras

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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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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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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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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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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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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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