PANDAS FOR DATA SCIENCE

In this learning path, you’ll get started with pandas and get to know the ins and outs of how you can use it to analyze data with Python.

Pandas is a game-changer for data science and analytics, particularly if you came to Python because you were searching for something more powerful than Excel and VBA. It uses fast, flexible, and expressive data structures designed to make working with relational or labeled data both easy and intuitive.

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⭐️ 15 Best Machine Learning Cheat Sheet ⭐️

1- Supervised Learning

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf

2- Unsupervised Learning

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-unsupervised-learning.pdf

3- Deep Learning

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-deep-learning.pdf

4- Machine Learning Tips and Tricks

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-machine-learning-tips-and-tricks.pdf

5- Probabilities and Statistics

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf

6- Comprehensive Stanford Master Cheat Sheet

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/super-cheatsheet-machine-learning.pdf

7- Linear Algebra and Calculus

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf

8- Data Science Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf

9- Keras Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Keras_Cheat_Sheet_Python.pdf

10- Deep Learning with Keras Cheat Sheet

https://github.com/rstudio/cheatsheets/raw/master/keras.pdf

11- Visual Guide to Neural Network Infrastructures

http://www.asimovinstitute.org/wp-content/uploads/2016/09/neuralnetworks.png

12- Skicit-Learn Python Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf

13- Scikit-learn Cheat Sheet: Choosing the Right Estimator

https://scikit-learn.org/stable/tutorial/machine_learning_map/

14- Tensorflow Cheat Sheet

https://github.com/kailashahirwar/cheatsheets-ai/blob/master/PDFs/Tensorflow.pdf

15- Machine Learning Test Cheat Sheet

https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/

#machine_learning #deep_learning #scikit-learn #keras
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Hey,
of course, If i find nice graphical representation I will send you.

In the meantime I can tell you how have I used every of these algorithms at my work:

I used SVM (Support Vector Machines) for text and product classification (Some article belongs to sport category, some to business, medicine etc, similar with products, I used it to classify products into categories similar to what you have on Amazon.

I used KNN (K-Nearest Neighbors ) for simple classification problems, but generally we don't use it much in production as there are more advanced ones.

I used Regression to predict continuous value as price of product.

I used Random Forest (and Gradient boosting algorithms like LightGBM and XGBoost) for predicting possibility that person will convert on some ad (for example that person will buy a product advertised in an ad). Both Random Forest and Gradient Boosting are based on decision trees, they are very similar but gradient boosting is more advanced.

I used CNN (Convolutional Neural Network) for image recognition (finding patterns in images to recognize objects).

I haven't used RNN (Recurrent neural networks ) much but they are used for problems that are recursive by their nature. For example good usage of it in my work would be for some NLP tasks (sentences could be considered as recursive so its used on text and speech data). Also they are used to simulate neuron activity in our brain).

I used K-means for clusterization of articles or products into different unlabeled clusters. It helps to determine which articles/products are similar to each other.

I used PCA (Principal Component Analysis) to reduce number of dimensions for datasets that have too many of them. It also helped me to remove personal data from some datasets and model them as doubles (instead of names, surnames, date of birth etc).

I hope this helps. I will send this to main channel in case somebody else finds it useful.

The "Approaching (Almost) Any Machine Learning Problem" book.
by 4x Kaggle grandmaster Abhishek Thakur

Best Statistic books for data science

Practical statistics for data scientists
by Peter Bruce and Andrew Bruce
🔗 Book Link

Think Stats
by Allen B. Downey
🔗 Book Link

Computer Age Statistical Inference
by Bradley Efron and Trevor Hastie
🔗 Book Link

Statistics in Plain English
by Timothy C. Urdan
🔗 Book Link

#Statistics #books #data_science
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👩‍💻 5 FREE DATA SCIENCE COURSES FOR BEGINNERS 👩‍🏫


CS109 Data Science (Harvard) -
http://cs109.github.io/2015/pages/videos.html

Data-Driven Decision Making (PwC) -
https://www.coursera.org/learn/decision-making

Machine Learning (Stanford) -
https://www.coursera.org/learn/machine-learning

Data Science Foundations (IBM) -
https://cognitiveclass.ai/learn/data-science

Data Science Specialization (JHU) -
https://www.coursera.org/specializations/jhu-data-science

Subscribe for more helpful data science learning materials and free courses

#data_science
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Hey folks,
some of you probably already know that,
I have Instagram page where i share educational posts about data science and machine learning.
Your support in form of follow and possibly engagement on my posts would be very appreciated.

Instagram Page Link:
http://Instagram.com/bigdataspecialist

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Python course by kaggle
Learn the most important language for data science.

🎬 8 lessons
⏰ 5 hours


https://www.kaggle.com/learn/python

#python
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