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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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.
Click Here
⭐️ 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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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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GitHub
stanford-cs-229-machine-learning/en/cheatsheet-supervised-learning.pdf at master · afshinea/stanford-cs-229-machine-learning
VIP cheatsheets for Stanford's CS 229 Machine Learning - afshinea/stanford-cs-229-machine-learning
Data science/ML/AI
Photo
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.
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.
Approaching (Almost) Any Machine Learning Problem.pdf
8 MB
The "Approaching (Almost) Any Machine Learning Problem" book.
by 4x Kaggle grandmaster Abhishek Thakur
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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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
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
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
👩💻 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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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
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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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
cs109.github.io
Class Material
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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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
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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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
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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Join @bigdataspecialist for more
Learn the most important language for data science.
🎬 8 lessons
⏰ 5 hours
https://www.kaggle.com/learn/python
#python
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Join @bigdataspecialist for more
Kaggle
Learn Python Tutorials
Learn the most important language for data science.
Source codes for data science projects from my Instagram post:
https://www.instagram.com/p/CJwDIpCA0nc/
1. Build chatbots:
https://dzone.com/articles/python-chatbot-project-build-your-first-python-pro
2. Credit card fraud detection:
https://www.kaggle.com/renjithmadhavan/credit-card-fraud-detection-using-python
3. Fake news detection
https://data-flair.training/blogs/advanced-python-project-detecting-fake-news/
4.Driver Drowsiness Detection
https://data-flair.training/blogs/python-project-driver-drowsiness-detection-system/
5. Recommender Systems (Movie Recommendation)
https://data-flair.training/blogs/data-science-r-movie-recommendation/
6. Sentiment Analysis
https://data-flair.training/blogs/data-science-r-sentiment-analysis-project/
7. Gender Detection & Age Prediction
https://www.pyimagesearch.com/2020/04/13/opencv-age-detection-with-deep-learning/
#data_science #projects
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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
https://www.instagram.com/p/CJwDIpCA0nc/
1. Build chatbots:
https://dzone.com/articles/python-chatbot-project-build-your-first-python-pro
2. Credit card fraud detection:
https://www.kaggle.com/renjithmadhavan/credit-card-fraud-detection-using-python
3. Fake news detection
https://data-flair.training/blogs/advanced-python-project-detecting-fake-news/
4.Driver Drowsiness Detection
https://data-flair.training/blogs/python-project-driver-drowsiness-detection-system/
5. Recommender Systems (Movie Recommendation)
https://data-flair.training/blogs/data-science-r-movie-recommendation/
6. Sentiment Analysis
https://data-flair.training/blogs/data-science-r-sentiment-analysis-project/
7. Gender Detection & Age Prediction
https://www.pyimagesearch.com/2020/04/13/opencv-age-detection-with-deep-learning/
#data_science #projects
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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group