Data Science Interview Resource

Looking to ace that Data Science Interview?

This resource got you covered 👌

It covers a wide range of topics from machine learning and deep learning to probability and statistics , python and SQL .

Link

Happy Learning 😃

#deep_learning #machine_learning #Data_Science #interview

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Stanford Seminar on Machine Learning Explainability

Hey there ☺️. Are you interested in the Explainability of ML and everything going on around it from the basics to the latest research in it?

Here's a cool 😎 video from Stanford

Video Link



#stanford #ml

Reading Minds with AI: Researchers Translate Brain Waves to Images

This article discusses a new AI technology developed by researchers that is capable of translating brain waves into images. The technology utilizes machine learning algorithms to analyze the patterns in brain waves produced when a person is shown visual stimuli and convert them into digital images.

This breakthrough could potentially help individuals who have difficulty communicating, such as those with paralysis, to express their thoughts and feelings through images.

An Interesting Article 😉

WHAT'S ON YOUR MIND? I'M CURIOUS.🙂

Hey 👋 there!

I see that you have been enjoying the resources shared here so far.

In a bid to serve you better ☺️ I'm really interested in how best the resources here suit you?

Are there resources you have been unable to find here😊 or resources you wish you could get access to?😉

Kindly leave your thoughts here. If you have been enjoying it so far.

I'm glad 😊to know this and I wish you well in your learning journey.

Top 12 Interesting Careers to Explore in BIG DATA

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.

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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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group

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