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

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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.

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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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CS109 Data Science
By Harvard University

⌛️ 12 weeks
✅ Video lectures
✅ Slides
✅ Lab exercises

🔗 http://cs109.github.io/2015/pages/videos.html

Note: i have issues with first video link but others are fine.

#datascience #python #harvard
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Visualize data on Google Maps Platform

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#Data_Science #Google_Map #Data_Visualization
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Python for Data Science: A Beginner’s Guide

Python is a programmer darling for plenty of reasons: the language is easy to read and work with, relatively simple to learn, and popular enough that there’s a great community and plenty of resources available.
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