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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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

Learn to translate external data sources to graphics on maps.

✅ Free Online Course
🧱 4 modules
🎬 Video Lectures
🏃‍♂️ Self paced
📊 Lab: 1
🧮 Quiz
Source: Google

🔗 https://developers.google.com/learn/pathways/maps-visualize-data?hl=en

#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.
And if you needed one more reason to consider starting Python for beginners, it plays an important role in lucrative data careers as well! Learning Python for data science or data analysis will give you a variety of useful skills.

✅ Free Online Tutorial
🧱 8 modules
🏃‍♂️ Self paced
Source: learntocodewithme

🔗 Course Link


#Data_Science #python #Python_For_Data_Science
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Data Science With Python Workflow Cheat Sheet

Creator: business Science
Stars ⭐️: 75
Forked By: 38
https://github.com/business-science/cheatsheets/blob/master/Data_Science_With_Python_Workflow.pdf


#Data #Science #cheatSheet
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How do Transformers work?

All the Transformer models mentioned above (GPT, BERT, BART, T5, etc.) have been trained as language models. This means they have been trained on large amounts of raw text in a self-supervised fashion. Self-supervised learning is a type of training in which the objective is automatically computed from the inputs of the model. That means that humans are not needed to label the data!

This type of model develops a statistical understanding of the language it has been trained on, but it’s not very useful for specific practical tasks. Because of this, the general pretrained model then goes through a process called transfer learning. During this process, the model is fine-tuned in a supervised way — that is, using human-annotated labels — on a given task

🔗 Read More