3 common mistakes of newcomers in data science #shorts
3 common mistakes of newcomers in data science #shorts #unfolddatascience #datascience #analytics
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My name is Aman and I am a Data Scientist.
About Unfold Data science: This channel is to help people understand basics of data science through simple examples in easy way. Anybody without having prior knowledge of computer programming or statistics or machine learning and artificial intelligence can get an understanding of data science at high level through this channel. The videos uploaded will not be very technical in nature and hence it can be easily grasped by viewers from different background as well.
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Book recommendation for Data Science:
Category 1 – Must Read For Every Data Scientist:
The Elements of Statistical Learning by Trevor Hastie – https://amzn.to/37wMo9H
Python Data Science Handbook – https://amzn.to/31UCScm
Business Statistics By Ken Black – https://amzn.to/2LObAA5
Hands-On Machine Learning with Scikit Learn, Keras, and TensorFlow by Aurelien Geron – https://amzn.to/3gV8sO9
Ctaegory 2 – Overall Data Science:
The Art of Data Science By Roger D. Peng – https://amzn.to/2KD75aD
Predictive Analytics By By Eric Siegel – https://amzn.to/3nsQftV
Data Science for Business By Foster Provost – https://amzn.to/3ajN8QZ
Category 3 – Statistics and Mathematics:
Naked Statistics By Charles Wheelan – https://amzn.to/3gXLdmp
Practical Statistics for Data Scientist By Peter Bruce – https://amzn.to/37wL9Y5
Category 4 – Machine Learning:
Introduction to machine learning by Andreas C Muller – https://amzn.to/3oZ3X7T
The Hundred Page Machine Learning Book by Andriy Burkov – https://amzn.to/3pdqCxJ
Category 5 – Programming:
The Pragmatic Programmer by David Thomas – https://amzn.to/2WqWXVj
Clean Code by Robert C. Martin – https://amzn.to/3oYOdlt
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Watch Introduction to Data Science full playlist here : https://www.youtube.com/watch?v=Zkyog5u1OGw&list=PLmPJQXJiMoUWXbjyedFTmXPzzoeMJV4fe
Watch python for data science playlist here:
Watch statistics and mathematics playlist here :
Watch End to End Implementation of a simple machine learning model in Python here:
Learn Ensemble Model, Bagging and Boosting here:
Build Career in Data Science Playlist:
Artificial Neural Network and Deep Learning Playlist:
Natural langugae Processing playlist:
Understanding and building recommendation system:
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Except accuracy and r-squared measures…..on which factors we should justify our ML model ? PLS ANSWER!
Can you do a video explaining these 3 mistakes more?