How to think feature engineering | Feature engineering tutorial | feature engineering explained
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Hello,
My name is Aman and I am a Data Scientist.
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About Unfold Data science: This channel is to help people understand the basics of data science through simple examples in an easy way. Anybody without prior knowledge of computer programming or statistics or machine learning and artificial intelligence can get an understanding of data science at a high level through this channel. The videos uploaded will not be very technical in nature and hence can be easily grasped by viewers from different backgrounds as well.
Book recommendation for Data Science:
Category 1 - Must Read For Every Data Scientist:
The Elements of Statistical Learning by Trevor Hastie - [ Ссылка ]
Python Data Science Handbook - [ Ссылка ]
Business Statistics By Ken Black - [ Ссылка ]
Hands-On Machine Learning with Scikit Learn, Keras, and TensorFlow by Aurelien Geron - [ Ссылка ]
Category 2 - Overall Data Science:
The Art of Data Science By Roger D. Peng - [ Ссылка ]
Predictive Analytics By By Eric Siegel - [ Ссылка ]
Data Science for Business By Foster Provost - [ Ссылка ]
Category 3 - Statistics and Mathematics:
Naked Statistics By Charles Wheelan - [ Ссылка ]
Practical Statistics for Data Scientist By Peter Bruce - [ Ссылка ]
Category 4 - Machine Learning:
Introduction to machine learning by Andreas C Muller - [ Ссылка ]
The Hundred Page Machine Learning Book by Andriy Burkov - [ Ссылка ]
Category 5 - Programming:
The Pragmatic Programmer by David Thomas - [ Ссылка ]
Clean Code by Robert C. Martin - [ Ссылка ]
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Watch python for data science playlist here:
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Watch End to End Implementation of a simple machine-learning model in Python here:
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Learn Ensemble Model, Bagging, and Boosting here:
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Build Career in Data Science Playlist:
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Artificial Neural Network and Deep Learning Playlist:
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Natural language Processing playlist:
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Understanding and building a recommendation system:
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