Python Machine Learning: A Step-by-Step Journey with Scikit-Learn and Tensorflow for Beginners

Python Machine Learning: A Step-by-Step Journey with Scikit-Learn and Tensorflow for Beginners

Python Machine Learning: A Step-by-Step Journey with Scikit-Learn and Tensorflow for Beginners
Автор: Annable Chloe
Дата выхода: 2024
Издательство: Independent publishing
Количество страниц: 126
Размер файла: 1009,4 КБ
Тип файла: PDF
Добавил: codelibs
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INTRODUCTION
CHAPTER A1:
UNSUPERVISED AMACHINE ALEARNING
Principal AComponent AAnalysis
k-means AClustering
CHAPTER A2:
DEEP ABELIEF ANETWORKS
Neural ANetworks
The ARestricted ABoltzmann AMachine
Constructing ADeep ABelief ANetworks
CHAPTER A3:
CONVOLUTIONAL ANEURAL ANETWORKS
Understanding Athe AArchitecture
Connecting Athe APieces
CHAPTER A4:
STACKED ADENOISING AAUTOENCODERS
Autoencoders
CHAPTER A5:
SEMI-SUPERVISED ALEARNING
Understanding Athe ATechniques
Self-learning
Contrastive APessimistic ALikelihood AEstimation
CHAPTER A6:
TEXT AFEATURE AENGINEERING
Text AData ACleaning
Building AFeatures
CHAPTER A7:
MORE AFEATURE AENGINEERING
Creating AFeature ASets
Real-world AFeature AEngineering
CHAPTER A8:
ENSEMBLE AMETHODS
Averaging AEnsembles
Stacking AEnsembles
CONCLUSION

Are you a budding programmer eager to delve into the realm of Python Machine Learning?

Does the prospect of transitioning your existing programming knowledge to Python leave you perplexed?
Fear not! This comprehensive guide is tailored to address precisely those concerns and assist you in navigating through the intricacies of Python Machine Learning.

In "Python Machine Learning: A Comprehensive Beginner's Guide with Scikit-Learn and Tensorflow," you will embark on a journey to unravel the mysteries of:

  • Understanding the essence of machine learning

  • Harnessing the power of Scikit-Learn & Tensorflow

  • Grasping the significance of the 5 V's of Big Data

  • Delving into the world of neural networks using Scikit-Learn

  • Exploring the intersection of machine learning and the Internet of Things (IoT)

  • Implementing the KNN algorithm with precision

  • Deciphering the nuances of determining the "k" parameter

This book is crafted with beginners in mind, providing clear, step-by-step instructions and straightforward language, making it an ideal starting point for anyone intrigued by this captivating subject. Python, with its immense capabilities, opens up a world of possibilities, and this guide will set you on the path to harnessing its potential.


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