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Pro machine learning algorithmsa han...

Ayyadevara, V Kishore.

Pro machine learning algorithmsa hands-on approach to implementing algorithms in Python and R /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Pro machine learning algorithmsby V Kishore Ayyadevara.
其他題名:	a hands-on approach to implementing algorithms in Python and R /
作者:	Ayyadevara, V Kishore.
出版者:	Berkeley, CA :Apress :2018.
面頁冊數:	xxi, 372 p. :digital ;24 cm.
Contained By:	Springer eBooks
標題:	Machine learning.
電子資源:	http://dx.doi.org/10.1007/978-1-4842-3564-5
ISBN:	9781484235645$q(electronic bk.)

Pro machine learning algorithmsa hands-on approach to implementing algorithms in Python and R /
Ayyadevara, V Kishore.

Pro machine learning algorithmsa hands-on approach to implementing algorithms in Python and R /[electronic resource] :by V Kishore Ayyadevara. - Berkeley, CA :Apress :2018. - xxi, 372 p. :digital ;24 cm.

Chapter 1: Basics of Machine Learning -- Chapter 2: Linear regression -- Chapter 3: Logistic regression -- Chapter 4: Decision tree -- Chapter 5: Random forest -- Chapter 6: GBM -- Chapter 7: Neural network -- Chapter 8: word2vec -- Chapter 9: Convolutional neural network -- Chapter 10: Recurrent Neural Network -- Chapter 11: Clustering -- Chapter 12: PCA -- Chapter 13: Recommender systems -- Chapter 14: Implementing algorithms in the cloud.

Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so that you get a practical understanding of all the levers that can be tuned in a model, before implementing the models in Python/R. You will cover all the major algorithms: supervised and unsupervised learning, which include linear/logistic regression; k-means clustering; PCA; recommender system; decision tree; random forest; GBM; and neural networks. You will also be exposed to the latest in deep learning through CNNs, RNNs, and word2vec for text mining. You will be learning not only the algorithms, but also the concepts of feature engineering to maximize the performance of a model. You will see the theory along with case studies, such as sentiment classification, fraud detection, recommender systems, and image recognition, so that you get the best of both theory and practice for the vast majority of the machine learning algorithms used in industry. Along with learning the algorithms, you will also be exposed to running machine-learning models on all the major cloud service providers. You are expected to have minimal knowledge of statistics/software programming and by the end of this book you should be able to work on a machine learning project with confidence. You will: Get an in-depth understanding of all the major machine learning and deep learning algorithms Fully appreciate the pitfalls to avoid while building models Implement machine learning algorithms in the cloud Follow a hands-on approach through case studies for each algorithm Gain the tricks of ensemble learning to build more accurate models Discover the basics of programming in R/Python and the Keras framework for deep learning.

ISBN: 9781484235645$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-3564-5doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .A993 2018

Dewey Class. No.: 006.31

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