This item is printed on demand - it takes 3-4 days longer - Neuware -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 youget a practical understanding ofall 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 assentiment 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.What You Will LearnGet an in-depth understanding of all the major machine learning and deep learning algorithmsFully appreciate the pitfalls to avoid while building modelsImplement machine learning algorithms in the cloudFollow a hands-on approach through case studies for each algorithmGain the tricks of ensemble learning to build more accurate modelsDiscover the basics of programming in R/Python and the Keras framework for deep learningWho This Book Is ForBusiness analysts/ IT professionals who want to transition into data science roles. Data scientists who want to solidify their knowledge in machine learning. 396 pp. Englisch.

N� de ref. del art�culo 9781484235638

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Detalles bibliogr�ficos

T�tulo: Pro Machine Learning Algorithms
Editor: Apress Jul 2018
Fecha de publicaci�n: 2018
Idioma: Ingl�s
ISBN 10: 1484235630
ISBN 13: 9781484235638
Encuadernaci�n: Taschenbuch
Estado: Neu

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Sinopsis

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.

What You Will Learn

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

Who This Book Is For

Business analysts/ IT professionals who want to transition into data science roles. Data scientists who want to solidify their knowledge in machine learning.

Acerca del autor

V Kishore Ayyadevara currently leads retail analytics consulting in a start-up. He received his MBA from IIM Calcutta. Following that, he worked for American Express in risk management and in Amazon's supply chain analytics teams. He is passionate about leveraging data to make informed decisions - faster and more accurately. Kishore's interests include identifying business problems that can be solved using data, simplifying the complexity within data science and applying data science to achieve quantifiable business results.

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BuchWeltWeit Ludwig Meier e.K.
Germany

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