Pro Machine Learning Algorithms A Hands-On Approach to Implementing Algorithms in Python and R

ISBN: 1484235630

Category: Uncategorized

Tag: Database/SQL


Posted on 2018-08-23, by nokia241186.

Description




Pro Machine Learning Algorithms: A Hands-On Approach to Implementing Algorithms in Python and R by V Kishore Ayyadevara
English | PDF,EPUB | 2018 | 379 Pages | ISBN : 1484235630 | 45.49 MB
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.
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