Pro Machine Learning Algorithms : A Hands-On Approach to Implementing Algorithms in Python and R / by V Kishore Ayyadevara.

Format:

Book

Author/Creator:

Ayyadevara, V Kishore., Author.

Language:

English

Subjects (All):

Artificial intelligence.

Python (Computer program language).

Big data.

Open source software.

Computer programming.

R (Computer program language).

Artificial Intelligence.

Python.

Big Data.

Open Source.

Local Subjects:

Artificial Intelligence.

Python.

Big Data.

Open Source.

Physical Description:

1 online resource (379 pages)

Edition:

1st ed. 2018.

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2018.

System Details:

text file

Summary:

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.

Contents:

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.

ISBN:

9781484235645

1484235649

OCLC:

1048573734