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Pro Machine Learning Algorithms - A Hands-On Approach to Implementing Algorithms in Python and R (Paperback, 1st ed.)
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Pro Machine Learning Algorithms - A Hands-On Approach to Implementing Algorithms in Python and R (Paperback, 1st ed.)
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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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