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