Pro Machine Learning Algorithms

Description

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 LearnGet 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 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 For Business analysts/ IT professionals who want to transition into data science roles. Data scientists who want to solidify their knowledge in machine learning. Front Matter ....Pages i-xxi Basics of Machine Learning (V Kishore Ayyadevara)....Pages 1-15 Linear Regression (V Kishore Ayyadevara)....Pages 17-47 Logistic Regression (V Kishore Ayyadevara)....Pages 49-69 Decision Tree (V Kishore Ayyadevara)....Pages 71-103 Random Forest (V Kishore Ayyadevara)....Pages 105-116 Gradient Boosting Machine (V Kishore Ayyadevara)....Pages 117-134 Artificial Neural Network (V Kishore Ayyadevara)....Pages 135-165 Word2vec (V Kishore Ayyadevara)....Pages 167-178 Convolutional Neural Network (V Kishore Ayyadevara)....Pages 179-215 Recurrent Neural Network (V Kishore Ayyadevara)....Pages 217-257 Clustering (V Kishore Ayyadevara)....Pages 259-281 Principal Component Analysis (V Kishore Ayyadevara)....Pages 283-297 Recommender Systems (V Kishore Ayyadevara)....Pages 299-325 Implementing Algorithms in the Cloud (V Kishore Ayyadevara)....Pages 327-344 Back Matter ....Pages 345-372