PRO MACHINE LEARNING ALGORITHMS IBD

Sinopsis

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 youáget a practical understanding ofáall 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.áYou 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.áYou will see the theory along with case studies, such asásentiment classification, fraud detection, recommender systems, and image recognition,áso 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.áWhat You Will LearnGet an in-depth understanding of all the major machine learning and deep learning algorithmsáFully appreciate the pitfalls to avoid while building modelsImplement machine learning algorithms in the cloudáFollow 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 ForBusiness analysts/ IT professionals who want to transition into data science roles. Data scientists who want to solidify their knowledge in machine learning.