Machine learning for developers : uplift your regular applications with the power of statistics, analytics, and machine learning

Your one-stop guide to becoming a Machine Learning expert. About This Book • Learn to develop efficient and intelligent applications by leveraging the power of Machine Learning • A highly practical guide explaining the concepts of problem solving in the easiest possible manner • Implement Machine Learning in the most practical way Who This Book Is For This book will appeal to any developer who wants to know what Machine Learning is and is keen to use Machine Learning to make their day-to-day apps fast, high performing, and accurate. Any developer who wants to enter the field of Machine Learning can effectively use this book as an entry point. What You Will Learn • Learn the math and mechanics of Machine Learning via a developer-friendly approach • Get to grips with widely used Machine Learning algorithms/techniques and how to use them to solve real problems • Get a feel for advanced concepts, using popular programming frameworks. • Prepare yourself and other developers for working in the new ubiquitous field of Machine Learning • Get an overview of the most well known and powerful tools, to solve computing problems using Machine Learning. • Get an intuitive and down-to-earth introduction to current Machine Learning areas, and apply these concepts on interesting and cutting-edge problems. In Detail Most of us have heard about the term Machine Learning, but surprisingly the question frequently asked by developers across the globe is, “How do I get started in Machine Learning?”. One reason could be attributed to the vastness of the subject area because people often get overwhelmed by the abstractness of ML and terms such as regression, supervised learning, probability density function, and so on. This book is a systematic guide teaching you how to implement various Machine Learning techniques and their day-to-day application and development. You will start with the very basics of data and mathematical models in easy-to-follow language that you are familiar with; you will feel at home while implementing the examples. The book will introduce you to various libraries and frameworks used in the world of Machine Learning, and then, without wasting any time, you will get to the point and implement Regression, Clustering, classification, Neural networks, and more with fun examples. As you get to grips with the techniques, you'll learn to implement those concepts to solve real-world scenarios for ML applications such as image analysis, Natural Language processing, and anomaly detections of time series data. By the end of the book, you will have learned various ML techniques to develop more efficient and intelligent applications. Style and approach This book gives you a glimpse of Machine Learning Models and the application of models at scale using clustering, classification, regression and reinforcement learning with fun examples. Hands-on examples will be presented to understand the power of problem solving with Machine Learning and Advanced architectures, software installation, and configuration. ""Cover"" ""Title Page"" ""Copyright"" ""Credits"" ""Foreword"" ""About the Author"" ""About the Reviewers"" ""www.PacktPub.com"" ""Customer Feedback"" ""Table of Contents"" ""Preface"" ""Chapter 1: Introduction -- Machine Learning and Statistical Science"" ""Machine learning in the bigger picture"" ""Types of machine learning"" ""Grades of supervision"" ""Supervised learning strategies -- regression versus classification"" ""Unsupervised problem solvingâ#x80 #x93 clustering"" ""Tools of the tradeâ#x80 #x93 programming language and libraries"" ""The Python language"" ""The NumPy library"" ""The matplotlib library""""What's matplotlib?"" ""Pandas"" ""SciPy"" ""Jupyter notebook"" ""Basic mathematical concepts"" ""Statistics -- the basic pillar of modeling uncertainty"" ""Descriptive statistics -- main operations"" ""Mean"" ""Variance"" ""Standard deviation"" ""Probability and random variables"" ""Events"" ""Probability"" ""Random variables and distributions"" ""Useful probability distributions"" ""Bernoulli distributions"" ""Uniform distribution"" ""Normal distribution"" ""Logistic distribution"" ""Statistical measures for probability functions"" ""Skewness"" ""Kurtosis""""Differential calculus elements"" ""Preliminary knowledge"" ""In search of changesâ#x80 #x93 derivatives"" ""Sliding on the slope"" ""Chain rule"" ""Partial derivatives"" ""Summary"" ""Chapter 2: The Learning Process"" ""Understanding the problem"" ""Dataset definition and retrieval"" ""The ETL process"" ""Loading datasets and doing exploratory analysis with SciPy and pandas"" ""Working interactively with IPython"" ""Working on 2D data"" ""Feature engineering"" ""Imputation of missing data"" ""One hot encoding"" ""Dataset preprocessing"" ""Normalization and feature scaling""""Normalization or standardization"" ""Model definition"" ""Asking ourselves the right questions"" ""Loss function definition"" ""Model fitting and evaluation"" ""Dataset partitioning"" ""Common training terms â#x80 #x93 iteration, batch, and epoch"" ""Types of training â#x80 #x93 online and batch processing"" ""Parameter initialization"" ""Model implementation and results interpretation"" ""Regression metrics"" ""Mean absolute error"" ""Median absolute error"" ""Mean squared error"" ""Classification metrics"" ""Accuracy"" ""Precision score, recall, and F-measure""""Confusion matrix"" ""Clustering quality measurements"" ""Silhouette coefficient"" ""Homogeneity, completeness, and V-measure"" ""Summary"" ""References"" ""Chapter 3: Clustering"" ""Grouping as a human activity"" ""Automating the clustering process"" ""Finding a common center -- K-means"" ""Pros and cons of K-means"" ""K-means algorithm breakdown"" ""K-means implementations"" ""Nearest neighbors"" ""Mechanics of K-NN"" ""Pros and cons of K-NN"" ""K-NN sample implementation"" ""Going beyond the basics"" ""The Elbow method""