Über den Autor
prateek joshi is a medical graduate from university college of medical sciences, new delhi. a voracious reader, poetry for him is a means of release from the everyday jargon. his style of writing remarks upon self-examination - an objective assay of experiences.
Klappentext
100 recipes that teach you how to perform various machine learning tasks in the real worldnKey Featuresnn Understand which algorithms to use in a given context with the help of this exciting recipe-based guidenn Learn about perceptrons and see how they are used to build neural networksnn Stuck while making sense of images, text, speech, and real estate? This guide will come to your rescue, showing you how to perform machine learning for each one of these using various techniquesnnnnnnBook DeillegalscriptionnnMachine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more.nnWith this book, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you'll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.nnYou'll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.nnWhat you will learnnnn Explore classification algorithms and apply them to the income bracket estimation problemnn Use predictive modeling and apply it to real-world problemsnn Understand how to perform market segmentation using unsupervised learningnn Explore data visualization techniques to interact with your data in diverse waysnn Find out how to build a recommendation enginenn Understand how to interact with text data and build models to analyze itnn Work with speech data and recognize spoken words using Hidden Markov Modelsnn Analyze stock market data using Conditional Random Fieldsnn Work with image data and build systems for image recognition and biometric face recognitionnn Grasp how to use deep neural networks to build an optical character recognition system
