Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data 1st Edition 29919
Many industry experts consider unsupervised learning the next in frontier artificial intelligence, one that may hold the key to the holy grail in AI research, the so called general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied; this is where unsupervised learning comes in. Unsupervised learning can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover.
Author Ankur Patel provides practical knowledge on how to apply unsupervised learning using two simple, production ready Python frameworks scikit learn and TensorFlow using Keras. With the hands on examples and code provided, you will identify difficult to find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started.
Author Ankur Patel provides practical knowledge on how to apply unsupervised learning using two simple, production ready Python frameworks scikit learn and TensorFlow using Keras. With the hands on examples and code provided, you will identify difficult to find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started.
- Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and punishment learning
- Set up and manage a machine learning project end to end everything from data acquisition to building a model and implementing a solution in production
- Use dimensionality reduction to algorithms uncover the most relevant information in data and build an anomaly detection system to catch credit card fraud
- Apply clustering to algorithms segment users such as loan borrowers into distinct and homogeneous groups
- Use autoencoders to perform automatic feature engineering and selection
- Combine supervised unsupervised learning and algorithms to develop semi supervised solutions
- Build movie recommender systems using restricted Boltzmann machines
- Generate synthetic images using deep belief networks and generative adversarial networks
- Perform clustering on time series data such as electrocardiograms
- Explore the successes of unsupervised learning to date and its promising future
- АвторAnkur A. Patel
- КатегоріяКомп'ютерна література
- МоваАнглійська
- Рік2019
- Сторінок362
- Формат170х240 мм
- ОбкладинкаМ'яка
- Тип паперуОфсетний
2517 ₴
Відділення Нова Пошта80 ₴
Поштомат Нова Пошта40 ₴
Кур’єр Нова Пошта120 ₴
Відділення УкрПошта50 ₴
Кур’єр за адресою90 ₴
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