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Applied Unsupervised Learning with Python. Discover hidden patterns and relationships in unstructured data with Python Benjamin Johnston, Aaron Jones, Christopher Kruger

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Applied Unsupervised Learning with Python. Discover hidden patterns and relationships in unstructured data with Python Benjamin Johnston, Aaron Jones, Christopher Kruger - okladka książki

Applied Unsupervised Learning with Python. Discover hidden patterns and relationships in unstructured data with Python Benjamin Johnston, Aaron Jones, Christopher Kruger - okladka książki

Autorzy:
Benjamin Johnston, Aaron Jones, Christopher Kruger
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
482
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Unsupervised learning is a useful and practical solution in situations where labeled data is not available.

Applied Unsupervised Learning with Python guides you in learning the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data. The book begins by explaining how basic clustering works to find similar data points in a set. Once you are well-versed with the k-means algorithm and how it operates, you’ll learn what dimensionality reduction is and where to apply it. As you progress, you’ll learn various neural network techniques and how they can improve your model. While studying the applications of unsupervised learning, you will also understand how to mine topics that are trending on Twitter and Facebook and build a news recommendation engine for users. Finally, you will be able to put your knowledge to work through interesting activities such as performing a Market Basket Analysis and identifying relationships between different products.

By the end of this book, you will have the skills you need to confidently build your own models using Python.

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O autorach książki

Benjamin Johnston zajmuje się zaawansowaną analizą danych w branży medycznej. Interesuje się uczeniem maszynowym, przetwarzaniem obrazów i sieciami neuronowymi.

Aaron Jones is a full-time senior data scientist and consultant. He has built models and data products while working in retail, media, and environmental science. Aaron is based in Seattle, Washington and has a particular interest in clustering algorithms, natural language processing, and Bayesian statistics.
Christopher Kruger is a practicing data scientist and AI researcher. He has managed applied machine learning projects across multiple industries while mentoring junior team members on best practices. His primary focus is on pushing both business practicality as well as academic rigor in every project. Chris is currently developing research in the computer vision space.

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