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    Hands-On Markov Models with Python. Implement probabilistic models for learning complex data sequences using the Python ecosystem Ankur Ankan, Abinash Panda

    (ebook) (audiobook) (audiobook) Język publikacji: angielski
    Hands-On Markov Models with Python. Implement probabilistic models for learning complex data sequences using the Python ecosystem Ankur Ankan, Abinash Panda - okladka książki

    Hands-On Markov Models with Python. Implement probabilistic models for learning complex data sequences using the Python ecosystem Ankur Ankan, Abinash Panda - okladka książki

    Hands-On Markov Models with Python. Implement probabilistic models for learning complex data sequences using the Python ecosystem Ankur Ankan, Abinash Panda - audiobook MP3

    Hands-On Markov Models with Python. Implement probabilistic models for learning complex data sequences using the Python ecosystem Ankur Ankan, Abinash Panda - audiobook CD

    Autorzy:
    Ankur Ankan, Abinash Panda
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    178
    Dostępne formaty:
         PDF
         ePub
         Mobi
    Hidden Markov Model (HMM) is a statistical model based on the Markov chain concept. Hands-On Markov Models with Python helps you get to grips with HMMs and different inference algorithms by working on real-world problems. The hands-on examples explored in the book help you simplify the process flow in machine learning by using Markov model concepts, thereby making it accessible to everyone.

    Once you’ve covered the basic concepts of Markov chains, you’ll get insights into Markov processes, models, and types with the help of practical examples. After grasping these fundamentals, you’ll move on to learning about the different algorithms used in inferences and applying them in state and parameter inference. In addition to this, you’ll explore the Bayesian approach of inference and learn how to apply it in HMMs.
    In further chapters, you’ll discover how to use HMMs in time series analysis and natural language processing (NLP) using Python. You’ll also learn to apply HMM to image processing using 2D-HMM to segment images. Finally, you’ll understand how to apply HMM for reinforcement learning (RL) with the help of Q-Learning, and use this technique for single-stock and multi-stock algorithmic trading.

    By the end of this book, you will have grasped how to build your own Markov and hidden Markov models on complex datasets in order to apply them to projects.

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    O autorach książki

    Ankur Ankan is a BTech graduate from IIT (BHU), Varanasi. He is currently working in the field of data science. He is an open source enthusiast and his major work includes starting pgmpy with four other members. In his free time, he likes to participate in Kaggle competitions.
    Abinash Panda has been a data scientist for more than 4 years. He has worked at multiple early-stage start-ups and helped them build their data analytics pipelines. He loves to munge, plot, and analyze data. He has been a speaker at Python conferences. These days, he is busy co-founding a start-up. He has contributed to books on probabilistic graphical models by Packt Publishing.

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