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    Hands-on Data Analysis and Visualization with Pandas PURNA CHANDER RAO. KATHULA

    (ebook) (audiobook) (audiobook) Książka w języku angielskim
    Hands-on Data Analysis and Visualization with Pandas PURNA CHANDER RAO. KATHULA - okladka książki

    Hands-on Data Analysis and Visualization with Pandas PURNA CHANDER RAO. KATHULA - okladka książki

    Hands-on Data Analysis and Visualization with Pandas PURNA CHANDER RAO. KATHULA - audiobook MP3

    Hands-on Data Analysis and Visualization with Pandas PURNA CHANDER RAO. KATHULA - audiobook CD

    Autor:
    PURNA CHANDER RAO. KATHULA
    Wydawnictwo:
    BPB Publications (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    316
    Dostępne formaty:
         ePub
         Mobi
    Get familiar with various Supervised, Unsupervised and Reinforcement learning algorithms

    Key Features
  1. Understand the types of Machine learning.
  2. Get familiar with different Feature extraction methods.
  3. Get an overview of how Neural Network Algorithms work.
  4. Learn how to implement Decision Trees and Random Forests.
  5. The book not only explains the Classification algorithms but also discusses the deviations/ mathematical modeling.

    6. Description
    This book covers important concepts and topics in Machine Learning. It begins with Data Cleansing and presents an overview of Feature Selection. It then talks about training and testing, cross-validation, and Feature Selection. The book covers algorithms and implementations of the most common Feature Selection Techniques. The book then focuses on Linear Regression and Gradient Descent. Some of the important Classification techniques such as K-nearest neighbors, logistic regression, Nave Bayesian, and Linear Discriminant Analysis are covered in the book. It then gives an overview of Neural Networks and explains the biological background, the limitations of the perceptron, and the backpropagation model. The Support Vector Machines and Kernel methods are also included in the book. It then shows how to implement Decision Trees and Random Forests.

    Towards the end, the book gives a brief overview of Unsupervised Learning. Various Feature Extraction techniques, such as Fourier Transform, STFT, and Local Binary patterns, are covered. The book also discusses Principle Component Analysis and its implementation.

    What will you learn
  6. Learn how to prepare Data for Machine Learning.
  7. Learn how to implement learning algorithms from scratch.
  8. Use scikit-learn to implement algorithms.
  9. Use various Feature Selection and Feature Extraction methods.
  10. Learn how to develop a Face recognition system.

    11. Who this book is for
    The book is designed for Undergraduate and Postgraduate Computer Science students and for the professionals who intend to switch to the fascinating world of Machine Learning. This book requires basic know-how of programming fundamentals, Python, in particular.

    Table of Contents
    1. An introduction to Machine Learning
    2. The beginning: Pre-Processing and Feature Selection
    3. Regression
    4. Classification
    5. Neural Networks- I
    6. Neural Networks-II
    7. Support Vector machines
    8. Decision Trees
    9. Clustering
    10. Feature Extraction
    Appendix
    A1. Cheat Sheets
    A2. Face Detection
    A3.Biblography

    About the Author
    Harsh Bhasin is an Applied Machine Learning researcher. Mr. Bhasin worked as Assistant Professor in Jamia Hamdard, New Delhi, and taught as a guest faculty in various institutes including Delhi Technological University. Before that, he worked in C# Client-Side Development and Algorithm Development.
    He has authored a few books including Programming in C#, Oxford University Press; Algorithms, Oxford University Press; Python Basics, Mercury; Python for Beginners, New Age International. Mr. Bhasin has authored a few papers published in renowned journals including Soft Computing, Springer, BMC Medical Informatics and Decision Making, AI and Society, etc. He is the reviewer of prominent journals and has been the editor of a few special issues. He has been a recipient of a distinguished fellowship.
    Outside work, he is deeply interested in Hindi Poetry, progressive era; Hindustani Classical Music, percussion instruments.
    His areas of interest include Data Structures, Algorithms Analysis and Design, Theory of Computation , Python, Machine Learning and Deep learning.

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