Menu

Zaloguj się

Twoje konto

×
Dodano do koszyka:
Pozycja znajduje się w koszyku, zwiększono ilość tej pozycji:
Zakupiłeś już tę pozycję:
Książkę możesz pobrać z biblioteki w panelu użytkownika
Pozycja znajduje się w koszyku
Przejdź do koszyka

Zawartość koszyka

ODBIERZ TWÓJ BONUS :: »

Szukaj

Zamknij

    Dostawa od 0,00zł

    TOP tytuł w super cenie » 50% taniej

    Hands-on Supervised Learning with Python Gnana Lakshmi T C, Madeleine Shang

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Hands-on Supervised Learning with Python Gnana Lakshmi T C, Madeleine Shang - okladka książki

    Hands-on Supervised Learning with Python Gnana Lakshmi T C, Madeleine Shang - okladka książki

    Hands-on Supervised Learning with Python Gnana Lakshmi T C, Madeleine Shang - audiobook MP3

    Hands-on Supervised Learning with Python Gnana Lakshmi T C, Madeleine Shang - audiobook CD

    Autorzy:
    Gnana Lakshmi T C, Madeleine Shang
    Wydawnictwo:
    BPB Publications (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    384
    Dostępne formaty:
         ePub
         Mobi
    Hands-On ML problem solving and creating solutions using Python.

    Key Features
  1. Introduction to Python Programming
  2. Python for Machine Learning
  3. Introduction to Machine Learning
  4. Introduction to Predictive Modelling, Supervised and Unsupervised Algorithms
  5. Linear Regression, Logistic Regression and Support Vector Machines

    6. Description
    You will learn about the fundamentals of Machine Learning and Python programming post, which you will be introduced to predictive modelling and the different methodologies in predictive modelling. You will be introduced to Supervised Learning algorithms and Unsupervised Learning algorithms and the difference between them.
    We will focus on learning supervised machine learning algorithms covering Linear Regression, Logistic Regression, Support Vector Machines, Decision Trees and Artificial Neural Networks. For each of these algorithms, you will work hands-on with open-source datasets and use python programming to program the machine learning algorithms. You will learn about cleaning the data and optimizing the features to get the best results out of your machine learning model. You will learn about the various parameters that determine the accuracy of your model and how you can tune your model based on the reflection of these parameters.

    What You Will Learn
  6. Get a clear vision of what is Machine Learning and get familiar with the foundation principles of Machine learning.
  7. Understand the Python language-specific libraries available for Machine learning and be able to work with those libraries.
    8. Explore the different Supervised Learning based algorithms in Machine Learning and know how to implement them when a real-time use case is presented to you.
  8. Have hands-on with Data Exploration, Data Cleaning, Data Preprocessing and Model implementation.
  9. Get to know the basics of Deep Learning and some interesting algorithms in this space.
  10. Choose the right model based on your problem statement and work with EDA techniques to get good accuracy on your model

    11. Who this book is for
    This book is for anyone interested in understanding Machine Learning. Beginners, Machine Learning Engineers and Data Scientists who want to get familiar with Supervised Learning algorithms will find this book helpful.

    Table of Contents
    1. Introduction to Python Programming
    2. Python for Machine Learning
    3. Introduction to Machine Learning
    4. Supervised Learning and Unsupervised Learning
    5. Linear Regression: A Hands-on guide 6. Logistic Regression An Introduction
    7. A sneak peek into the working of Support Vector machines(SVM)
    8. Decision Trees
    9. Random Forests
    10. Time Series models in Machine Learning
    11. Introduction to Neural Networks
    12. Recurrent Neural Networks
    13. Convolutional Neural Networks
    14. Performance Metrics
    15. Introduction to Design Thinking
    16. Design Thinking Case Study

    About the Author
    Gnana Lakshmi T C iis Technology Geek, Innovator, Keynote speaker, Community builder and holds a Bachelor degree in Computer Science from National Institute of Technology, Tiruchirappalli. She is currently associated with WileyNXT as Product Manager; Emerging Technologies. She is also a Fellow Alumni at WomenWhoCode and started WomenWhoCode Blockchain community (www.womenwhocode.com/blockchain). She harnesses her knowledge by sharing it with others by conducting live events like webinars and workshops and through online channels like tutorials, social media posts etc. She has conducted several meetups on Machine learning, Blockchain and various other emerging technology topics including a recent meetup at the International Open UP Summit on GPT-3.

    LinkedIn Profile: https://www.linkedin.com/in/gyan-lakshmi

    Madeleine Shang is a Recommender Systems Team Lead @OpenMined. She started the Data Science and Machine Learning community at WomenWhoCode which is now successfully running with 2147 members. She is an expert in AI and Blockchain Research. She has been involved in many startups as a Founder. She is an Adventurer and Futurist at heart.

    LinkedIn Profile: https://www.linkedin.com/in/madeleine-shang/

    Wybrane bestsellery

    Selling helion.pl

    BPB Publications - inne książki

    Zamknij

    Przenieś na półkę

    Proszę czekać...
    ajax-loader

    Zamknij

    Wybierz metodę płatności

    Ebook
    67,43 zł
    Dodaj do koszyka

    Wydawnictwo Naukowe Helion »

    Grafika Wydawnictwo Naukowe Helion. Dołącz do nas!
    Zamknij Pobierz aplikację mobilną Ebookpoint
    Zabrania się wykorzystania treści strony do celów eksploracji tekstu i danych (TDM), w tym eksploracji w celu szkolenia technologii AI i innych systemów uczenia maszynowego. It is forbidden to use the content of the site for text and data mining (TDM), including mining for training AI technologies and other machine learning systems.