Machine Learning with Python: A Practical Beginnersโ€™ Guide: 2 (Learn Machine Learning for Beginners)
Machine Learning with Python: A Practical Beginnersโ€™ Guide: 2 (Learn Machine Learning for Beginners)
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Machine Learning with Python: A Practical Beginnersโ€™ Guide: 2 (Learn Machine Learning for Beginners)
Machine Learning with Python: A Practical Beginnersโ€™ Guide: 2 (Learn Machine Learning for Beginners)
Machine Learning with Python: A Practical Beginnersโ€™ Guide: 2 (Learn Machine Learning for Beginners)
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Machine Learning with Python: A Practical Beginnersโ€™ Guide: 2 (Learn Machine Learning for Beginners)

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Shop Machine Learning with Python: A Practical Beginnersโ€™ Guide: 2 (Learn Machine Learning for Beginners) online at a best price in Georgia. 1686658494
  • Looking for a machine learning book to help you build real models, solve real problems, and future-proof your skillset?Machine Learning with Python is a hands-on, beginner-friendly guide to building practical machine learning models from the ground up. Whether you're just getting started or you've read a few โ€œmachine learning for dummiesโ€ books that left you more confused than confident, this book breaks down the process with clarity and real code examples.Using Python and essential libraries like Pandas, NumPy, Scikit-learn, and Matplotlib, youโ€™ll walk step-by-step through the full machine learning pipeline. From importing and cleaning data to training algorithms and evaluating predictions, this book teaches you how to think and work like a real machine learning practitioner.What youโ€™ll learn:- How to install Python and set up Jupyter Notebook- How to perform exploratory data analysis and data scrubbing- Key algorithms: linear/logistic regression, decision trees, KNN, SVMs- How to write your model in the least amount of code possible with the help of Pandas, Scikit-learn, Matplotlib & Seaborn.- How to visualize relationships in your dataset, including heatmaps with just a few lines of code.This book is perfect for:- Readers of Machine Learning for Absolute Beginners- Anyone wanting to learn machine learning without advanced math- Readers who want a machine learning Python book with real code, not just theory- Developers and analysts seeking a clear introduction to machine learning with PythonPacked with practical examples and visual walkthroughs, Machine Learning with Python empowers you to move beyond passive learning and start coding smart, useful models today.Buy a copy now to fast-track your machine learning journey!
Publisher Independently published
Publication date 15 Oct. 2019
Language English
Print length 186 pages
ISBN-10 1686658494
ISBN-13 978-1686658495
Item weight 1.05 kg
Dimensions 15.24 x 1.19 x 22.86 cm
Book 2 of 3 Learn Machine Learning for Beginners

แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ˜แƒก แƒแƒฆแƒฌแƒ”แƒ แƒ˜แƒšแƒแƒ‘แƒ

Machine Learning with Python: A Practical Beginnersโ€™ Guide: 2 (Learn Machine Learning for Beginners)

แƒ’แƒแƒฅแƒ•แƒ— แƒ แƒแƒ˜แƒ›แƒ” แƒจแƒ”แƒ™แƒ˜แƒ—แƒฎแƒ•แƒ? แƒ›แƒแƒ’แƒ•แƒ›แƒแƒ แƒ—แƒ”แƒ— แƒฉแƒแƒ—แƒจแƒ˜

แƒ›แƒแƒ›แƒฎแƒ›แƒแƒ แƒ”แƒ‘แƒ”แƒšแƒ—แƒ แƒ™แƒ˜แƒ—แƒฎแƒ•แƒ”แƒ‘แƒ˜ แƒ“แƒ แƒžแƒแƒกแƒฃแƒฎแƒ”แƒ‘แƒ˜

  • แƒ™แƒ˜แƒ—แƒฎแƒ•แƒ: แƒ แƒแƒ’แƒแƒ  แƒ•แƒ˜แƒงแƒ˜แƒ“แƒแƒ— Machine Learning with Python: A Practical แƒแƒœแƒšแƒแƒ˜แƒœ Ubuy-แƒ“แƒแƒœ?

    แƒžแƒแƒกแƒฃแƒฎแƒ˜: แƒ›แƒแƒ แƒขแƒ˜แƒ•แƒ˜แƒ แƒงแƒ˜แƒ“แƒ•แƒ Machine Learning with Python: A Practical แƒแƒœแƒšแƒแƒ˜แƒœ Ubuy-แƒ“แƒแƒœ.. แƒ—แƒฅแƒ•แƒ”แƒœ แƒฃแƒ‘แƒ แƒแƒšแƒแƒ“ แƒฃแƒœแƒ“แƒ แƒ›แƒแƒซแƒ”แƒ‘แƒœแƒแƒ— แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ˜, แƒแƒ˜แƒ แƒฉแƒ˜แƒแƒ— แƒ—แƒฅแƒ•แƒ”แƒœแƒ˜ แƒ›แƒ˜แƒฌแƒแƒ“แƒ”แƒ‘แƒ˜แƒก แƒ›แƒ”แƒ—แƒแƒ“แƒ˜ แƒจแƒ”แƒ›แƒแƒฌแƒ›แƒ”แƒ‘แƒ˜แƒก แƒ“แƒ แƒแƒก แƒ“แƒ แƒ›แƒ˜แƒ˜แƒขแƒแƒœแƒแƒ— แƒ˜แƒ’แƒ˜ แƒ—แƒฅแƒ•แƒ”แƒœแƒก แƒแƒ“แƒ’แƒ˜แƒšแƒแƒก.
  • แƒ™แƒ˜แƒ—แƒฎแƒ•แƒ: Machine Learning with Python: A Practical แƒฎแƒ”แƒšแƒ›แƒ˜แƒกแƒแƒฌแƒ•แƒ“แƒแƒ›แƒ˜แƒ แƒแƒœแƒšแƒแƒ˜แƒœ แƒจแƒแƒžแƒ˜แƒœแƒ’แƒ˜แƒกแƒ—แƒ•แƒ˜แƒก Georgia-แƒจแƒ˜?

    แƒžแƒแƒกแƒฃแƒฎแƒ˜: แƒ“แƒ˜แƒแƒฎ, Ubuy Georgia-แƒจแƒ˜ แƒ”แƒก แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ˜ แƒ—แƒฅแƒ•แƒ”แƒœแƒ—แƒ•แƒ˜แƒก แƒฎแƒ”แƒšแƒ›แƒ˜แƒกแƒแƒฌแƒ•แƒ“แƒแƒ›แƒ˜แƒ แƒ’แƒแƒœแƒ˜แƒ•แƒ แƒฃแƒš แƒคแƒแƒกแƒแƒ“.. Machine Learning with Python: A Practical แƒแƒ  แƒแƒ แƒ˜แƒก แƒฎแƒ”แƒšแƒ›แƒ˜แƒกแƒแƒฌแƒ•แƒ“แƒแƒ›แƒ˜ แƒแƒ“แƒ’แƒ˜แƒšแƒแƒ‘แƒ แƒ˜แƒ•แƒแƒ“, แƒ›แƒแƒ’แƒ แƒแƒ› แƒจแƒ”แƒ’แƒ˜แƒซแƒšแƒ˜แƒแƒ— แƒ’แƒ•แƒฏแƒ”แƒ แƒแƒ“แƒ”แƒก แƒฉแƒ•แƒ”แƒœแƒ˜ แƒ”แƒฅแƒกแƒžแƒ แƒ”แƒก แƒ›แƒ˜แƒฌแƒแƒ“แƒ”แƒ‘แƒ˜แƒก แƒกแƒ”แƒ แƒ•แƒ˜แƒกแƒ”แƒ‘แƒ˜.
  • แƒ™แƒ˜แƒ—แƒฎแƒ•แƒ: แƒ แƒแƒ›แƒ“แƒ”แƒœแƒ˜ แƒ“แƒ แƒ แƒกแƒญแƒ˜แƒ แƒ“แƒ”แƒ‘แƒ แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ˜แƒก แƒ›แƒ˜แƒฆแƒ”แƒ‘แƒแƒก แƒจแƒ”แƒ™แƒ•แƒ”แƒ—แƒ˜แƒก แƒ’แƒแƒคแƒแƒ แƒ›แƒ”แƒ‘แƒ˜แƒก แƒจแƒ”แƒ›แƒ“แƒ”แƒ’?

    แƒžแƒแƒกแƒฃแƒฎแƒ˜: แƒ—แƒฅแƒ•แƒ”แƒœแƒ˜ แƒจแƒ”แƒ™แƒ•แƒ”แƒ—แƒ˜แƒšแƒ˜ แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ˜แƒก แƒ›แƒ˜แƒฌแƒแƒ“แƒ”แƒ‘แƒ˜แƒก แƒ“แƒ แƒ แƒ’แƒแƒœแƒกแƒฎแƒ•แƒแƒ•แƒ“แƒ”แƒ‘แƒ แƒ—แƒฅแƒ•แƒ”แƒœ แƒ›แƒ˜แƒ”แƒ  แƒจแƒ”แƒ™แƒ•แƒ”แƒ—แƒ˜แƒšแƒ˜ แƒ“แƒ แƒ›แƒ˜แƒฌแƒแƒ“แƒ”แƒ‘แƒ˜แƒก แƒ›แƒ”แƒ—แƒแƒ“แƒ˜แƒก แƒ›แƒ˜แƒฎแƒ”แƒ“แƒ•แƒ˜แƒ—, แƒ แƒแƒ›แƒ”แƒšแƒ˜แƒช แƒแƒ แƒฉแƒ”แƒฃแƒšแƒ˜แƒ.. แƒ›แƒ˜แƒฌแƒแƒ“แƒ”แƒ‘แƒ˜แƒก แƒกแƒแƒ•แƒแƒ แƒแƒฃแƒ“แƒ แƒ“แƒ แƒ แƒ›แƒ˜แƒ—แƒ˜แƒ—แƒ”แƒ‘แƒฃแƒšแƒ˜แƒ แƒจแƒ”แƒ™แƒ•แƒ”แƒ—แƒ˜แƒก แƒžแƒ แƒแƒชแƒ”แƒกแƒจแƒ˜, แƒแƒกแƒ” แƒ แƒแƒ› แƒจแƒแƒžแƒ˜แƒœแƒ’แƒ˜แƒก แƒ“แƒ แƒแƒก แƒ˜แƒงแƒแƒ•แƒ˜แƒ— แƒฃแƒงแƒฃแƒ แƒแƒ“แƒฆแƒ”แƒ‘แƒ.

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  • แƒจแƒ”แƒ–แƒฆแƒฃแƒ“แƒ•แƒ”แƒ‘แƒ˜: แƒ’แƒ—แƒฎแƒแƒ•แƒ—, แƒ’แƒแƒ˜แƒ—แƒ•แƒแƒšแƒ˜แƒกแƒฌแƒ˜แƒœแƒแƒ—, แƒ แƒแƒ› แƒกแƒแƒ–แƒฆแƒ•แƒแƒ แƒ’แƒแƒ แƒ”แƒ— แƒ’แƒแƒ’แƒ–แƒแƒ•แƒœแƒ˜แƒšแƒ˜ แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ”แƒ‘แƒ˜แƒกแƒ—แƒ•แƒ˜แƒก แƒ›แƒฌแƒแƒ แƒ›แƒแƒ”แƒ‘แƒšแƒ˜แƒก แƒ’แƒแƒ แƒแƒœแƒขแƒ˜แƒ แƒจแƒ”แƒ˜แƒซแƒšแƒ”แƒ‘แƒ แƒแƒ  แƒ˜แƒงแƒแƒก แƒ›แƒแƒฅแƒ›แƒ”แƒ“แƒ˜; แƒ›แƒฌแƒแƒ แƒ›แƒแƒ”แƒ‘แƒšแƒ˜แƒก แƒ›แƒแƒ›แƒกแƒแƒฎแƒฃแƒ แƒ”แƒ‘แƒ˜แƒก แƒžแƒแƒ แƒแƒ›แƒ”แƒขแƒ แƒ”แƒ‘แƒ˜ แƒจแƒ”แƒ˜แƒซแƒšแƒ”แƒ‘แƒ แƒแƒ  แƒ˜แƒงแƒแƒก แƒฎแƒ”แƒšแƒ›แƒ˜แƒกแƒแƒฌแƒ•แƒ“แƒแƒ›แƒ˜; แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ˜แƒก แƒ˜แƒœแƒกแƒขแƒ แƒฃแƒฅแƒชแƒ˜แƒ”แƒ‘แƒ˜ แƒ“แƒ แƒฃแƒกแƒแƒคแƒ แƒ—แƒฎแƒแƒ”แƒ‘แƒ˜แƒก แƒ’แƒแƒคแƒ แƒ—แƒฎแƒ˜แƒšแƒ”แƒ‘แƒ”แƒ‘แƒ˜ แƒจแƒ”แƒ˜แƒซแƒšแƒ”แƒ‘แƒ แƒแƒ  แƒ˜แƒงแƒแƒก แƒ“แƒแƒœแƒ˜แƒจแƒœแƒฃแƒšแƒ”แƒ‘แƒ˜แƒก แƒฅแƒ•แƒ”แƒงแƒœแƒ˜แƒก แƒ”แƒœแƒแƒ–แƒ”; แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ”แƒ‘แƒ˜ (แƒ“แƒ แƒ›แƒแƒกแƒ—แƒแƒœ แƒ“แƒแƒ™แƒแƒ•แƒจแƒ˜แƒ แƒ”แƒ‘แƒฃแƒšแƒ˜ แƒ›แƒแƒกแƒแƒšแƒ”แƒ‘แƒ˜) แƒจแƒ”แƒ˜แƒซแƒšแƒ”แƒ‘แƒ แƒแƒ  แƒ˜แƒงแƒแƒก แƒจแƒ”แƒ›แƒฃแƒจแƒแƒ•แƒ”แƒ‘แƒฃแƒšแƒ˜ แƒ“แƒแƒœแƒ˜แƒจแƒœแƒฃแƒšแƒ”แƒ‘แƒ˜แƒก แƒฅแƒ•แƒ”แƒงแƒœแƒ˜แƒก แƒกแƒขแƒแƒœแƒ“แƒแƒ แƒขแƒ”แƒ‘แƒ˜แƒก, แƒกแƒžแƒ”แƒชแƒ˜แƒคแƒ˜แƒ™แƒแƒชแƒ˜แƒ”แƒ‘แƒ˜แƒกแƒ แƒ“แƒ แƒ›แƒแƒ แƒ™แƒ˜แƒ แƒ”แƒ‘แƒ˜แƒก แƒ›แƒแƒ—แƒฎแƒแƒ•แƒœแƒ”แƒ‘แƒ˜แƒก แƒจแƒ”แƒกแƒแƒ‘แƒแƒ›แƒ˜แƒกแƒแƒ“; แƒ“แƒ แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ”แƒ‘แƒ˜ แƒจแƒ”แƒ˜แƒซแƒšแƒ”แƒ‘แƒ แƒแƒ  แƒจแƒ”แƒ”แƒกแƒแƒ‘แƒแƒ›แƒ”แƒ‘แƒแƒ“แƒ”แƒก แƒ“แƒแƒœแƒ˜แƒจแƒœแƒฃแƒšแƒ”แƒ‘แƒ˜แƒก แƒฅแƒ•แƒ”แƒงแƒœแƒ˜แƒก แƒซแƒแƒ‘แƒ•แƒ˜แƒกแƒ แƒ“แƒ แƒกแƒฎแƒ•แƒ แƒ”แƒšแƒ”แƒฅแƒขแƒ แƒ แƒกแƒขแƒแƒœแƒ“แƒแƒ แƒขแƒ”แƒ‘แƒก (แƒกแƒแƒญแƒ˜แƒ แƒแƒ”แƒ‘แƒ˜แƒก แƒจแƒ”แƒ›แƒ—แƒฎแƒ•แƒ”แƒ•แƒแƒจแƒ˜ แƒกแƒแƒญแƒ˜แƒ แƒแƒ แƒแƒ“แƒแƒžแƒขแƒ”แƒ แƒ˜ แƒแƒœ แƒ’แƒแƒ“แƒแƒ›แƒงแƒ•แƒแƒœแƒ˜). แƒ›แƒ˜แƒ›แƒฆแƒ”แƒ‘แƒ˜ แƒžแƒแƒกแƒฃแƒฎแƒ˜แƒกแƒ›แƒ’แƒ”แƒ‘แƒ”แƒšแƒ˜แƒ แƒฃแƒ–แƒ แƒฃแƒœแƒ•แƒ”แƒšแƒงแƒแƒก, แƒ แƒแƒ› แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ˜ แƒ™แƒแƒœแƒแƒœแƒ˜แƒ”แƒ แƒแƒ“ แƒจแƒ”แƒ›แƒแƒ˜แƒขแƒแƒœแƒแƒก แƒ“แƒแƒœแƒ˜แƒจแƒœแƒฃแƒšแƒ”แƒ‘แƒ˜แƒก แƒฅแƒ•แƒ”แƒงแƒแƒœแƒแƒจแƒ˜. Ubuy-แƒก แƒแƒœ แƒ›แƒ˜แƒกแƒ˜ แƒแƒคแƒ˜แƒšแƒ˜แƒ แƒ”แƒ‘แƒฃแƒšแƒ˜ แƒžแƒ˜แƒ แƒ”แƒ‘แƒ˜แƒก แƒ›แƒ”แƒจแƒ•แƒ”แƒแƒ‘แƒ˜แƒ— แƒจแƒ”แƒ™แƒ•แƒ”แƒ—แƒ˜แƒกแƒแƒก แƒ›แƒ˜แƒ›แƒฆแƒ”แƒ‘แƒ˜ แƒแƒ แƒ˜แƒก แƒ แƒ”แƒ’แƒ˜แƒกแƒขแƒ แƒ˜แƒ แƒ”แƒ‘แƒฃแƒšแƒ˜ แƒ˜แƒ›แƒžแƒแƒ แƒขแƒ˜แƒแƒ แƒ˜ แƒ“แƒ แƒ›แƒแƒœ แƒฃแƒœแƒ“แƒ แƒ’แƒแƒ˜แƒ—แƒ•แƒแƒšแƒ˜แƒกแƒฌแƒ˜แƒœแƒแƒก แƒ“แƒแƒœแƒ˜แƒจแƒœแƒฃแƒšแƒ”แƒ‘แƒ˜แƒก แƒฅแƒ•แƒ”แƒงแƒœแƒ˜แƒก แƒงแƒ•แƒ”แƒšแƒ แƒ™แƒแƒœแƒแƒœแƒ˜ แƒ“แƒ แƒ“แƒ”แƒ‘แƒฃแƒšแƒ”แƒ‘แƒ.
  • Ubuy-แƒ–แƒ” แƒฉแƒแƒ›แƒแƒ—แƒ•แƒšแƒ˜แƒšแƒ˜ แƒงแƒ•แƒ”แƒšแƒ แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ˜ แƒแƒ  แƒแƒ แƒ˜แƒก แƒ’แƒแƒงแƒ˜แƒ“แƒ•แƒแƒจแƒ˜, แƒ แƒแƒ“แƒ’แƒแƒœ Ubuy แƒแƒ แƒ˜แƒก แƒ’แƒšแƒแƒ‘แƒแƒšแƒฃแƒ แƒ˜ แƒกแƒแƒซแƒ˜แƒ”แƒ‘แƒ แƒกแƒ˜แƒกแƒขแƒ”แƒ›แƒ แƒ“แƒ แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ”แƒ‘แƒ˜ แƒ”แƒฅแƒ•แƒ”แƒ›แƒ“แƒ”แƒ‘แƒแƒ แƒ”แƒ‘แƒ แƒกแƒแƒ”แƒฅแƒกแƒžแƒแƒ แƒขแƒ/แƒกแƒแƒ•แƒแƒญแƒ แƒ แƒ แƒ”แƒ’แƒฃแƒšแƒแƒชแƒ˜แƒ”แƒ‘แƒก.

GEL 73

แƒจแƒ”แƒฃแƒ™แƒ•แƒ”แƒ—แƒ”แƒ— แƒแƒฎแƒšแƒแƒ•แƒ” แƒ“แƒ แƒ›แƒ˜แƒ˜แƒฆแƒ”แƒ— แƒฎแƒฃแƒ—แƒจแƒแƒ‘แƒแƒ—แƒ˜, แƒแƒฅแƒขแƒแƒ›แƒ‘แƒ”แƒ แƒ˜ 08

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