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Machine Learning with Python: A Practical Beginners’ Guide: 2 (Learn Machine Learning for Beginners)
Machine Learning with Python: A Practical Beginners’ Guide: 2 (Learn Machine Learning for Beginners)
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Machine Learning with Python: A Practical Beginners’ Guide: 2 (Learn Machine Learning for Beginners)
Machine Learning with Python: A Practical Beginners’ Guide: 2 (Learn Machine Learning for Beginners)
Ready to add Machine Learning to your skill stack?
Machine Learning with Python: A Practical Beginners’ Guide: 2 (Learn Machine Learning for Beginners)
제품번호 #: 90231392

Machine Learning with Python: A Practical Beginners’ Guide: 2 (Learn Machine Learning for Beginners)

89% of respondents would recommend this to a friend

제품번호 #: 90231392

KRW 38210

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What Stands Out

Beginner Friendly
This guide is designed specifically for beginners, making complex concepts accessible and easy to understand, allowing readers to build a solid foundation in machine learning.
Practical Applications
Focuses on real-world applications and hands-on projects, helping readers to immediately apply learned concepts and gain practical experience in machine learning with Python.
Comprehensive Coverage
Offers extensive coverage of essential machine learning topics, ensuring a well-rounded understanding and equipping readers with the necessary skills to tackle various challenges.

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Shop Machine Learning with Python: A Practical Beginners’ Guide: 2 (Learn Machine Learning for Beginners) online at a best price in South Korea. 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 “machine learning for dummies” 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, you’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 you’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

Who Should Buy?

Suitable For
  • Beginners in ML

    Ideal for individuals new to machine learning concepts and Python programming looking to build foundational knowledge.

  • Self-learners

    Great for self-motivated learners who prefer a practical approach with hands-on examples and exercises.

  • Students

    Perfect for students in data science or computer science programs needing supplemental material for coursework.

Not Suitable For
  • Advanced Users

    Not suitable for experienced machine learning practitioners seeking in-depth or advanced algorithmic discussions and applications.

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고객 질문 및 답변

  • 의문: Is this book suitable for complete beginners?

    답변: No, it's designed for those with basic knowledge of machine learning.
  • 의문: What programming language is used in this book?

    답변: The book uses Python for coding machine learning models.
  • 의문: Will I learn about data visualization in this book?

    답변: Yes, the book includes techniques for visualizing datasets effectively.

Statistics & Research Editorial Review

**** "Machine Learning with Python: A Practical Beginners’ Guide" serves as an excellent entry point for newcomers to the field of machine learning. The text stands out for its readability, allowing beginners to grasp complex concepts without feeling overwhelmed. Readers appreciate the straightforward illustrations and practical coding exercises, which effectively bridge the gap between theory and its application. Many users have praised the book for equipping them with the knowledge necessary to adapt the content to their personal projects, signaling its utility in real-world scenarios. However, potential readers should be aware that while the book provides a solid foundational understanding, the examples may not always be fully comprehensive. A notable downside mentioned by readers is that a significant number of the dataset links referenced within the book are no longer available, necessitating additional effort on the reader's part to locate alternative datasets. Despite this hurdle, the Consensus is that the book is a highly valued resource for beginners, with many finding the material intuitive and engaging. Overall, this guide is particularly suitable for those just starting their journey in machine learning, especially for individuals who appreciate a clear and supportive learning style. **

Customer Reviews & Ratings

230 고객 평가
  • 5 점
    64%
  • 4 점
    21%
  • 3 점
    10%
  • 2 점
    3%
  • 1 점
    2%

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장점

  • Very readable and easy to follow
  • Excellent introduction to basic ML concepts
  • Practical coding exercises to apply theory
  • Good for adapting concepts to personal projects

단점

  • Some examples lack completeness

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특징 및 장점

  • Practical guide for beginners to learn machine learning with Python.
  • Includes step-by-step instructions for coding various prediction models.
  • Focus on popular algorithms such as logistic regression and decision trees.
  • Utilizes popular Python libraries: Pandas, Scikit-learn, Matplotlib, and Seaborn.
  • Offers hands-on projects for applying learned skills.
  • Perfect for those with basic knowledge looking to enhance their skill set.

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