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Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn and Tensorflow (Step-by-Step Tutorial For Beginners)
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Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn and Tensorflow (Step-by-Step Tutorial For Beginners)
This book is the best guide for you.
Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn and Tensorflow (Step-by-Step Tutorial For Beginners)
Item #: 91465567

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn and Tensorflow (Step-by-Step Tutorial For Beginners)

75% of respondents would recommend this to a friend

Item #: 91465567

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What Stands Out

Comprehensive Guide
Offers a detailed introduction to machine learning and deep learning, making it suitable for beginners who seek a structured and step-by-step approach.
Hands-On Experience
Provides practical examples and projects using Python, scikit-learn, and TensorFlow, enabling users to apply concepts effectively and enhance their learning experience.
Updated Techniques
Includes modern practices and techniques ensuring readers are well-versed in the latest trends within machine learning and deep learning, aiding career advancement.

Product Details

Shop Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn and Tensorflow (Step-by-Step Tutorial For Beginners) online at a best price in Martinique. 1090434162
Publisher Independently published
Publication date March 13, 2019
Language English
Print length 176 pages
ISBN-10 1090434162
ISBN-13 978-1090434166
Item Weight 9.5 ounces (269.33 grams)
Dimensions 6 x 0.44 x 9 inches (15.2 x 1.1 x 22.9 cm)

Who Should Buy?

Suitable For
  • Beginner Programmers

    Those new to programming will find clear explanations and practical examples to facilitate learning machine learning.

  • Data Science Enthusiasts

    Individuals interested in data analysis can leverage this book to understand and implement machine learning techniques effectively.

  • Self-learners

    Learners who prefer self-study will benefit from the step-by-step tutorials and hands-on practice in Python.

Not Suitable For
  • Advanced Practitioners

    Experienced data scientists may find the content too basic and lacking in advanced machine learning techniques.

Product Description

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn and Tensorflow (Step-by-Step Tutorial For Beginners)

Have any Query? Chat with us

Customer Questions & Answers

  • Question: What is the main focus of the book 'Python Machine Learning'?

    Answer: The book primarily focuses on providing a comprehensive introduction to machine learning and deep learning using Python programming. It guides beginners through fundamental concepts, practical techniques, and step-by-step tutorials. It emphasizes hands-on learning by demonstrating how to implement various algorithms using popular libraries like scikit-learn and TensorFlow, enabling readers to build their own machine learning models and applications.
  • Question: Who is the target audience for this book?

    Answer: This book is aimed at beginners who have little to no experience with machine learning or deep learning. It's perfect for individuals interested in enhancing their programming skills or students pursuing studies in data science and artificial intelligence. The step-by-step approach ensures that even those new to programming can grasp complex concepts, making it accessible for a wide range of readers seeking to enter the field of machine learning.
  • Question: What programming skills are required to benefit from this book?

    Answer: While prior programming experience is helpful, it is not strictly necessary for readers of 'Python Machine Learning.' The book is designed for beginners and teaches programming concepts in the context of machine learning. Basic understanding of Python is beneficial, but the tutorial approach breaks down complex tasks into manageable steps, allowing readers to gradually build their skills as they progress through the content.
  • Question: Does the book cover practical examples of machine learning projects?

    Answer: Yes, the book includes numerous practical examples and projects that allow readers to apply what they've learned in real-world scenarios. Each chapter presents case studies that help readers understand how to implement machine learning algorithms effectively. By working through these examples—such as creating predictive models or classification tasks—readers gain valuable hands-on experience, which is essential for mastering machine learning techniques.
  • Question: Is deep learning covered in this book?

    Answer: Absolutely! 'Python Machine Learning' also delves into deep learning concepts, explaining how they differ from traditional machine learning methods. It introduces neural networks and demonstrates practical implementations using TensorFlow. This is particularly beneficial for readers looking to explore advanced topics, as deep learning is increasingly important in areas like image recognition and natural language processing.
  • Question: How does this book help in practical machine learning applications?

    Answer: The book emphasizes practical applications of machine learning by guiding readers through the entire process—from data preprocessing to model evaluation. Each tutorial is designed to be hands-on, using real datasets. This approach not only helps reinforce the concepts learned but also prepares readers to tackle similar challenges in their own projects, whether in academic settings or industry applications.
  • Question: What are the key topics covered in the book?

    Answer: Key topics include foundational concepts in machine learning, data preprocessing techniques, supervised and unsupervised learning algorithms, neural networks, model evaluation, and deployment strategies. The structured layout allows readers to build upon their knowledge sequentially, ensuring that they develop a well-rounded understanding of both machine learning and deep learning principles, starting from the basics to more advanced techniques.
  • Question: Are there any prerequisites for this book?

    Answer: The book is designed for readers with a beginner level of programming knowledge, ideally in Python. Familiarity with basic mathematical and statistical concepts can enhance understanding but is not mandatory. The author clearly explains necessary concepts as they arise, ensuring that readers can keep pace with the material and feel confident in their learning journey.
  • Question: Can this book be used for self-study?

    Answer: Yes, 'Python Machine Learning' is well-suited for self-study. Its structured approach, with clear explanations and examples, allows readers to learn at their own pace. The step-by-step tutorials provide practical exercises that reinforce learning. This format encourages independent exploration and experimentation, making it an ideal resource for those looking to enhance their skills in machine learning and data science without formal instruction.
  • Question: Where can I buy Python Machine Learning in Martinique?

    Answer: You can purchase 'Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow' on Ubuy. Ubuy is a reliable e-commerce platform that offers a wide range of books and educational resources, providing customers with convenient shopping options tailored to their needs in Martinique.

Intelligence & Semantics Editorial Review

**** The book "Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn and TensorFlow" is predominantly well-received, offering a solid introduction to crucial concepts in machine learning and deep learning for beginners. Reviewers have praised the book for its clarity and organization, highlighting its well-written structure that caters to newcomers in the field. The tutorial-style approach with practical examples provides readers with a useful framework to understand complex topics. However, a few critiques have surfaced, notably concerning the inclusion of outdated TensorFlow versions, which may impede the learning process slightly for those using the latest tools. Furthermore, a common point of contention is the absence of sample data files for practice exercises. Although some links to external databases are provided later in the book, the initial lack of resources has left readers to generate their datasets, which can be a hurdle for those new to the subject matter. Despite these shortcomings, the overall experience with the book remains positive, and it is recommended for those eager to dive into the world of machine learning using Python. **

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Pros

  • Well-written and easy to follow
  • Good examples and practical approach
  • Well organized for beginners

Cons

  • Outdated TensorFlow version

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Features & Benefits

  • Explore machine learning and deep learning from the basics.
  • Written in simple language with practical examples.
  • Chronologically organized for step-by-step learning.
  • Learn to implement ML algorithms using Python.
  • Gain insights into Scikit-Learn and TensorFlow libraries.
  • Ideal for beginners and professionals in data science.

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