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Machine Learning with Python: A Practical Beginners’ Guide: 2 (Learn Machine Learning for Beginners)
85% of respondents would recommend this to a friend
TWD 852
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What Stands Out
Product Details
| 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?
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Beginners in ML
Ideal for individuals new to machine learning concepts and Python programming looking to build foundational knowledge.
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Self-learners
Great for self-motivated learners who prefer a practical approach with hands-on examples and exercises.
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Students
Perfect for students in data science or computer science programs needing supplemental material for coursework.
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Advanced Users
Not suitable for experienced machine learning practitioners seeking in-depth or advanced algorithmic discussions and applications.
Product Description
Machine Learning with Python: A Practical Beginners’ Guide: 2 (Learn Machine Learning for Beginners)
Customer Questions & Answers
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Question:
Is this book suitable for complete beginners?
Answer: No, it's designed for those with basic knowledge of machine learning. -
Question:
What programming language is used in this book?
Answer: The book uses Python for coding machine learning models. -
Question:
Will I learn about data visualization in this book?
Answer: Yes, the book includes techniques for visualizing datasets effectively.
Statistics & Research Editorial Review
Machine Learning With Python A Practical Beginners Guide 2 Learn Machine Learning For Beginners is a fantastic resource that provides an excellent introduction to simple machine learning models along with practical coding exercises that help put theory into practice. Readers have praised its readability and understandable illustrations, making it suitable for beginners looking to navigate the complexities of machine learning. Although the book contains useful examples, some datasets referenced may no longer be available and could require additional effort to source. Overall, it effectively breaks down challenging concepts, ensuring that newcomers do not underestimate the intricacies of data mining and data science.
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Pros
- Readable and easy to follow for beginners
- Practical coding exercises enhance learning
- Illustrated examples make concepts clear
- Excellent introduction to machine learning models
- Encourages critical thinking about data science
Cons
- Setting up the development environment can be challenging
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Features & Benefits
- 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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