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Machine Learning with Python: The Ultimate Beginners Guide to Learn Machine Learning with Python Step by Step
XPF 3586
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Be ready to learn all that it takes to be an expert in the field of Machine Learning!
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- We live in a world of data deluge where gigabytes of data are generated daily. It is possible that this data might not be very useful for our daily applications. Major setbacks in the use of such data may be due to the presence of loopholes in data links previously generated or the data might be too vast for the limited human mind. Machine learning in this book presents some of the solutions to the problems above. Being an introductory guide, expect to learn the various basics involved in Machine Learning and Python. This book provides an insight into the new world of big data, then behooves you to learn more about Machine Learning. You will be able to get answers to the following questions: •What is Machine Learning and what does it entail? •How can I apply machine learning to have a glimpse into the new world, power my enterprise or find out how the Internet thinks about my academic research work? This is one of the best languages that you can choose to begin learning and at the end have a successful career in it. I know that you are going to have a very nice experience in this programming language. In summary, since the programming language was open-sourced, we expect a lot of advancements and developments on the language that will make it simpler and easier to use over the coming years.Be ready to learn all that it takes to be an expert in the field of Machine Learning!
| Publisher | Independently published |
| Publication date | August 26, 2019 |
| Language | English |
| Print length | 211 pages |
| ISBN-10 | 1688731008 |
| ISBN-13 | 978-1688731004 |
| Item Weight | 10.2 ounces (289.17 grams) |
| Dimensions | 6 x 0.48 x 9 inches (15.2 x 1.2 x 22.9 cm) |
| Book 1 of 3 | Machine Learning with Python |
Product Description
Machine Learning with Python: The Ultimate Beginners Guide to Learn Machine Learning with Python Step by Step
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Neural Networks Editorial Review
The reviews for this self-published manuscript reveal a predominantly negative customer experience, focusing on issues related to editing, clarity, and usability rather than content quality. Many readers noted the glaring typographical and grammatical errors present on nearly every page, indicating a lack of proper proofreading. Even those who found some value in the concepts expressed frustration at the confusing presentation, which was exacerbated by erroneous references and blurry figures. Moreover, critical readers highlighted how these mistakes interfered with comprehension of fundamental programming concepts, especially for those not already familiar with Python. The examples provided within the book were Considered poorly explained and lacked accessible data sets, which limited readers’ ability to apply the material practically. As a result, customers suggested that there are more effective free resources available online for those interested in learning machine learning with Python. In contrast, a few reviewers found the book beneficial for introducing machine learning concepts to beginners, recommending a preliminary knowledge of Python to alleviate potential frustrations. The mixed feedback of some users indicates that while the material may have potential, it is currently marred by significant flaws that hinder its overall effectiveness. **
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Pros
- Some readers found the book useful for introducing machine learning concepts.
- A few Considered it a good resource for beginners with prior knowledge of Python.
Cons
- Numerous typographical and grammatical errors throughout the text.
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XPF 3586
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Features & Benefits
- Comprehensive guide for beginners in Machine Learning with Python.
- Explore the fundamentals of Machine Learning and Python step by step.
- Gain insights into big data and its applications.
- Learn strategies to tackle challenges in data management and usage.
- Discover how to leverage Machine Learning for personal and professional growth.
- Open-source advantages ensure continual language advancements and user-friendliness.
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