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42079191
Ultimate Step by Step Guide to Machine Learning Using Python: Predictive modelling concepts explained in simple terms for beginners
89% of respondents would recommend this to a friend
Nº de artículo:
42079191
NIO 1000
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What Stands Out
Beginner-Friendly
Designed specifically for newcomers, this guide simplifies complex predictive modeling concepts, making machine learning approachable and easy to understand.
Comprehensive Coverage
The step-by-step format ensures thorough exploration of machine learning essentials, offering practical examples and exercises that reinforce learning and retention.
Python Built-In
Utilizing Python, a popular programming language for data science, the guide equips readers with versatile coding skills applicable in real-world machine learning projects.
Detalles de producto
- Introduces Python basics including data structures like Pandas, and foundational libraries like Numpy, Seaborn, and Scikit-Learn
- Shows how to build predictive machine learning models step by step using techniques such as regression analysis, decision tree analysis, and training/testing data models
- Enables the reader to code confidently in Python, build new machine learning models from scratch, and clean and prepare data for analytics
- Differentiates from other books with step by step code examples and explanations, visual explanation of complex concepts, real-world applicability of machine learning models, and bonus free code samples
- Offers a step by step action plan for mastering data science and machine learning, and starting a lucrative career
| Publisher | Independently published |
| Publication date | February 17, 2020 |
| Language | English |
| Print length | 68 pages |
| ISBN-13 | 979-8611399538 |
| Item Weight | 3.67 ounces (104.04 grams) |
| Dimensions | 6 x 0.16 x 9 inches (15.2 x 0.4 x 22.9 cm) |
| Book 1 of 4 | Ultimate Step by Step Guide to Machine Learning |
Who Should Buy?
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Beginners in Tech
Ideal for individuals starting their journey in technology and seeking a structured approach to machine learning using Python.
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Data Science Students
Great for students studying data science who need clear explanations of predictive modeling and machine learning concepts.
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Self-learners
Perfect for individuals who prefer to learn at their own pace and require easily digestible materials on complex topics.
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Advanced Practitioners
Not suitable for experienced professionals already familiar with machine learning techniques and programming in Python.
DESCRIPCIÓN DEL PRODUCTO
Ultimate Step by Step Guide to Machine Learning Using Python: Predictive modelling concepts explained in simple terms for beginners
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Expert Systems Editorial Review
"The Ultimate Step by Step Guide to Machine Learning Using Python" is a concise book that promises to teach beginners how to understand machine learning concepts through Python programming. While the author's explanations are clear and easy to follow, some reviewers found the book lacking in depth and basic explanations. The book is less than 100 pages and contains typographical errors that the reader needs to catch on their own. The author assumes some familiarity with Python and does not always provide background information before introducing concepts. The book includes examples of linear regression and decision trees fit to CSV data, but the majority of the book covers the very basics of Python and Machine Learning concepts. Overall, it is a good starting point for readers who already have some Python experience and want to learn more about Machine Learning.
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ventajas
- Clear and concise explanations of Machine Learning concepts
- Easy to read and follow
- Includes examples to practice with
Contras
- Lacks depth and basic explanations
Product Price History
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características y beneficios
- Introduction to Python basics such as Data Structures and Foundational Libraries
- Step-by-step guide to building predictive machine learning models using Regression analysis, Decision tree analysis and more
- Ability to code in Python with confidence
- Learn how to clean and prepare data for analytics and speak confidently about statistical analysis techniques
- Real-world applicability of the machine learning models introduced
- Bonus free code samples to try without any prior experience in Python
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