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Bezig met laden... Thoughtful Machine Learning with Python: A Test-Driven Approach (2017)door Matthew Kirk
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Learn how to apply test-driven development (TDD) to machine-learning algorithms ?and catch mistakes that could sink your analysis. In this practical guide, author Matthew Kirk takes you through the principles of TDD and machine learning, and shows you how to apply TDD to several machine-learning algorithms, including Naive Bayesian classifiers and Neural Networks. Machine-learning algorithms often have tests baked in, but they can ?t account for human errors in coding. Rather than blindly rely on machine-learning results as many researchers have, you can mitigate the risk of errors with TDD and write clean, stable machine-learning code. If you ?re familiar with Ruby 2.1, you ?re ready to start. Apply TDD to write and run tests before you start coding Learn the best uses and tradeoffs of eight machine learning algorithms Use real-world examples to test each algorithm through engaging, hands-on exercises Understand the similarities between TDD and the scientific method for validating solutions Be aware of the risks of machine learning, such as underfitting and overfitting data Explore techniques for improving your machine-learning models or data extraction Geen bibliotheekbeschrijvingen gevonden. |
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Google Books — Bezig met laden... GenresDewey Decimale Classificatie (DDC)006.76Information Computer Science; Knowledge and Systems Special Topics Multimedia systems Web & Multimedia ProgrammingLC-classificatieWaarderingGemiddelde:
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Overall, an Excellent Gentle Introduction to Machine Learning.
I think, I'd recommend this as your first Machine Learning book if you want to know the basics. I have a summary of the book, if you want, do message me. Here is the outline
Outline:
### 1 Probably Approximately Correct Software
### 2 A Quick Introduction to Machine Learning
### 3 K-Nearest Neighbors
### 4 Naive Bayesian
### 5 Decision Trees and Random Forests
### 6 Hidden Markov Models
### 7 Support Vector Machines
### 8 Neural Networks
### 9 Clustering
### 10 Improving Models and Data Extraction
### 11 Putting it Together: Conclusion
Deus Vult,
Gottfried
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