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Item #:
56696640
Machine Learning: A First Course for Engineers and Scientists
98% of respondents would recommend this to a friend
Item #:
56696640
ZAR 512
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Covers a selection of supervised machine learning methods from fundamental to advanced, plus commonly-used unsupervised methods, with careful explanations and pseudo-code provided for all methods.
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What Stands Out
Targeted Learning
This course is specifically designed for engineers and scientists, providing focused content that applies machine learning concepts directly to relevant fields and practical applications.
Hands-On Approach
Offers interactive exercises and real-world projects, allowing learners to gain practical experience while solidifying their understanding of complex machine learning algorithms and techniques.
Comprehensive Coverage
Covers a wide range of machine learning topics, from foundational theories to advanced applications, ensuring a deep and thorough understanding suitable for both beginners and experienced practitioners.
Product Details
| Item Weight | 1.5 lbs (680 grams) |
Who Should Buy?
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Engineering Students
Ideal for engineering students seeking a foundational understanding of machine learning principles and applications in various fields.
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Data Scientists
Beneficial for aspiring data scientists wanting to reinforce their skills with structured learning on machine learning techniques.
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Technical Professionals
Useful for professionals in tech industries looking to apply machine learning concepts to enhance their work and projects.
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Absolute Beginners
Not suitable for those without prior knowledge of programming or mathematics, as prerequisites are assumed for effective learning.
Product Description
Machine Learning: A First Course for Engineers and Scientists
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- Excellent build quality
- User-friendly design
- Great performance
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- Stylish appearance
Cons
- Limited color options.
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ZAR 512
Order now and get it around Friday, November 06
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Features & Benefits
- k-NN, decision trees, linear and logistic regression, deep neural networks, support vector machines, Gaussian processes, random forests, boosting, generative modeling, k-means, PCA, autoencoders, and generative adversarial networks
- Connections between methods, general concepts like loss functions and bias-variance decomposition, useful tools such as regularization and cross validation are discussed
- Final chapters offer advice for solving real-world supervised machine learning problems and touch on ethical aspects of modern machine learning
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