JavaScript seems to be disabled in your browser. For the best experience on our site, be sure to turn on Javascript in your browser.

Artículo: AMZ-B0FX925WRN

Mathematics and Statistics for Machine Learning: Essential Foundations with Python Examples and Practical Applications

Format:

Paperback

Kindle

Paperback

Detalles del producto

Disponibilidad
En stock

Peso con empaque
0.41 kg

Devolución
Si

Condición
Nuevo

Producto de
Amazon

Viaja desde
USA

Sobre este producto

Are you struggling to break into machine learning and AI, feeling overwhelmed by complex algorithms and data-driven decisions? Do mathematical concepts like linear algebra, calculus, and probability distributions leave you scratching your head, hindering your progress in building intelligent systems? You're not alone—many aspiring data scientists face these barriers, wasting time on trial-and-error without solid mathematical foundations.Enter Mathematics and Statistics for Machine Learning—your ultimate roadmap to conquering these challenges with Essential Foundations with Python Examples and Practical Applications. This comprehensive guide empowers you to build the mathematical and statistical skills necessary for data science and AI development. Dive into vectors, matrices, derivatives, gradients, probability distributions, and statistical inference, all while implementing concepts in Python using NumPy, SciPy, and scikit-learn.Transform Your Skills with Actionable Knowledge:Master Linear Algebra and Calculus: Understand matrix operations, eigenvalues, vector spaces, derivatives, and optimization techniques like gradient descent—the mathematical backbone of neural networks and machine learning algorithms.Harness Probability and Statistics: Work with probability distributions, Bayes' theorem, sampling methods, hypothesis testing, and confidence intervals to make data-driven decisions and validate model performance.Build Predictive Models: Implement linear regression, polynomial regression, logistic regression, and understand the mathematics behind these fundamental ML algorithms for forecasting and classification tasks.Apply Advanced Techniques: Use principal component analysis (PCA) for dimensionality reduction, understand regularization methods (L1/L2) to prevent overfitting, and grasp the mathematical principles behind model evaluation metrics.No more passive reading—work through hands-on exercises and real-world projects including sales forecasting, customer segmentation, classification problems, and optimization challenges. Each chapter bridges theory with practice, showing you exactly how mathematical concepts translate into working Python code.Whether you're a beginner building foundational knowledge or a professional filling gaps in your mathematical understanding, this practical guide provides clear explanations, visual intuitions, and executable code that demystify the math behind machine learning.Ready to unlock your potential and truly understand what's happening under the hood of ML algorithms? Stop feeling intimidated by the mathematics and start building with confidence.Grab your copy now and transform from someone who uses ML libraries blindly to someone who understands, adapts, and innovates!

₡ 9,496

₡ 9,496

Llega en 5 a 10 días hábiles

Con envío

Tienes garantia de entrega

Medios de pago

Cantidad

Este producto viaja de USA

a tus manos en

Medios de pago