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Artículo: AMZ-B0DHDV4PGR

Ultimate Machine Learning Job Interview Questions Workbook: Brief Crash Courses and Real Interview Questions taking you from Beginner to FAANG & Wall Street Offers (Mastering Machine Learning)

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Hardcover

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Paperback

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En stock

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1.05 kg

Devolución
Sí

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Nuevo

Producto de
Amazon

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USA

Sobre este producto

Dive into a treasure trove of meticulously curated knowledge designed to propel you from a beginner to securing offers from the industry's giants like FAANG and Wall Street. This workbook combines brief crash courses on essential topics with real-world interview questions, helping you navigate even the toughest interview scenarios. Key Features: - Comprehensive Coverage: From foundational concepts to advanced topics, this workbook covers an extensive range of subjects crucial for machine learning roles. - Real Interview Questions: Prepare with confidence using questions based on what actual top-tier companies ask. - Crash Courses: Brief yet thorough insights into each topic ensure you understand the core concepts rapidly. - Industry Application: Learn how various machine learning techniques are applied across different industries. - Optimized Learning: The workbook's structured approach enables you to focus on key areas and polish your skills comprehensively. What You Will Learn: - Grasp the principles and applications of Gradient Boosting Machines - Master the kernel trick in Support Vector Machines for high-dimensional classification - Understand backpropagation in neural networks with detailed walkthroughs - Analyze the workings of convolutional layers in CNNs - Explore Recurrent Neural Networks and the functionality of LSTM cells - Unpack attention mechanisms crucial for natural language processing - Harness the power of transfer learning and its popular architectures - Perform Bayesian inference for predictive modeling - Implement Markov Chain Monte Carlo Methods for complex sampling - Comprehend the mathematical framework of Variational Autoencoders - Delve into adversarial training with Generative Adversarial Networks - Utilize Principal Component Analysis for dimensionality reduction and anomaly detection - Apply k-Nearest Neighbors for effective anomaly detection - Break down Q-Learning in reinforcement learning - Evaluate Proximal Policy Optimization in reinforcement learning contexts - Compare Gini Impurity versus Entropy in Decision Trees - Evaluate the out-of-bag error in Random Forests - Understand Regularization Techniques in XGBoost - Leverage Matrix Factorization for Recommender Systems - Implement Hierarchical and DBSCAN Clustering Algorithms - Navigate Expectation-Maximization for parameter estimation - Perform topic modeling using Latent Dirichlet Allocation - Explore Ensemble Methods like Stacking for prediction enhancement - Optimize with Simulated Annealing inspired by metallurgy - Differentiate between Ridge and Lasso Regression for feature selection - Investigate Elastic Net Regularization for improved predictions - Learn Fisher's Linear Discriminant Analysis for class separation - Forecast with Kalman Filters and ARIMA for time-series analysis - Deconstruct time series using Seasonal Decomposition (STL) - Apply Recursive Feature Elimination for selecting influential features - Utilize exponential smoothing for precise time series forecasting

$102,02

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$102,02

49% OFF

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