Price: ₹954.08 - ₹532.80
(as of Jan 12, 2026 13:58:01 UTC – Details)
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
ASIN : B0DNWQ6HDM
Language : English
File size : 10.9 MB
Enhanced typesetting : Not Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 509 pages
Format : Print Replica
Best Sellers Rank: #141,155 in Kindle Store (See Top 100 in Kindle Store) #696 in Mathematics (Kindle Store) #1,172 in Self-Help for the Workplace #4,331 in Computers & Technology eBooks
