Advanced Python Programming for Machine Learning
Chapter 1: Advanced Python Constructs for ML Pipelines
Advanced Generator Techniques for Memory-Efficient Data Handling
Context Managers for Resource Management in ML Workflows
Functional Programming Patterns in Python for Data Transformation
Higher-Order Functions and Closures in ML
Working with Iterators and Itertools for Complex Sequences
Hands-on Practical: Building a Data Pipeline Component
Chapter 2: Performance Optimization in Python for ML
Profiling Python Code: Identifying Bottlenecks
Optimizing NumPy Operations
Efficient Pandas Usage for Large Datasets
Introduction to Cython for Speeding Up Python Code
Using Numba for Just-In-Time Compilation
Understanding Python's Global Interpreter Lock (GIL)
Memory Profiling and Optimization Techniques
Hands-on Practical: Optimizing a Feature Engineering Function
Chapter 3: Metaprogramming and Python Internals for ML Frameworks
Advanced Decorator Applications
Understanding and Implementing Descriptors
Metaclasses: Customizing Class Creation
Dynamic Code Generation and Execution
Introspection and Reflection Techniques
Attribute Access Customization (__getattr__, __getattribute__)
Hands-on Practical: Building a Plugin System with Metaclasses
Chapter 4: Advanced Data Structures and Algorithms in Python for ML
Implementing Trees for Hierarchical Data
Graph Data Structures and Algorithms
Using Priority Queues and Heaps
Spatial Data Structures (Quadtrees, Octrees)
Probabilistic Data Structures (Bloom Filters, HyperLogLog)
Algorithm Design Paradigms (Greedy, Dynamic Programming) in ML
Hands-on Practical: Implementing a k-d Tree for Nearest Neighbor Search
Chapter 5: Concurrency and Parallelism in Python for ML Workloads
Threading vs Multiprocessing for ML Tasks
The multiprocessing Module for Parallel Execution
Inter-Process Communication (IPC) Techniques
Using concurrent.futures for High-Level Concurrency
Introduction to asyncio for Asynchronous ML Operations
Synchronization Primitives (Locks, Semaphores, Events)
Debugging Concurrent Python Applications
Hands-on Practical: Parallelizing Data Preprocessing
Chapter 6: Building Custom ML Estimators and Transformers with Python
Scikit-learn API and Estimator Interface
Implementing Custom Transformers
Developing Custom Estimators
Composition and Inheritance for ML Components
Parameter Validation and Management
Integrating Custom Components into Pipelines
Testing Custom ML Components
Hands-on Practical: Building a Custom Ensemble Estimator
Algorithm Design Paradigms (Greedy, Dynamic Programming) in ML
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- Introduction to Algorithms, Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein, 2022 (MIT Press) - A foundational textbook that rigorously covers algorithm design paradigms, including greedy algorithms and dynamic programming, with comprehensive theoretical analysis and examples.
- Reinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. Barto, 2018 (MIT Press) - The seminal work in reinforcement learning, explaining the role of dynamic programming in fundamental algorithms like Value Iteration and Policy Iteration for optimal control.
- Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, Daniel Jurafsky and James H. Martin, 2025 (Stanford University) - A comprehensive resource for natural language processing, detailing dynamic programming applications such as Levenshtein distance for sequence alignment and the Viterbi algorithm for Hidden Markov Models.
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman, 2009 (Springer) DOI: 10.1007/978-0-387-84858-7 - A classic machine learning textbook that discusses various algorithms, including decision trees and feature selection methods, which often employ greedy strategies in their construction.