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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

Advanced Python Programming for Machine Learning

Prerequisites: Strong proficiency in Python programming and familiarity with core machine learning concepts and libraries (e.g., NumPy, Pandas, Scikit-learn).

Level: Advanced

What You'll Learn

Performance Optimization
Apply profiling and optimization techniques to accelerate Python code for ML tasks.
Memory Management
Implement memory-efficient data handling using advanced generator patterns and data structures.
Metaprogramming
Utilize decorators, descriptors, and metaclasses for building flexible ML frameworks and tools.
Concurrency and Parallelism
Implement concurrent and parallel processing techniques for computationally intensive ML workloads.
Custom ML Components
Develop custom Scikit-learn compatible estimators and transformers using advanced Python features.
Python Internals
Understand Python's internal mechanisms (like the GIL, CPython extensions) relevant to ML performance.

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