Intermediate Python Programming for Machine Learning
Chapter 1: Advanced Python Constructs for Data Science
Review of Python Fundamentals
List Comprehensions and Generator Expressions
Working with Iterators and Generators
Advanced Function Arguments
Decorators for Code Reusability
Context Managers for Resource Management
Object-Oriented Programming Principles in ML
Error Handling and Exception Management
Practice: Implementing Advanced Python Techniques
Quiz for Chapter 1
Chapter 2: Numerical Computing with NumPy
Introduction to NumPy Arrays
Array Creation Techniques
Indexing and Slicing NumPy Arrays
Array Mathematics and Universal Functions
Broadcasting Rules and Applications
Linear Algebra Operations with NumPy
Statistical Functions in NumPy
Reading and Writing Array Data to Files
Hands-on Practical: NumPy Array Manipulations
Quiz for Chapter 2
Chapter 3: Data Manipulation with Pandas
Introduction to Pandas Data Structures
Loading Data from Various Sources
Data Indexing and Selection
Handling Missing Data
Data Cleaning and Transformation Techniques
Grouping and Aggregation Operations
Merging, Joining, and Concatenating DataFrames
Time Series Data Handling in Pandas
Practice: Data Wrangling with Pandas
Quiz for Chapter 3
Chapter 4: Data Visualization with Matplotlib and Seaborn
Fundamentals of Matplotlib Plotting
Creating Common Plot Types
Customizing Plots
Working with Subplots
Introduction to Seaborn for Statistical Visualization
Creating Advanced Plots with Seaborn
Visualizing Distributions and Relationships
Saving Plots for Reports and Presentations
Hands-on Practical: Visual Data Exploration
Quiz for Chapter 4
Chapter 5: Preparing Data for Machine Learning
Overview of the Machine Learning Workflow
Feature Engineering Concepts
Handling Categorical Data
Feature Scaling and Normalization Methods
Splitting Data into Training and Testing Sets
Introduction to Scikit-learn Pipelines
Applying Data Transformations Consistently
Practice: Building a Data Preparation Pipeline
Quiz for Chapter 5
Chapter 6: Writing Efficient and Maintainable Python Code for ML
Code Styling and Readability
Structuring Machine Learning Projects
Writing Effective Functions and Modules
Introduction to Virtual Environments
Profiling Python Code for Performance
Techniques for Optimizing NumPy and Pandas
Introduction to Unit Testing for ML
Version Control Basics with Git
Practice: Refactoring and Optimizing ML Code Snippets
Quiz for Chapter 6
Applying Data Transformations Consistently
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- Preprocessing data, scikit-learn developers, 2023 (scikit-learn) - Official guide to Scikit-learn's preprocessing modules and pipelines, illustrating correct usage patterns for data transformation and preventing data leakage.
- Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Aurélien Géron, 2022 (O'Reilly Media) - Practical guide to machine learning with Scikit-learn, featuring concrete examples on data preprocessing, feature scaling, and preventing data leakage through pipelines.
- An Introduction to Statistical Learning: With Applications in R, Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani, 2021 (Springer) - Foundational text on statistical learning methods, covering the principles of data splitting and the necessity of independent evaluation to prevent over-optimistic model assessment.
- Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists, Alice Zheng and Amanda Casari, 2018 (O'Reilly Media) - Dedicated resource on feature engineering strategies, outlining methods for preparing data while maintaining transformation consistency across training and test sets.