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
Code Styling and Readability
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- PEP 8 -- Style Guide for Python Code, Guido van Rossum, Barry Warsaw, Alyssa Coghlan, 2001 - This is the definitive style guide for Python code, outlining conventions for layout, naming, and documentation to enhance readability and consistency.
- Effective Python: 90 Specific Ways to Write Better Python, Brett Slatkin, 2019 (Addison-Wesley Professional) - Offers practical advice and best practices for writing high-quality, efficient, and maintainable Python code, complementing style guide adherence.
- PEP 257 -- Docstring Conventions, David Goodger, Guido van Rossum, 2001 - Specifies standard conventions for docstrings in Python, which are essential for documenting modules, classes, functions, and methods effectively.
- Black - The Uncompromising Code Formatter, The Black Developers, 2024 - Provides an automated code formatting solution that strictly adheres to style standards, ensuring uniformity across codebases with minimal configuration.