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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

Intermediate Python Programming for Machine Learning

Prerequisites: Foundational Python skills

Level:

Intermediate

What You'll Learn

Advanced Python Techniques
Apply intermediate Python features like comprehensions, generators, decorators, and OOP principles relevant to data science.
Numerical Computation with NumPy
Perform efficient array operations, indexing, slicing, broadcasting, and linear algebra using NumPy.
Data Manipulation with Pandas
Utilize Pandas Series and DataFrames for loading, cleaning, transforming, grouping, and merging datasets.
Data Visualization
Create informative static plots using Matplotlib and Seaborn for data exploration and communication.
Data Preparation for ML
Implement common data preprocessing steps such as feature scaling, encoding categorical data, and splitting datasets.
Efficient Python Code
Write cleaner, more efficient, and maintainable Python code following best practices for ML projects.

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