Machine Learning with Python

2.

Core Python Libraries for Data Science

2.1.

NumPy for Numerical Computing

2.1.1.

2.1.1.1.

Array Creation Methods

2.1.1.2.

Array Attributes

2.1.1.3.

2.1.1.4.

Array Dimensions

2.1.2.

Array Operations

2.1.2.1.

Indexing and Slicing

2.1.2.2.

Boolean Indexing

2.1.2.3.

2.1.2.4.

Array Reshaping

2.1.2.5.

Array Concatenation

2.1.2.6.

Array Splitting

2.1.3.

Mathematical Operations

2.1.3.1.

Element-wise Operations

2.1.3.2.

Universal Functions

2.1.3.3.

Aggregation Functions

2.1.3.4.

Statistical Functions

2.1.4.

2.1.4.1.

Broadcasting Rules

2.1.4.2.

Broadcasting Examples

2.1.4.3.

Memory Efficiency

2.1.5.

2.1.5.1.

Matrix Operations

2.1.5.2.

Matrix Multiplication

2.1.5.3.

Eigenvalues and Eigenvectors

2.1.5.4.

Matrix Decomposition

2.1.5.5.

Solving Linear Systems

2.1.6.

Random Number Generation

2.1.6.1.

Random Sampling

2.1.6.2.

Random Distributions

2.1.6.3.

Seed Management

2.2.

Pandas for Data Manipulation

2.2.1.

Core Data Structures

2.2.1.1.

2.2.1.1.1.

Creating Series

2.2.1.1.2.

Series Attributes

2.2.1.1.3.

2.2.1.2.

DataFrame Objects

2.2.1.2.1.

Creating DataFrames

2.2.1.2.2.

DataFrame Attributes

2.2.1.2.3.

DataFrame Methods

2.2.1.3.

2.2.1.3.1.

2.2.1.3.2.

2.2.2.

Data Input and Output

2.2.2.1.

2.2.2.1.1.

2.2.2.1.2.

2.2.2.1.3.

2.2.2.1.4.

2.2.2.1.5.

2.2.2.2.

2.2.2.2.1.

2.2.2.2.2.

Compression Options

2.2.3.

Data Exploration

2.2.3.1.

Data Inspection

2.2.3.1.1.

Basic Information

2.2.3.1.2.

2.2.3.1.3.

Summary Statistics

2.2.3.2.

2.2.3.2.1.

Missing Values Analysis

2.2.3.2.2.

Data Distribution

2.2.3.2.3.

Correlation Analysis

2.2.4.

2.2.4.1.

Handling Missing Data

2.2.4.1.1.

Detecting Missing Values

2.2.4.1.2.

Imputation Strategies

2.2.4.1.3.

Dropping Missing Values

2.2.4.2.

Data Type Conversion

2.2.4.2.1.

Numeric Conversion

2.2.4.2.2.

Datetime Conversion

2.2.4.2.3.

Categorical Data

2.2.4.3.

2.2.4.3.1.

Identifying Duplicates

2.2.4.3.2.

Removing Duplicates

2.2.5.

Data Transformation

2.2.5.1.

Applying Functions

2.2.5.1.1.

2.2.5.1.2.

2.2.5.1.3.

Lambda Functions

2.2.5.2.

String Operations

2.2.5.2.1.

2.2.5.2.2.

Regular Expressions

2.2.5.3.

Date and Time Operations

2.2.5.3.1.

Datetime Parsing

2.2.5.3.2.

Time Series Operations

2.2.6.

Data Selection and Filtering

2.2.6.1.

Indexing Methods

2.2.6.1.1.

Label-based Indexing

2.2.6.1.2.

Position-based Indexing

2.2.6.1.3.

Boolean Indexing

2.2.6.2.

2.2.6.2.1.

2.2.6.2.2.

Conditional Selection

2.2.7.

Data Aggregation and Grouping

2.2.7.1.

GroupBy Operations

2.2.7.1.1.

Grouping Strategies

2.2.7.1.2.

Aggregation Functions

2.2.7.1.3.

Transform Operations

2.2.7.1.4.

Filter Operations

2.2.7.2.

2.2.7.2.1.

Creating Pivot Tables

2.2.7.2.2.

Pivot Table Operations

2.2.8.

Data Merging and Joining

2.2.8.1.

2.2.8.1.1.

Vertical Concatenation

2.2.8.1.2.

Horizontal Concatenation

2.2.8.2.

Merging DataFrames

2.2.8.2.1.

2.2.8.2.2.

2.2.8.2.3.

2.2.8.2.4.

2.2.8.3.

Advanced Joining

2.2.8.3.1.

Multiple Key Joins

2.2.8.3.2.

Suffix Handling

2.3.

Matplotlib for Data Visualization

2.3.1.

Matplotlib Architecture

2.3.1.1.

Figure and Axes Objects

2.3.1.2.

Pyplot Interface

2.3.1.3.

Object-Oriented Interface

2.3.2.

Basic Plot Types

2.3.2.1.

2.3.2.1.1.

Single Line Plots

2.3.2.1.2.

Multiple Line Plots

2.3.2.1.3.

2.3.2.2.

2.3.2.2.1.

Basic Scatter Plots

2.3.2.2.2.

Colored Scatter Plots

2.3.2.2.3.

Sized Scatter Plots

2.3.2.3.

2.3.2.3.1.

Vertical Bar Charts

2.3.2.3.2.

Horizontal Bar Charts

2.3.2.3.3.

Grouped Bar Charts

2.3.2.3.4.

Stacked Bar Charts

2.3.2.4.

2.3.2.4.1.

Basic Histograms

2.3.2.4.2.

Multiple Histograms

2.3.2.4.3.

Histogram Customization

2.3.3.

Plot Customization

2.3.3.1.

Titles and Labels

2.3.3.1.1.

2.3.3.1.2.

2.3.3.1.3.

Text Annotations

2.3.3.2.

Colors and Styles

2.3.3.2.1.

2.3.3.2.2.

2.3.3.2.3.

2.3.3.3.

2.3.3.3.1.

Legend Placement

2.3.3.3.2.

Legend Customization

2.3.3.4.

Axes Customization

2.3.3.4.1.

2.3.3.4.2.

2.3.3.4.3.

2.3.4.

Advanced Plotting

2.3.4.1.

2.3.4.1.1.

Creating Subplots

2.3.4.1.2.

2.3.4.1.3.

2.3.4.2.

Multiple Figures

2.3.4.2.1.

Figure Management

2.3.4.2.2.

2.3.4.3.

2.3.4.3.1.

3D Scatter Plots

2.3.4.3.2.

3D Surface Plots

2.4.

Seaborn for Statistical Visualization

2.4.1.

Seaborn Fundamentals

2.4.1.1.

Seaborn vs Matplotlib

2.4.1.2.

Dataset Integration

2.4.1.3.

Styling Options

2.4.2.

Distribution Plots

2.4.2.1.

2.4.2.1.1.

Basic Histograms

2.4.2.1.2.

Kernel Density Estimation

2.4.2.2.

Distribution Comparisons

2.4.2.2.1.

2.4.2.2.2.

2.4.2.2.3.

2.4.2.2.4.

2.4.3.

Relationship Plots

2.4.3.1.

2.4.3.1.1.

Basic Scatter Plots

2.4.3.1.2.

Regression Lines

2.4.3.2.

2.4.3.2.1.

Time Series Plots

2.4.3.2.2.

Multiple Series

2.4.4.

Categorical Plots

2.4.4.1.

2.4.4.1.1.

2.4.4.1.2.

Categorical Bar Plots

2.4.4.2.

2.4.4.2.1.

2.4.4.2.2.

Confidence Intervals

2.4.5.

2.4.5.1.

2.4.5.1.1.

Correlation Matrices

2.4.5.1.2.

Pivot Table Heatmaps

2.4.5.2.

2.4.5.2.1.

Hierarchical Clustering

2.4.5.2.2.

Dendrogram Integration

2.4.6.

Multi-plot Grids

2.4.6.1.

2.4.6.1.1.

2.4.6.1.2.

2.4.6.2.

2.4.6.2.1.

Pairwise Relationships

2.4.6.2.2.

Custom Functions

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1. Foundations of Machine Learning and Python

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