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Python for AI
1. Python Fundamentals for AI
2. Essential Libraries for Data Science and AI
3. Machine Learning with Scikit-Learn
4. Deep Learning Frameworks
5. Specialized AI Applications
6. Model Deployment and Production
Essential Libraries for Data Science and AI
NumPy for Numerical Computing
NumPy Arrays
Understanding ndarray
Array Properties
shape Attribute
dtype Attribute
ndim Attribute
size Attribute
Array Memory Layout
Array Creation
Creating Arrays from Lists
Creating Arrays from Tuples
Array Creation Functions
zeros()
ones()
empty()
eye()
arange()
linspace()
logspace()
Random Array Generation
Array Initialization Patterns
Array Indexing and Slicing
Basic Indexing
Negative Indexing
Slicing Syntax
Multi-dimensional Indexing
Boolean Indexing
Fancy Indexing
Index Arrays
Array Operations
Element-wise Operations
Arithmetic Operations
Comparison Operations
Logical Operations
Broadcasting Rules
Broadcasting Examples
Array Manipulation
Reshaping Arrays
Flattening Arrays
Transposing Arrays
Concatenating Arrays
Splitting Arrays
Stacking Arrays
Mathematical Functions
Universal Functions (ufuncs)
Trigonometric Functions
Exponential and Logarithmic Functions
Statistical Functions
mean()
median()
std()
var()
min() and max()
sum()
Aggregation Functions
Reduction Operations
Linear Algebra
Matrix Operations
Matrix Multiplication
Dot Product
Cross Product
Matrix Decomposition
Eigenvalues and Eigenvectors
Solving Linear Systems
Advanced NumPy Features
Structured Arrays
Memory-mapped Files
Performance Optimization
Vectorization Techniques
Pandas for Data Manipulation
Core Data Structures
Series
Creating Series
Series Indexing
Series Operations
Series Methods
Series Attributes
DataFrame
Creating DataFrames
DataFrame Structure
Column Operations
Row Operations
DataFrame Attributes
Index Objects
Index Types
Index Operations
MultiIndex
Data Input and Output
Reading Data
CSV Files
Excel Files
JSON Files
SQL Databases
Web APIs
HTML Tables
Writing Data
CSV Output
Excel Output
JSON Output
SQL Database Output
Data Format Conversion
Data Selection and Filtering
Column Selection
Row Selection
Label-based Selection (loc)
Position-based Selection (iloc)
Boolean Indexing
Query Method
Conditional Selection
Multi-level Selection
Data Cleaning and Preparation
Missing Data Handling
Detecting Missing Values
Filling Missing Values
Dropping Missing Values
Interpolation Methods
Duplicate Data
Identifying Duplicates
Removing Duplicates
Duplicate Handling Strategies
Data Type Conversion
String Data Cleaning
Outlier Detection and Treatment
Data Transformation
Applying Functions
apply() Method
map() Method
applymap() Method
Data Aggregation
groupby() Operations
Aggregation Functions
Custom Aggregations
Multiple Aggregations
Data Reshaping
Pivoting Data
Melting Data
Stacking and Unstacking
Data Merging and Joining
merge() Function
join() Method
concat() Function
Merge Types
Time Series Analysis
DateTime Data Types
DateTime Indexing
Time Series Operations
Resampling
Frequency Conversion
Rolling Windows
Expanding Windows
Time Zone Handling
Advanced Pandas Features
Categorical Data
Sparse Data
Performance Optimization
Memory Usage Optimization
Parallel Processing
Matplotlib for Data Visualization
Matplotlib Basics
Figure and Axes Objects
Plotting Interface
Object-oriented Interface
Pyplot Interface
Basic Plotting
Line Plots
Scatter Plots
Bar Charts
Histograms
Box Plots
Pie Charts
Plot Customization
Colors and Styles
Markers and Line Styles
Labels and Titles
Legends
Annotations
Text and Fonts
Axes and Layout
Multiple Subplots
Subplot Arrangements
Shared Axes
Axis Limits and Scaling
Ticks and Tick Labels
Grid Lines
Advanced Plotting Features
3D Plotting
Contour Plots
Heatmaps
Error Bars
Fill Between
Animation
Saving and Exporting
File Formats
Resolution Settings
Figure Size Control
Seaborn for Statistical Visualization
Seaborn Basics
Seaborn vs Matplotlib
Setting Styles and Themes
Color Palettes
Statistical Plots
Distribution Plots
Regression Plots
Categorical Plots
Matrix Plots
Multi-plot Grids
FacetGrid
PairGrid
JointGrid
Advanced Seaborn Features
Custom Color Maps
Statistical Annotations
Plot Aesthetics
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