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Welcome to Data Mining Course
Introduction to NumPy
Introduction to NumPy
Understanding Data Types in Python
The Basics of NumPy Arrays
Computation on NumPy Arrays: Universal Functions
Aggregations: min, max, and Everything in Between
Computation on Arrays: Broadcasting
Comparisons, Masks, and Boolean Logic
Fancy Indexing
Sorting Arrays
Structured Data: NumPy’s Structured Arrays
Data Manipulation with Pandas
Data Manipulation with Pandas
Introducing Pandas Objects
Data Indexing and Selection
Operating on Data in Pandas
Handling Missing Data
Hierarchical Indexing
Combining Datasets: concat and append
Combining Datasets: merge and join
Aggregation and Grouping
Pivot Tables
Vectorized String Operations
Working with Time Series
High-Performance Pandas: eval and query
Further Resources
Visualization with Matplotlib
Visualization with Matplotlib
Simple Line Plots
Simple Scatter Plots
Visualizing Uncertainties
Density and Contour Plots
Histograms, Binnings, and Density
Customizing Plot Legends
Customizing Colorbars
Multiple Subplots
Text and Annotation
Customizing Ticks
Customizing Matplotlib: Configurations and Stylesheets
Three-Dimensional Plotting in Matplotlib
Visualization with Seaborn
Further Resources
Machine Learning
Machine Learning
What Is Machine Learning?
Introducing Scikit-Learn
Hyperparameters and Model Validation
Feature Engineering
In Depth: Naive Bayes Classification
In Depth: Linear Regression
In Depth: Support Vector Machines
In Depth: Decision Trees and Random Forests
In Depth: Principal Component Analysis
In Depth: Manifold Learning
In Depth: k-Means Clustering
In Depth: Gaussian Mixture Models
In Depth: Kernel Density Estimation
Application: A Face Detection Pipeline
Repository
Open issue
Index
