Data Science & Machine Learning

NumPy Arrays: Create, Index, Slice, and Reshape — interactive tutorial with runnable examples and practice exercises.

NumPy Operations: Element-wise Math, Aggregations, and ufuncs — interactive tutorial with runnable examples and practice exercises.

NumPy Broadcasting: Operate on Arrays of Different Shapes — interactive tutorial with runnable examples and practice exercises.

NumPy Linear Algebra: Dot Products, Inverses, and Eigenvalues — interactive tutorial with runnable examples and practice exercises.

Pandas: Create, Load, and Explore DataFrames — interactive tutorial with runnable examples and practice exercises.

Pandas Indexing: loc, iloc, Boolean Indexing, and Selection — interactive tutorial with runnable examples and practice exercises.

Pandas Data Cleaning: Missing Values, Duplicates, and Outliers — interactive tutorial with runnable examples and practice exercises.

Pandas merge(), join(), concat(): Combine DataFrames Like SQL — interactive tutorial with runnable examples and practice exercises.

Pandas GroupBy: Split-Apply-Combine for Powerful Aggregations — interactive tutorial with runnable examples and practice exercises.

Pandas apply(), map(), transform(): Custom Data Transformations — interactive tutorial with runnable examples and practice exercises.

Pandas String and DateTime Operations for Real-World Data — interactive tutorial with runnable examples and practice exercises.

Pandas Pivot Tables and Cross-Tabulation for Business Analysis — interactive tutorial with runnable examples and practice exercises.

Matplotlib: Create Line, Bar, Scatter, and Pie Charts — interactive tutorial with runnable examples and practice exercises.

Advanced Matplotlib: Subplots, Dual Axes, Styles, Annotations — interactive tutorial with runnable examples and practice exercises.

Data Visualization: Choose the Right Chart and Tell a Story — interactive tutorial with runnable examples and practice exercises.

SciPy Statistics: Distributions, Hypothesis Tests, and Correlations — interactive tutorial with runnable examples and practice exercises.

Build Your First ML Model: Linear Regression with scikit-learn — interactive tutorial with runnable examples and practice exercises.

Python Classification: Build a Classifier with scikit-learn — interactive tutorial with runnable examples and practice exercises.

Evaluating ML Models: Cross-Validation, Metrics, and Overfitting — interactive tutorial with runnable examples and practice exercises.

K-Means Clustering: Find Patterns in Data Without Labels — interactive tutorial with runnable examples and practice exercises.