Artificial Intelligence

Anomaly Detection: Finding Outliers in Your Data

Artificial Intelligence Techietory -
Master anomaly detection from first principles. Learn Isolation Forest, Local Outlier Factor, One-Class SVM, statistical methods, and PCA reconstruction error — with complete Python implementations.

Implementing PCA for Data Visualization

Artificial Intelligence Techietory -
Learn to implement PCA for visualization in Python. Compare PCA with t-SNE and UMAP, create biplots, visualize explained variance, plot reconstruction, and apply to images and text data.
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Understanding Eigenvalues and Eigenvectors in PCA

Artificial Intelligence Techietory -
Understand eigenvalues and eigenvectors from scratch with geometric intuition and Python code. Learn how they power PCA, what they mean, and how to compute them — no linear algebra background required.

Principal Component Analysis: Reducing Dimensionality

Artificial Intelligence Techietory -
Master PCA from first principles. Learn variance, covariance, eigenvectors, principal components, variance explained, and how to use PCA for preprocessing and visualization in Python.

DBSCAN: Density-Based Clustering for Complex Shapes

Artificial Intelligence Techietory -
Master DBSCAN from first principles. Learn core points, border points, noise, epsilon and min_samples tuning, comparison with K-Means, and Python implementation with scikit-learn.

Hierarchical Clustering: Building Dendrograms

Artificial Intelligence Techietory -
Master hierarchical clustering from scratch. Learn agglomerative and divisive approaches, linkage criteria, dendrogram interpretation, cutting trees, and Python implementation with scipy and scikit-learn.

Choosing the Number of Clusters: The Elbow Method

Artificial Intelligence Techietory -
Learn every method for choosing k in K-Means clustering: the elbow method, silhouette analysis, gap statistic, Calinski-Harabasz, Davies-Bouldin, and the X-Means algorithm with Python code.

Implementing K-Means Clustering in Python

Artificial Intelligence Techietory -
Learn to implement K-Means clustering in Python from scratch and with scikit-learn. Covers preprocessing, multiple datasets, cluster evaluation, visualization, and production patterns.
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