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18 articles tagged #Data Science.
P.01Explore dimensionality reduction techniques — PCA, LDA, t-SNE, and autoencoders — for improving model performance and data visualization.
P.02Discover the key advantages of Random Forest algorithms — high accuracy, resistance to overfitting, feature importance, and handling missing data.
P.03Learn how centroid-based clustering algorithms like K-means partition datasets into meaningful groups based on distance metrics.
P.04A guide to clustering algorithm types — partition-based, hierarchical, density-based, and model-based — with use cases and selection criteria.
P.05Explore time series forecasting methods including ARIMA, exponential smoothing, and seasonal decomposition for real-world prediction tasks.
P.06Build an insurance cost prediction model using multivariate linear regression with one-hot encoding, evaluation metrics, and residual analysis.
P.07Build a linear regression model from scratch using scikit-learn, with data visualization, feature selection, and model evaluation metrics.
P.08Understand unsupervised learning methods including clustering, dimensionality reduction, anomaly detection, and generative models with practical examples.
P.09Explore derivatives, integrals, multivariate calculus, optimization, and differential equations with Python implementations using SymPy and NumPy.
P.10Learn essential statistics concepts — mean, median, mode, variance, standard deviation, percentiles, quartiles, and z-scores with Python implementations.
P.11Understand vectors, matrices, transpose, inverse, determinant, trace, dot product, and eigenvalues with NumPy implementations for data science.
P.12Learn to create compelling data visualizations using Matplotlib and Seaborn — line plots, scatter plots, bar charts, histograms, heatmaps, and more.
P.13Master Pandas for data manipulation — reading data, selecting columns, grouping, merging DataFrames, handling missing values, and working with dates.
P.14Learn NumPy essentials — arrays, shapes, reshaping, slicing, stacking, broadcasting, universal functions, and image processing with practical examples.
P.15EditorPickA structured 100-day data science bootcamp roadmap covering Python, statistics, machine learning, deep learning, and real-world projects.
P.16EditorPickA comprehensive guide covering 10 regression types — linear, polynomial, logistic, ridge, lasso, elastic net, and more — with Python code examples and selection criteria.
P.17Understand the key differences between artificial intelligence, machine learning, and deep learning with clear definitions, examples, and real-world applications.
P.18EditorPickA comprehensive guide to EDA covering visualization techniques, summary statistics, correlation analysis, data cleaning, PCA, anomaly detection, and feature engineering.