Linear Regression in Python: From Data to Model
Build a linear regression model from scratch using scikit-learn, with data visualization, feature selection, and model evaluation metrics.
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Build a linear regression model from scratch using scikit-learn, with data visualization, feature selection, and model evaluation metrics.
Explore reinforcement learning fundamentals — agents, environments, states, actions, Q-learning, SARSA, Actor-Critic, and deep RL approaches.
Understand unsupervised learning methods including clustering, dimensionality reduction, anomaly detection, and generative models with practical examples.
Explore derivatives, integrals, multivariate calculus, optimization, and differential equations with Python implementations using SymPy and NumPy.
Learn essential statistics concepts — mean, median, mode, variance, standard deviation, percentiles, quartiles, and z-scores with Python implementations.
Understand vectors, matrices, transpose, inverse, determinant, trace, dot product, and eigenvalues with NumPy implementations for data science.
Master SQL from basics to advanced — SELECT, JOIN, GROUP BY, ORDER BY, indexes, date functions, and more using SQLite with Python.
Learn to create compelling data visualizations using Matplotlib and Seaborn — line plots, scatter plots, bar charts, histograms, heatmaps, and more.
Master Pandas for data manipulation — reading data, selecting columns, grouping, merging DataFrames, handling missing values, and working with dates.
Learn NumPy essentials — arrays, shapes, reshaping, slicing, stacking, broadcasting, universal functions, and image processing with practical examples.
A deep dive into Python lists, tuples, sets, dictionaries, and functions with comprehensive code examples and practical exercises.
Learn Python fundamentals including identifiers, data types (int, float, str, list, tuple, set, dict), operators, and basic operations with hands-on examples.