pure python library code
Data science is a field that combines programming, statistics, and subject expertise.
Mastering all three is hard.
The goal is to provide hands-on examples and encourage learning by doing.
Build data science tools and algorithms by hand rather than relying on pre-built libraries.
This method aims to deepen understanding,
even though the custom-built tools may only work well on small datasets.
c01_intro
c02_crash_course
c03_visualizing_data
c04_linear_algebra
c05_statistics
c06_probability
c07_hypothesis_and_inference
c08_gradient_descent
c09_getting_data
c10_working_with_data
c11_machine_learning
c12_k_nearest_neighbors
c13_naive_bayes
c14_simple_linear_regression
c15_multiple_regression
c16_logistic_regression
c17_decision_trees
c18_neural_networks
c19_deep_learning
c20_clustering
c21_natural_language_processing
c22_network_analysis
c23_recommender_systems
c24_databases
c25_mapreduce
c26_data_ethics
c27_go_forth
