├── .gitignore ├── 00-Datasets ├── 20021010_easy_ham.tar.bz2 ├── 20021010_hard_ham.tar.bz2 ├── 20021010_spam.tar.bz2 ├── imdb-5000-movie-dataset.zip ├── mushroom-classification.zip └── student-alcohol-consumption.zip ├── 01-Data-Science-From-Scratch ├── 00-원본소스코드(source_code) │ ├── LICENSE │ ├── README.md │ ├── code-python3 │ │ ├── README.md │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── gradient_descent.cpython-35.pyc │ │ │ ├── linear_algebra.cpython-35.pyc │ │ │ ├── machine_learning.cpython-35.pyc │ │ │ └── stats.cpython-35.pyc │ │ ├── charts.py │ │ ├── clustering.py │ │ ├── colon_delimited_stock_prices.txt │ │ ├── comma_delimited_stock_prices.csv │ │ ├── comma_delimited_stock_prices.txt │ │ ├── databases.py │ │ ├── decision_trees.py │ │ ├── egrep.py │ │ ├── getting_data.py │ │ ├── gradient_descent.py │ │ ├── hypothesis_and_inference.py │ │ ├── introduction.py │ │ ├── line_count.py │ │ ├── linear_algebra.py │ │ ├── logistic_regression.py │ │ ├── machine_learning.py │ │ ├── mapreduce.py │ │ ├── most_common_words.py │ │ ├── multiple_regression.py │ │ ├── naive_bayes.py │ │ ├── natural_language_processing.py │ │ ├── nearest_neighbors.py │ │ ├── network_analysis.py │ │ ├── neural_networks.py │ │ ├── plot_state_borders.py │ │ ├── probability.py │ │ ├── recommender_systems.py │ │ ├── simple_linear_regression.py │ │ ├── states.txt │ │ ├── stats.py │ │ ├── stocks.txt │ │ ├── tab_delimited_stock_prices.txt │ │ ├── visualizing_data.py │ │ └── working_with_data.py │ ├── code │ │ ├── __init__.py │ │ ├── charts.py │ │ ├── clustering.py │ │ ├── colon_delimited_stock_prices.txt │ │ ├── comma_delimited_stock_prices.csv │ │ ├── comma_delimited_stock_prices.txt │ │ ├── databases.py │ │ ├── decision_trees.py │ │ ├── egrep.py │ │ ├── getting_data.py │ │ ├── gradient_descent.py │ │ ├── hypothesis_and_inference.py │ │ ├── introduction.py │ │ ├── line_count.py │ │ ├── linear_algebra.py │ │ ├── logistic_regression.py │ │ ├── machine_learning.py │ │ ├── mapreduce.py │ │ ├── most_common_words.py │ │ ├── multiple_regression.py │ │ ├── naive_bayes.py │ │ ├── natural_language_processing.py │ │ ├── nearest_neighbors.py │ │ ├── network_analysis.py │ │ ├── neural_networks.py │ │ ├── plot_state_borders.py │ │ ├── probability.py │ │ ├── recommender_systems.py │ │ ├── simple_linear_regression.py │ │ ├── states.txt │ │ ├── statistics.py │ │ ├── stocks.txt │ │ ├── tab_delimited_stock_prices.txt │ │ ├── visualizing_data.py │ │ └── working_with_data.py │ └── links.md ├── 01-들어가기(Introduction) │ ├── 0. Datum inc.ipynb │ ├── 0.1 Datum inc by me.ipynb │ ├── 1. Hello, World.ipynb │ ├── 2. Random Stuff.ipynb │ ├── 3. Replacing a list comprehension.ipynb │ └── 4. Using a class.ipynb ├── 02-시각화(Visualization) │ ├── matplot.ipynb │ └── visualization with lightning.ipynb ├── 03-선형대수(Linear_Algebra) │ └── vector.ipynb ├── 04-확률&통계(Probability&Statistics) │ ├── 0. 통계(Statistics).ipynb │ ├── 1. 확률(Probability).ipynb │ ├── 2. 가설과 추론(Hypothesis and Inference).ipynb │ └── 부록(Probability, Specific case is never happened in continuous distribution).ipynb ├── 05-최적화_알고리즘(Optimization_Algorithm) │ └── 1. Gradiant Descent.ipynb ├── 06-데이터_탐색(Data_exploration) │ ├── 1. Data exploration.ipynb │ ├── __pycache__ │ │ ├── linear_algebra.cpython-35.pyc │ │ ├── probability.cpython-35.pyc │ │ └── stats.cpython-35.pyc │ ├── linear_algebra.py │ ├── probability.py │ └── stats.py ├── 07-데이터_수집&처리(Collecting&Processing_Data) │ ├── 0. Crawling.ipynb │ ├── 0.1. Scraped-blogdata.txt │ ├── 1. play with sys.ipynb │ ├── bible.txt │ ├── colon_delimited_stock_prices.txt │ ├── comma_delimited_stock_prices.txt │ ├── egrep.py │ ├── emails.txt │ ├── line.py │ ├── most_common_words.py │ ├── new-file.txt │ ├── readnwrite.py │ ├── tab_delimited_stock_prices.txt │ └── wrote-some-file └── 08-기계학습(Machine_Learning) │ ├── 0. Machinelearning.ipynb │ ├── 1. k-NearestNeighbor.ipynb │ ├── 2. Linear regression.ipynb │ ├── 4. Logistic Regression.ipynb │ ├── 5. Multiple linear regression.ipynb │ ├── 6. Naive Bayes.ipynb │ ├── __pycache__ │ ├── gradient_descent.cpython-35.pyc │ ├── linear_algebra.cpython-35.pyc │ ├── probability.cpython-35.pyc │ ├── simple_linear_regression.cpython-35.pyc │ └── stats.cpython-35.pyc │ ├── gradient_descent.py │ ├── linear_algebra.py │ ├── multiple_regression.py │ ├── probability.py │ ├── simple_linear_regression.py │ └── stats.py ├── 02-Tensorflow ├── 00-Hello_Tensor&Linear_Regression │ └── Hello, World.ipynb ├── 01-Cost │ ├── .ipynb_checkpoints │ │ └── minimize_cost-checkpoint.ipynb │ └── minimize_cost.ipynb ├── 02-Multivariable_Linear_Regression │ └── Multivariable linear regression.ipynb ├── 03-Logistic_Classification │ ├── Logistic Classification.ipynb │ └── train.txt ├── 04-Multinomial_Classification │ ├── Multinomial classification.ipynb │ └── train.txt ├── 05-Tip&Tricks │ ├── 1. 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