├── .gitignore ├── .gitmodules ├── README.md ├── good-articles.md ├── interview ├── README.md ├── dataswati │ ├── le_test_de_biblios_de_Dataswati.html │ ├── le_test_de_biblios_de_Dataswati.md │ ├── test_biblios_python_thi.ipynb │ └── test_python_dataswati_2019 │ │ ├── test_python_data_science_Dataswati-v2.ipynb │ │ └── wave_data │ │ ├── signal_1.txt │ │ ├── signal_10.txt │ │ ├── signal_100.txt │ │ ├── signal_11.txt │ │ ├── signal_12.txt │ │ ├── signal_13.txt │ │ ├── signal_14.txt │ │ ├── signal_15.txt │ │ ├── signal_16.txt │ │ ├── signal_17.txt │ │ ├── signal_18.txt │ │ ├── signal_19.txt │ │ ├── signal_2.txt │ │ ├── signal_20.txt │ │ ├── signal_21.txt │ │ ├── signal_22.txt │ │ ├── signal_23.txt │ │ ├── signal_24.txt │ │ ├── signal_25.txt │ │ ├── signal_26.txt │ │ ├── signal_27.txt │ │ ├── signal_28.txt │ │ ├── signal_29.txt │ │ ├── signal_3.txt │ │ ├── signal_30.txt │ │ ├── signal_31.txt │ │ ├── signal_32.txt │ │ ├── signal_33.txt │ │ ├── signal_34.txt │ │ ├── signal_35.txt │ │ ├── signal_36.txt │ │ ├── signal_37.txt │ │ ├── signal_38.txt │ │ ├── signal_39.txt │ │ ├── signal_4.txt │ │ ├── signal_40.txt │ │ ├── signal_41.txt │ │ ├── signal_42.txt │ │ ├── signal_43.txt │ │ ├── signal_44.txt │ │ ├── signal_45.txt │ │ ├── signal_46.txt │ │ ├── signal_47.txt │ │ ├── signal_48.txt │ │ ├── signal_49.txt │ │ ├── signal_5.txt │ │ ├── signal_50.txt │ │ ├── signal_51.txt │ │ ├── signal_52.txt │ │ ├── signal_53.txt │ │ ├── signal_54.txt │ │ ├── signal_55.txt │ │ ├── signal_56.txt │ │ ├── signal_57.txt │ │ ├── signal_58.txt │ │ ├── signal_59.txt │ │ ├── signal_6.txt │ │ ├── signal_60.txt │ │ ├── signal_61.txt │ │ ├── signal_62.txt │ │ ├── signal_63.txt │ │ ├── signal_64.txt │ │ ├── signal_65.txt │ │ ├── signal_66.txt │ │ ├── signal_67.txt │ │ ├── signal_68.txt │ │ ├── signal_69.txt │ │ ├── signal_7.txt │ │ ├── signal_70.txt │ │ ├── signal_71.txt │ │ ├── signal_72.txt │ │ ├── signal_73.txt │ │ ├── signal_74.txt │ │ ├── signal_75.txt │ │ ├── signal_76.txt │ │ ├── signal_77.txt │ │ ├── signal_78.txt │ │ ├── signal_79.txt │ │ ├── signal_8.txt │ │ ├── signal_80.txt │ │ ├── signal_81.txt │ │ ├── signal_82.txt │ │ ├── signal_83.txt │ │ ├── signal_84.txt │ │ ├── signal_85.txt │ │ ├── signal_86.txt │ │ ├── signal_87.txt │ │ ├── signal_88.txt │ │ ├── signal_89.txt │ │ ├── signal_9.txt │ │ ├── signal_90.txt │ │ ├── signal_91.txt │ │ ├── signal_92.txt │ │ ├── signal_93.txt │ │ ├── signal_94.txt │ │ ├── signal_95.txt │ │ ├── signal_96.txt │ │ ├── signal_97.txt │ │ ├── signal_98.txt │ │ └── signal_99.txt └── onogone │ ├── README.md │ ├── exerciceDS.csv │ ├── onogone_entretien.ipynb │ └── result.jpg ├── mooc └── codecademy-data-science │ ├── README.md │ ├── course-10 Data Analysis with Pandas │ ├── A B Testing for ShoeFly.com.ipynb │ ├── Aggregates in Pandas.ipynb │ ├── Data Analysis with Pandas.ipynb │ ├── Project - Page Visits Funnel.ipynb │ ├── Project - Petal Power Inventory.ipynb │ ├── Working with Multiple DataFrames.ipynb │ ├── ad_clicks.csv │ ├── bakery.csv │ ├── cart.csv │ ├── checkout.csv │ ├── customers.csv │ ├── employees.csv │ ├── ice_cream.csv │ ├── imdb.csv │ ├── inventory.csv │ ├── men_women_sales.csv │ ├── orders.csv │ ├── orders2.csv │ ├── orders_2.csv │ ├── page_visits.csv │ ├── products.csv │ ├── products_2.csv │ ├── purchase.csv │ ├── sales.csv │ ├── shoefly.csv │ ├── targets.csv │ └── visits.csv │ ├── course-4 Go Off-Platform with SQL │ ├── List of SQL commands.md │ └── What is SQLite.md │ └── course-5 Analyze Real Data with SQL │ ├── Calculating Churn Rates - Codeflix.sqlite │ ├── Calculating Churn Rates.md │ ├── Marketing Attribution.md │ ├── Project Assignment - Marketing Attribution.sqlite │ ├── Usage Funnels with Warby Parker.md │ ├── Usage Funnels.md │ ├── User Churn.md │ ├── User Churn.sqlite │ ├── img │ └── funnels.png │ └── pdf │ └── page_visits_schema.pdf ├── playground ├── 2022-06-23-CNN-TF.ipynb ├── AutoEncoder-Time_Series-Pytorch.ipynb ├── HOG_histogram_oriented_gradient_object_detection.ipynb ├── K_Means_image_compression.ipynb ├── LSTM_AutoEncoder_101.ipynb ├── PCA-image-compression.ipynb ├── PCA_understanding_example.ipynb ├── PCA_without_scikit_learn.ipynb ├── README.md ├── SVM-XOR-RBF-kernel-parameters.ipynb ├── SVM-face-recognition.ipynb ├── TensorFlow_2_quickstart_for_beginner.ipynb ├── connect_google_drive_and_install_packages.ipynb ├── data │ ├── labrador.jpg │ ├── labrador_bw.jpg │ └── labrador_compress.jpg ├── notebook_in_html │ ├── K_Means_image_compression.html │ ├── PCA-image-compression.html │ ├── PCA_understanding_example.html │ ├── PCA_without_scikit_learn.html │ ├── SVM-XOR-RBF-kernel-parameters.html │ ├── SVM-face-recognition.html │ └── TensorFlow_2_quickstart_for_beginner.html ├── object-detection-lilian-weng.ipynb └── simple_import_grayscale_and_apply_kernel.ipynb ├── projects ├── README.md └── kaggle-titanic-disaster │ ├── README.md │ ├── gender_submission.csv │ ├── test.csv │ ├── titanic_chrisalbon_Random_Forest.html │ ├── titanic_chrisalbon_Random_Forest.ipynb │ ├── titanic_kaggle.html │ ├── titanic_kaggle.ipynb │ ├── titanic_submission.csv │ └── train.csv └── reading-books └── handson-ml2 └── testing.ipynb /.gitignore: -------------------------------------------------------------------------------- 1 | .ipynb_checkpoints 2 | lib/ 3 | token.mat 4 | submit.m 5 | */lib 6 | */lib/ 7 | test.m 8 | */test.m 9 | ML for Coders by fast.ai/data/Train.csv 10 | ML for Coders by fast.ai/data/Valid.csv 11 | ML for Coders by fast.ai/data/Train.zip 12 | ML for Coders by fast.ai/data/TrainAndValid.zip 13 | ML for Coders by fast.ai/data/TrainAndValid.csv 14 | ML for Coders by fast.ai/data/Valid.zip 15 | 16 | .DS_Store 17 | balloon 18 | output 19 | saved_model 20 | saved_models 21 | __MACOSX/ 22 | playground/detectron2/skaters 23 | playground/data/skaters/ 24 | -------------------------------------------------------------------------------- /.gitmodules: -------------------------------------------------------------------------------- 1 | 2 | [submodule "my-certificates"] 3 | path = my-certificates 4 | url = https://github.com/dinhanhthi/my-certificates 5 | [submodule "projects/cafe-in-hcm"] 6 | path = projects/cafe-in-hcm 7 | url = git@github.com:dinhanhthi/cafe-in-hcm.git 8 | [submodule "mooc/dataquest-aio"] 9 | path = mooc/dataquest-aio 10 | url = git@github.com:dinhanhthi/dataquest-aio.git 11 | [submodule "mooc/coursera-ml-andrew-ng"] 12 | path = mooc/coursera-ml-andrew-ng 13 | url = git@github.com:dinhanhthi/coursera-ml-andrew-ng.git 14 | [submodule "mooc/coursera-ibm-data-professional-certificate"] 15 | path = mooc/coursera-ibm-data-professional-certificate 16 | url = git@github.com:dinhanhthi/coursera-ibm-data-professional-certificate.git 17 | [submodule "mooc/deeplearning.ai-courses"] 18 | path = mooc/deeplearning.ai-courses 19 | url = git@github.com:dinhanhthi/deeplearning.ai-courses.git 20 | [submodule "my-dockerfiles"] 21 | path = my-dockerfiles 22 | url = git@github.com:dinhanhthi/my-dockerfiles.git 23 | [submodule "interview/mountain-vs-beach"] 24 | path = interview/mountain-vs-beach 25 | url = https://github.com/dinhanhthi/mountain-vs-beach.git 26 | [submodule "playground/google-vertex-ai"] 27 | path = playground/google-vertex-ai 28 | url = git@github.com:dinhanhthi/google-vertex-ai.git 29 | [submodule "projects/mountain-vs-beach"] 30 | path = projects/mountain-vs-beach 31 | url = git@github.com:dinhanhthi/mountain-vs-beach.git 32 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 📊 data-science-learning 2 | 3 | Check [this note](https://dinhanhthi.com/note/a-collection-of-learning-resources-for-the-ai-world/). -------------------------------------------------------------------------------- /good-articles.md: -------------------------------------------------------------------------------- 1 | # 📝 Google articles for understanding concepts 2 | 3 | Below are the collection of articles / blog posts / videos which are useful (and intuitive) to understanding concepts / algorithms in Machine Learning and Data Science. In the time of collecting, the URLs are fine but I don't guarantee for their existence after that. 4 | 5 | The articles are grouped by subjects and alphabet orders. 6 | 7 | 8 | ## Deep Learning 9 | 10 | - [Building Autoencoders in Keras](https://blog.keras.io/building-autoencoders-in-keras.html) — Keras blog. 11 | - [Your first Deep Learning project in Python with Keras step by step](https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/) — Jason Brownlee — Machine Learning Mastery. 12 | 13 | 14 | ## Anomaly Detection 15 | 16 | - [Novelty and Outlier Detection](https://scikit-learn.org/stable/modules/outlier_detection.html) — scikit-learn docs. 17 | - [Time Series of Price Anomaly Detection](https://towardsdatascience.com/time-series-of-price-anomaly-detection-13586cd5ff46) — Susan Li — Toward Data Science. -------------------------------------------------------------------------------- /interview/README.md: -------------------------------------------------------------------------------- 1 | # Intervew questions 2 | 3 | This folder contains codes from companies that I've applied for jobs. 4 | -------------------------------------------------------------------------------- /interview/dataswati/test_python_dataswati_2019/wave_data/signal_1.txt: -------------------------------------------------------------------------------- 1 | 4.676030337251419589e+00 2 | 5.579901925947641672e+00 3 | 4.457225811680703309e+00 4 | 3.684350973779676242e+00 5 | 6.059251705171537772e+00 6 | 5.023170101703076540e+00 7 | -3.363137775864433188e-02 8 | 3.585268849573825634e+00 9 | 1.379742574979718972e+00 10 | 3.807967390373257821e+00 11 | 6.029857524120067147e+00 12 | 1.536235572628652513e+00 13 | 4.922928683510484404e+00 14 | 6.020439306218375641e+00 15 | 4.051738236509399016e+00 16 | 8.098788358353301575e+00 17 | 4.761633896311574077e+00 18 | -9.441943146844433699e-02 19 | -1.464214997213687308e+00 20 | 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3.180632838058949474e+00 183 | -1.966539324162151825e+00 184 | 2.318841208728809722e+00 185 | 2.085156883040453213e+00 186 | 1.495585779908776392e+00 187 | -3.442869631384118279e+00 188 | -3.521820740668633487e+00 189 | -2.617877176831103991e+00 190 | -8.068578718934773519e+00 191 | -6.898050302791589594e+00 192 | -7.438475924052694310e+00 193 | -1.176653115501121327e+01 194 | -1.019776489392310914e+01 195 | -1.224239610376556087e+01 196 | -6.185177814856290368e+00 197 | -4.818563520270894074e+00 198 | -1.119462614290473335e+00 199 | -2.741300335630586726e+00 200 | 3.796614595671894854e+00 201 | -------------------------------------------------------------------------------- /interview/onogone/README.md: -------------------------------------------------------------------------------- 1 | The goal is to build a binary classifier based on the attached corpus. Documents are classified as either belonging to the desired class (TRUE) or not (FALSE). 2 | 3 | We would simply like you to have a look at the data and come up with one or several strategies to build such a classifier and maximize its accuracy. 4 | 5 | This may include suggestions on how to pre-process, vectorize or resample the data or how to evaluate the classifier. 6 | 7 | If time permits, code samples (in the language of your choice) in which you implement and evaluate your different strategies would be appreciated. -------------------------------------------------------------------------------- /interview/onogone/exerciceDS.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dinhanhthi/learn-ai/7a8f5beee0a18dc5881e4070f7f218a27442c0ca/interview/onogone/exerciceDS.csv -------------------------------------------------------------------------------- /interview/onogone/result.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dinhanhthi/learn-ai/7a8f5beee0a18dc5881e4070f7f218a27442c0ca/interview/onogone/result.jpg -------------------------------------------------------------------------------- /mooc/codecademy-data-science/README.md: -------------------------------------------------------------------------------- 1 | # ❄ Data Science path on Codecademy 2 | 3 | This folder contains codes & "my-way descriptions" when I follow [Data Science path](https://www.codecademy.com/learn/paths/data-science) on Codecademy. 4 | 5 | **Note**: Unlike my notes for [dataquest](https://github.com/dinhanhthi/dataquest-aio) or [deelearning.ai](https://github.com/dinhanhthi/deeplearning.ai-notes), this folder only contains the important codes & nodes (for me). 6 | 7 | ❗💀❗ For the reason of efficiency, I drop this path. It takes more time (and a little useless) to learn some basic tasks. I change to learn from more advanced courses. 8 | 9 | ## What's in this path? 10 | 11 | - [x] The Importance of Data and SQL Basics 12 | - [x] SQL: Basics 13 | - [x] SQL: Intermediate 14 | - [x] Go Off-Platform with SQL 15 | - [x] Analyze Real Data with SQL 16 | - [x] Python Functions and Logic 17 | - [x] Python Lists and Loops 18 | - [x] Advanced Python 19 | - [x] Python Cumulative Project 20 | - [x] Data Analysis with Pandas 21 | - [x] Data Visualization 22 | - [x] Visualization Cumulative Projects 23 | - [x] Data Visualization Capstone Projects 24 | - [x] Learn Statistics With Python 25 | - [x] Introduction to Statistics with NumPy 26 | - [x] Hypothesis Testing with SciPy 27 | - [x] Practical Data Cleaning 28 | - [x] Data Analysis Capstone Projects 29 | - [x] Learn Web Scraping with Beautiful Soup 30 | - [x] Machine Learning: Supervised Learning 31 | - [x] Supervised Machine Learning Cumulative Project 32 | - [x] Machine Learning: Unsupervised Learning 33 | - [x] Unsupervised Machine Learning Cumulative Project 34 | - [x] Perceptrons and Neural Nets 35 | - [x] Machine Learning Capstone Project 36 | - [x] Natural Language Processing -------------------------------------------------------------------------------- /mooc/codecademy-data-science/course-10 Data Analysis with Pandas/bakery.csv: -------------------------------------------------------------------------------- 1 | item,price 2 | cookie,2.50 3 | brownie,3.50 4 | slice of cake,4.75 5 | slice of cheesecake,4.75 6 | slice of pie,5.00 -------------------------------------------------------------------------------- /mooc/codecademy-data-science/course-10 Data Analysis with Pandas/customers.csv: -------------------------------------------------------------------------------- 1 | customer_id,customer_name,address,phone_number 2 | 1,John Smith,123 Main St.,212-123-4567 3 | 2,Jane Doe,456 Park Ave.,949-867-5309 4 | 3,Joe Schmo,798 Broadway,112-358-1321 5 | -------------------------------------------------------------------------------- /mooc/codecademy-data-science/course-10 Data Analysis with Pandas/employees.csv: -------------------------------------------------------------------------------- 1 | id,name,hourly_wage,hours_worked 2 | 10310,Lauren Durham,19,43 3 | 18656,Grace Sellers,17,40 4 | 61254,Shirley Rasmussen,16,30 5 | 16886,Brian Rojas,18,47 6 | 89010,Samantha Mosley,11,38 7 | 87246,Louis Guzman,14,39 8 | 20578,Denise Mcclure,15,40 9 | 12869,James Raymond,15,32 10 | 53461,Noah Collier,18,35 11 | 14746,Donna Frederick,20,41 12 | 71127,Shirley Beck,14,32 13 | 92522,Christina Kelly,8,44 14 | 22447,Brian Noble,11,39 15 | 61654,Randy Key,16,38 16 | 16988,Diana Stewart,14,48 17 | 68619,Timothy Sosa,14,42 18 | 59949,Betty Skinner,11,48 19 | 81418,Janet Maxwell,12,38 20 | 27267,Madison Johnston,20,37 21 | 19985,Virginia Nichols,13,49 -------------------------------------------------------------------------------- /mooc/codecademy-data-science/course-10 Data Analysis with Pandas/ice_cream.csv: -------------------------------------------------------------------------------- 1 | item,price 2 | scoop of chocolate ice cream,3.00 3 | scoop of vanilla ice cream,2.95 4 | scoop of strawberry ice cream,3.05 5 | scoop of cookie dough ice cream,3.25 -------------------------------------------------------------------------------- /mooc/codecademy-data-science/course-10 Data Analysis with Pandas/inventory.csv: -------------------------------------------------------------------------------- 1 | location,product_type,product_description,quantity,price 2 | Staten Island,seeds,daisy,4,6.99 3 | Staten Island,seeds,calla lily,46,19.99 4 | Staten Island,seeds,tomato,85,13.99 5 | Staten Island,garden tools,rake,4,13.99 6 | Staten Island,garden tools,wheelbarrow,0,89.99 7 | Staten Island,garden tools,spade,93,19.99 8 | Staten Island,pest_control,insect killer,74,12.99 9 | Staten Island,pest_control,weed killer,8,23.99 10 | Staten Island,planter,20 inch terracotta planter,0,17.99 11 | Staten Island,planter,8 inch plastic planter,53,3.99 12 | Brooklyn,seeds,daisy,50,6.99 13 | Brooklyn,seeds,calla lily,0,19.99 14 | Brooklyn,seeds,tomato,0,13.99 15 | Brooklyn,garden tools,rake,15,13.99 16 | Brooklyn,garden tools,wheelbarrow,82,89.99 17 | Brooklyn,garden tools,spade,36,19.99 18 | Brooklyn,pest_control,insect killer,80,12.99 19 | Brooklyn,pest_control,weed killer,76,23.99 20 | Brooklyn,planter,20 inch terracotta planter,5,17.99 21 | Brooklyn,planter,8 inch plastic planter,26,3.99 22 | Queens,seeds,daisy,57,6.99 23 | Queens,seeds,calla lily,95,19.99 24 | Queens,seeds,tomato,45,13.99 25 | Queens,garden tools,rake,21,13.99 26 | Queens,garden tools,wheelbarrow,98,89.99 27 | Queens,garden tools,spade,26,19.99 28 | Queens,pest_control,insect killer,0,12.99 29 | Queens,pest_control,weed killer,16,23.99 30 | Queens,planter,20 inch terracotta planter,87,17.99 -------------------------------------------------------------------------------- /mooc/codecademy-data-science/course-10 Data Analysis with Pandas/men_women_sales.csv: -------------------------------------------------------------------------------- 1 | month,men,women 2 | January,30,35 3 | February,29,35 4 | March,31,29 5 | April,32,28 6 | May,47,50 7 | June,49,45 -------------------------------------------------------------------------------- /mooc/codecademy-data-science/course-10 Data Analysis with Pandas/orders2.csv: -------------------------------------------------------------------------------- 1 | order_id,customer_id,product_id,quantity,timestamp 2 | 1,2,3,1,2017-01-01 3 | 2,2,2,3,2017-01-01 4 | 3,3,1,1,2017-01-01 5 | 4,3,2,2,2017-02-01 6 | 5,3,3,3,2017-02-01 7 | 6,1,4,2,2017-03-01 8 | 7,1,1,1,2017-02-02 9 | 8,1,4,1,2017-02-02 10 | -------------------------------------------------------------------------------- /mooc/codecademy-data-science/course-10 Data Analysis with Pandas/orders_2.csv: -------------------------------------------------------------------------------- 1 | id,product_id,customer_id,quantity,timestamp 2 | 1,3,2,1,2017-01-01 3 | 2,2,2,3,2017-01-01 4 | 3,5,1,1,2017-01-01 5 | 4,2,3,2,2016-02-01 6 | 5,3,3,3,2017-02-01 -------------------------------------------------------------------------------- /mooc/codecademy-data-science/course-10 Data Analysis with Pandas/products.csv: -------------------------------------------------------------------------------- 1 | product_id,description,price 2 | 1,thing-a-ma-jig,5 3 | 2,whatcha-ma-call-it,10 4 | 3,doo-hickey,7 5 | 4,gizmo,3 6 | -------------------------------------------------------------------------------- /mooc/codecademy-data-science/course-10 Data Analysis with Pandas/products_2.csv: -------------------------------------------------------------------------------- 1 | product_id,description,price 2 | 1,thing-a-ma-jig,5 3 | 2,whatcha-ma-call-it,10 4 | 3,doo-hickey,7 5 | 4,gizmo,3 -------------------------------------------------------------------------------- /mooc/codecademy-data-science/course-10 Data Analysis with Pandas/sales.csv: -------------------------------------------------------------------------------- 1 | month,revenue 2 | January,300 3 | February,290 4 | March,310 5 | April,325 6 | May,475 7 | June,495 -------------------------------------------------------------------------------- /mooc/codecademy-data-science/course-10 Data Analysis with Pandas/shoefly.csv: -------------------------------------------------------------------------------- 1 | id,first_name,last_name,email,shoe_type,shoe_material,shoe_color 2 | 54791,Rebecca,Lindsay,RebeccaLindsay57@hotmail.com,clogs,faux-leather,black 3 | 53450,Emily,Joyce,EmilyJoyce25@gmail.com,ballet flats,faux-leather,navy 4 | 91987,Joyce,Waller,Joyce.Waller@gmail.com,sandals,fabric,black 5 | 14437,Justin,Erickson,Justin.Erickson@outlook.com,clogs,faux-leather,red 6 | 79357,Andrew,Banks,AB4318@gmail.com,boots,leather,brown 7 | 52386,Julie,Marsh,JulieMarsh59@gmail.com,sandals,fabric,black 8 | 20487,Thomas,Jensen,TJ5470@gmail.com,clogs,fabric,navy 9 | 76971,Janice,Hicks,Janice.Hicks@gmail.com,clogs,faux-leather,navy 10 | 21586,Gabriel,Porter,GabrielPorter24@gmail.com,clogs,leather,brown 11 | 62083,Frances,Palmer,FrancesPalmer50@gmail.com,wedges,leather,white 12 | 91629,Jessica,Hale,JessicaHale25@gmail.com,clogs,leather,red 13 | 98602,Lawrence,Parker,LawrenceParker44@gmail.com,wedges,fabric,brown 14 | 45832,Susan,Dennis,SusanDennis58@gmail.com,ballet flats,fabric,white 15 | 33862,Diane,Ochoa,DO2680@gmail.com,sandals,fabric,red 16 | 73431,Rebecca,Charles,Rebecca.Charles@gmail.com,boots,faux-leather,white 17 | 93889,Jacqueline,Crane,JC2072@hotmail.com,wedges,fabric,red 18 | 39888,Vincent,Stephenson,VS4753@outlook.com,boots,leather,black 19 | 35961,Roy,Tillman,RoyTillman20@gmail.com,boots,leather,white 20 | 24560,Thomas,Roberson,Thomas.Roberson@gmail.com,wedges,fabric,red 21 | 28559,Angela,Newton,ANewton1977@outlook.com,wedges,fabric,red -------------------------------------------------------------------------------- /mooc/codecademy-data-science/course-10 Data Analysis with Pandas/targets.csv: -------------------------------------------------------------------------------- 1 | month,target 2 | January,310 3 | February,270 4 | March,300 5 | April,350 6 | May,475 7 | June,500 -------------------------------------------------------------------------------- /mooc/codecademy-data-science/course-4 Go Off-Platform with SQL/What is SQLite.md: -------------------------------------------------------------------------------- 1 | # What is SQLite? 2 | 3 | *Learn about the SQLite database engine and how to install it on your computer.* 4 | 5 | In this article we will be exploring the extremely prevalent database engine called [SQLite](https://www.sqlite.org/index.html). We will describe what it does, its main uses, and then explain how to set it up and use it on your own computer. 6 | 7 | ## Overview 8 | 9 | - [Home page](https://www.sqlite.org/index.html) ([download](https://www.sqlite.org/download.html) `sqlite-tools-win-...`) 10 | - SQLite is a database engine 11 | - **Advantage** 12 | - In SQLite, a database is stored in a single file 13 | - Copy database = copy file 14 | - access and manipulate a database without involving a server application 15 | - used worldwide for testing, development 16 | - **Drawback**: 17 | - poor choice when many different users are updating the table at the same time 18 | - require some more work to ensure the security of private data 19 | - SQLite does not offer the same exact functionality as many other database systems 20 | - SQLite does not validate data types 21 | - SQLite will not reject values of the wrong type 22 | 23 | ## How to? 24 | 25 | - Setting up SQLite locally: [Windows](https://youtu.be/dcfh5iQ_-3s), [Mac](https://youtu.be/4MJSZi4qvIE) 26 | - Need to add to `path` (on Windows, press `Start` and search `variable...` and then add to `Environment Variables`) 27 | - On Windows: after adding to `path`, run `sqlite3`. 28 | - exit: `Ctrl` + `C` 29 | - On Linux: `sudo apt-get install sqlite3` 30 | - `sqlite3 newdb.sqlite` to run 31 | - exit: `.exit` and then press `Enter` 32 | -------------------------------------------------------------------------------- /mooc/codecademy-data-science/course-5 Analyze Real Data with SQL/Calculating Churn Rates - Codeflix.sqlite: -------------------------------------------------------------------------------- 1 | -- select * 2 | -- from subscriptions 3 | -- limit 100; 4 | 5 | -- select max(subscription_start), min(subscription_start) 6 | -- from subscriptions; 7 | 8 | -- select max(subscription_end), min(subscription_end) 9 | -- from subscriptions; 10 | 11 | with months as ( 12 | select 13 | '2017-01-01' as first_day, 14 | '2017-01-31' as last_day 15 | union 16 | select 17 | '2017-02-01' as first_day, 18 | '2017-02-28' as last_day 19 | union 20 | select 21 | '2017-03-01' as first_day, 22 | '2017-03-31' as last_day 23 | ), 24 | cross_join as ( 25 | select * 26 | from subscriptions 27 | cross join months 28 | ) 29 | , 30 | status as ( 31 | select 32 | id, 33 | first_day as month, 34 | case 35 | when (subscription_start < first_day) 36 | and ( 37 | subscription_end > first_day 38 | or subscription_end is null 39 | ) 40 | and (segment = 87) 41 | then 1 42 | else 0 43 | end as is_active_87, 44 | case 45 | when (subscription_start < first_day) 46 | and ( 47 | subscription_end > first_day 48 | or subscription_end is null 49 | ) 50 | and (segment = 30) 51 | then 1 52 | else 0 53 | end as is_active_30, 54 | case 55 | when (subscription_end between first_day and last_day) 56 | and (segment = 87) 57 | then 1 58 | else 0 59 | end as is_canceled_87, 60 | case 61 | when (subscription_end between first_day and last_day) 62 | and (segment = 30) 63 | then 1 64 | else 0 65 | end as is_canceled_30 66 | from cross_join 67 | ), 68 | status_aggregate as ( 69 | select 70 | month, 71 | sum(is_active_87) AS sum_active_87, 72 | sum(is_active_30) AS sum_active_30, 73 | sum(is_canceled_87) AS sum_canceled_87, 74 | sum(is_canceled_30) AS sum_canceled_30 75 | from status 76 | group by month 77 | ) 78 | select 79 | month, 80 | 1.0 * sum_canceled_87/sum_active_87 as churn_rate_87, 81 | 1.0 * sum_canceled_30/sum_active_30 as churn_rate_30 82 | from status_aggregate; -------------------------------------------------------------------------------- /mooc/codecademy-data-science/course-5 Analyze Real Data with SQL/Calculating Churn Rates.md: -------------------------------------------------------------------------------- 1 | # Calculating Churn Rates 2 | 3 | Take a look at the first 100 rows 4 | 5 | ``` sql 6 | select * 7 | from subscriptions 8 | limit 100; 9 | ``` 10 | 11 | Determine the range of months of data provided. Which months will you be able to calculate churn for? 12 | 13 | ``` sql 14 | select max(subscription_start), min(subscription_start) 15 | from subscriptions; 16 | ``` 17 | 18 | -------------------------------------------------------------------------------- /mooc/codecademy-data-science/course-5 Analyze Real Data with SQL/Marketing Attribution.md: -------------------------------------------------------------------------------- 1 | # Marketing Attribution 2 | 3 | The ways (sources) customers come to our website is called **channels** / **touchpoints** / **sources**. 4 | 5 | If an ad campaign drives a lot of visits to their site, then they know that source is working! We say that **those visits are attributed** to the ad campaign. 6 | 7 | But how do websites capture that information? The answer is **UTM parameters**. 8 | 9 | You can see a common schema for a "page visits" table at [this link](./pdf/page_visits_schema.pdf). 10 | 11 | ## First / Last Touch 12 | 13 | June's first touch — the **first time** she was exposed to CoolTShirts.com 14 | 15 | June’s **last touch** — the exposure to CoolTShirts.com that led to a purchase (from `facebook`, for example) 16 | 17 | If you want to increase sales at CoolTShirts.com, *would you count on buzzfeed or increase facebook ads?* The real question is: should June’s purchase be attributed to buzzfeed or to facebook? 18 | 19 | - First-touch attribution only considers the first utm_source for each customer, which would be buzzfeed in this case. *This is a good way of knowing how visitors initially discover a website.* 20 | - Last-touch attribution only considers the last utm_source for each customer, which would be facebook in this case. *This is a good way of knowing how visitors are drawn back to a website, especially for making a final purchase.* 21 | 22 | The results can be crucial to improving a company’s marketing and online presence. Most companies analyze both first- and last-touch attribution and display the results separately. 23 | 24 | ## Review 25 | 26 | You can now wield SQL to find where, when, and how users are visiting a website. Well done! Here’s a summary of what you learned: 27 | 28 | - **UTM parameters** are a way of tracking visits to a website. Developers, marketers, and analysts use them to capture information like the time, attribution source, and attribution medium for each user visit. 29 | - **First-touch attribution** only considers the first source for each customer. This is a good way of knowing how visitors initially discover a website. 30 | - **Last-touch attribution** only considers the last source for each customer. This is a good way of knowing how visitors are drawn back to a website, especially for making a final purchase. 31 | - Find first and last touches by grouping `page_visits` by `user_id` and finding the `MIN` and `MAX` of `timestamp`. 32 | - To find firstand last-touch attribution, join that table back with the original `page_visits` table on `user_id` and `timestamp`. -------------------------------------------------------------------------------- /mooc/codecademy-data-science/course-5 Analyze Real Data with SQL/Project Assignment - Marketing Attribution.sqlite: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dinhanhthi/learn-ai/7a8f5beee0a18dc5881e4070f7f218a27442c0ca/mooc/codecademy-data-science/course-5 Analyze Real Data with SQL/Project Assignment - Marketing Attribution.sqlite -------------------------------------------------------------------------------- /mooc/codecademy-data-science/course-5 Analyze Real Data with SQL/Usage Funnels with Warby Parker.md: -------------------------------------------------------------------------------- 1 | # Usage Funnels with Warby Parker 2 | 3 | [Warby Parker](https://www.warbyparker.com/) is a transformative lifestyle brand with a lofty objective: to offer designer eyewear at a revolutionary price while leading the way for socially conscious businesses. Founded in 2010 and named after two characters in an early Jack Kerouac journal, Warby Parker believes in creative thinking, smart design, and doing good in the world. For every pair of eyeglasses and sunglasses sold, a pair is distributed to someone in need. 4 | 5 | In this project, you will analyze different Warby Parker’s marketing funnels in order to calculate conversion rates. Here are the funnels and the tables that you are given: 6 | 7 | Quiz Funnel: `survey` 8 | 9 | Home Try-On Funnel: `quiz`, `home_try_on`, `purchase` 10 | 11 | This project was a collaboration with Warby Parker’s Data Science team (thank you!) and uses fictional data. Let’s get started! 12 | 13 | ## 1 14 | 15 | To help users find their perfect frame, Warby Parker has a Style Quiz that has the following questions: 16 | 17 | 1. “What are you looking for?” 18 | 1. “What’s your fit?” 19 | 1. “Which shapes do you like?” 20 | 1. “Which colors do you like?” 21 | 1. “When was your last eye exam?” 22 | 23 | The users’ responses are stored in a table called survey. 24 | 25 | *Select all columns from the first 10 rows. What columns does the table have?* 26 | 27 | ``` sql 28 | SELECT * 29 | FROM survey 30 | LIMIT 10; 31 | ``` 32 | 33 | ## 2 34 | 35 | Users will “give up” at different points in the survey. Let’s analyze how many users move from Question 1 to Question 2, etc. 36 | 37 | *What is the number of responses for each question?* 38 | 39 | ``` sql 40 | SELECT question, 41 | COUNT(DISTINCT user_id) 42 | FROM survey 43 | GROUP BY 1; 44 | ``` 45 | 46 | ## 4 47 | 48 | Warby Parker’s purchase funnel is: 49 | 50 | Take the Style Quiz → Home Try-On → Purchase the Perfect Pair of Glasses 51 | 52 | During the Home Try-On stage, we will be conducting an A/B Test: 53 | 54 | - 50% of the users will get 3 pairs to try on 55 | - 50% of the users will get 5 pairs to try on 56 | 57 | Let’s find out whether or not users who get more pairs to try on at home will be more likely to make a purchase. 58 | 59 | The data will be distributed across three tables: `quiz`, `home_try_on`, `purchase` 60 | 61 | Examine the first five rows of each table 62 | 63 | *What are the column names?* 64 | 65 | ``` sql 66 | SELECT * 67 | FROM quiz 68 | LIMIT 5; 69 | 70 | SELECT * 71 | FROM home_try_on 72 | LIMIT 5; 73 | 74 | SELECT * 75 | FROM purchase 76 | LIMIT 5; 77 | ``` 78 | 79 | ## 5 80 | 81 | Each row will represent a single user from the browse table: 82 | 83 | - If the user has any entries in home_try_on, then is_home_try_on will be ‘True’. 84 | - number_of_pairs comes from home_try_on table 85 | - If the user has any entries in is_purchase, then is_purchase will be ‘True’. 86 | 87 | Use a LEFT JOIN to combine the three tables, starting with the top of the funnel (quiz) and ending with the bottom of the funnel (purchase). 88 | 89 | Select only the first 10 rows from this table (otherwise, the query will run really slowly). 90 | 91 | ``` sql 92 | SELECT DISTINCT q.user_id, 93 | h.user_id IS NOT NULL AS 'is_home_try_on', 94 | h.number_of_pairs, 95 | p.user_id IS NOT NULL AS 'is_purchase' 96 | FROM quiz q 97 | LEFT JOIN home_try_on h 98 | ON q.user_id = h.user_id 99 | LEFT JOIN purchase p 100 | ON p.user_id = q.user_id 101 | LIMIT 10; 102 | ``` -------------------------------------------------------------------------------- /mooc/codecademy-data-science/course-5 Analyze Real Data with SQL/Usage Funnels.md: -------------------------------------------------------------------------------- 1 | # Usage Funnels 2 | 3 | ## What is a funnel? 4 | 5 | A **funnel** is a marketing model which illustrates the theoretical customer journey towards the purchase of a product or service. **Example**: we want to track how many users complete a series of steps and know which steps have the most number of users giving up. 6 | 7 | ![funnels](img/funnels.png) 8 | 9 | Generally, we want to know the total number of users in each step of the funnel, as well as the percent of users who complete each step. 10 | 11 | ## Build a Funnel from a single table 12 | 13 | On step `Survey`, not every user finished the survey! We want to build a funnel to analyze if certain questions prompted users to stop working on the survey. 14 | 15 | *What is the number of responses for each question?* 16 | 17 | ```sql 18 | SELECT question_text, 19 | COUNT(DISTINCT user_id) 20 | FROM survey_responses 21 | GROUP BY 1; 22 | ``` 23 | 24 | ## Compare Funnels For A/B Tests 25 | 26 | There is a popup to welcome new users. The company try using an A/B test where 27 | 28 | - 50% of users view the original `control` version of the pop-ups. 29 | - 50% of uses view the new `variant` version of the pop-ups. 30 | 31 | *How is the funnel different between the two groups?* 32 | 33 | ``` sql 34 | SELECT modal_text, 35 | COUNT(DISTINCT CASE 36 | WHEN ab_group = 'control' THEN user_id 37 | END) AS 'control_clicks', 38 | COUNT(DISTINCT CASE 39 | WHEN ab_group = 'variant' THEN user_id 40 | END) AS 'variant_clicks' 41 | FROM onboarding_modals 42 | GROUP BY 1 43 | ORDER BY 1; 44 | ``` 45 | 46 | 47 | 48 | -------------------------------------------------------------------------------- /mooc/codecademy-data-science/course-5 Analyze Real Data with SQL/User Churn.md: -------------------------------------------------------------------------------- 1 | # User Churn 2 | 3 | ## What is Churn 4 | 5 | A common revenue model for SaaS (Software as a service) companies is to charge a monthly subscription fee for access to their product. Frequently, these companies *aim to continually increase the number of users paying for their product*. One metric that is helpful for this goal is churn rate. 6 | 7 | **Churn rate** is the percent of subscribers that have canceled within a certain period, usually a month. For a user base to grow, the *churn rate must be less than the new subscriber rate for the same period*. 8 | 9 | For example, suppose you were analyzing data for a monthly video streaming service called CodeFlix. At the beginning of February, CodeFlix has 1,000 customers. In February, 250 of these customers cancel. The churn rate for February would be: 10 | 11 | 250 / 1000 = 25% churn rate 12 | 13 | ``` sql 14 | select 250. / 1000; 15 | ``` 16 | 17 | ## Single Month I 18 | 19 | - For the numerator, we only want the portion of the customers who canceled during December. 20 | - For the denominator, we only want to be considering customers who were active at the beginning of December: 21 | 22 | ``` sql 23 | SELECT 1.0 * 24 | ( 25 | SELECT COUNT(*) 26 | FROM subscriptions 27 | WHERE subscription_start < '2016-12-01' 28 | AND ( 29 | subscription_end 30 | BETWEEN '2016-12-01' 31 | AND '2016-12-31' 32 | ) 33 | ) / ( 34 | SELECT COUNT(*) 35 | FROM subscriptions 36 | WHERE subscription_start < '2016-12-01' 37 | AND ( 38 | (subscription_end >= '2016-12-01') 39 | OR (subscription_end IS NULL) 40 | ) 41 | ) 42 | AS result; 43 | ``` 44 | 45 | ## Single Month II 46 | 47 | The previous method worked, but you may have noticed we selected the same group of customers twice for the same month and repeated a number of conditional statements. 48 | 49 | Companies typically look at churn data over a period of many months. We need to modify the calculation a bit to make it easier to mold into a multi-month result. This is done by making use of `WITH` and `CASE`. 50 | 51 | ``` sql 52 | WITH enrollments AS 53 | (SELECT * 54 | FROM subscriptions 55 | WHERE subscription_start < '2017-01-01' 56 | AND ( 57 | (subscription_end >= '2017-01-01') 58 | OR (subscription_end IS NULL) 59 | )), 60 | status AS 61 | (SELECT 62 | CASE 63 | WHEN (subscription_end > '2017-01-31') 64 | OR (subscription_end IS NULL) THEN 0 65 | ELSE 1 66 | END as is_canceled, 67 | CASE 68 | WHEN (subscription_start < '2017-01-01') 69 | AND ( 70 | (subscription_end >= '2017-01-01') 71 | OR (subscription_end IS NULL) 72 | ) THEN 1 73 | ELSE 0 74 | END as is_active 75 | FROM enrollments 76 | ) 77 | SELECT 1.0 * SUM(is_canceled)/SUM(is_active) FROM status; 78 | ``` 79 | 80 | ## Multiple Month: Create Months Temporary Table 81 | 82 | We need a table for January, February, and March of 2017. 83 | 84 | ``` sql 85 | WITH months AS ( 86 | SELECT 87 | '2017-01-01' AS first_day, 88 | '2017-01-31' AS last_day 89 | UNION 90 | SELECT 91 | '2017-02-01' AS first_day, 92 | '2017-02-28' AS last_day 93 | UNION 94 | SELECT 95 | '2017-03-01' AS first_day, 96 | '2017-03-31' AS last_day 97 | ), 98 | cross_join AS ( 99 | SELECT * 100 | FROM subscriptions 101 | CROSS JOIN months 102 | ), 103 | status AS ( 104 | SELECT 105 | id, 106 | first_day AS month, 107 | CASE 108 | WHEN (subscription_start < first_day) 109 | AND ( 110 | subscription_end > first_day 111 | OR subscription_end IS NULL 112 | ) THEN 1 113 | ELSE 0 114 | END AS is_active, 115 | CASE 116 | WHEN subscription_end BETWEEN first_day AND last_day THEN 1 117 | ELSE 0 118 | END AS is_canceled 119 | FROM cross_join 120 | ), 121 | status_aggregate AS ( 122 | SELECT 123 | month, 124 | SUM(is_active) AS active, 125 | SUM(is_canceled) AS canceled 126 | FROM status 127 | GROUP BY month 128 | ) 129 | SELECT 130 | month, 131 | 1.0 * canceled / active AS churn_rate 132 | FROM status_aggregate; 133 | ``` 134 | 135 | ## Conclusion 136 | 137 | In this lesson you learned: 138 | 139 | - The churn rate is a percent of subscribers at the beginning of a period that cancel within that period. “Monthly churn” is a typical metric and what was used in the examples. 140 | - How to calculate this metric using SQL for a single month. This used COUNT() and conditions to determine the number of subscribers that were active and how many canceled. 141 | - A more complex method to track the subscriber churn rate over many months. -------------------------------------------------------------------------------- /mooc/codecademy-data-science/course-5 Analyze Real Data with SQL/User Churn.sqlite: -------------------------------------------------------------------------------- 1 | WITH months AS ( 2 | SELECT 3 | '2017-01-01' AS first_day, 4 | '2017-01-31' AS last_day 5 | UNION 6 | SELECT 7 | '2017-02-01' AS first_day, 8 | '2017-02-28' AS last_day 9 | UNION 10 | SELECT 11 | '2017-03-01' AS first_day, 12 | '2017-03-31' AS last_day 13 | ), 14 | cross_join AS ( 15 | SELECT * 16 | FROM subscriptions 17 | CROSS JOIN months 18 | ), 19 | status AS ( 20 | SELECT 21 | id, 22 | first_day AS month, 23 | CASE 24 | WHEN (subscription_start < first_day) 25 | AND ( 26 | subscription_end > first_day 27 | OR subscription_end IS NULL 28 | ) THEN 1 29 | ELSE 0 30 | END AS is_active, 31 | CASE 32 | WHEN subscription_end BETWEEN first_day AND last_day THEN 1 33 | ELSE 0 34 | END AS is_canceled 35 | FROM cross_join 36 | ), 37 | status_aggregate AS ( 38 | SELECT 39 | month, 40 | SUM(is_active) AS active, 41 | SUM(is_canceled) AS canceled 42 | FROM status 43 | GROUP BY month 44 | ) 45 | SELECT 46 | month, 47 | 1.0 * canceled / active AS churn_rate 48 | FROM status_aggregate; -------------------------------------------------------------------------------- /mooc/codecademy-data-science/course-5 Analyze Real Data with SQL/img/funnels.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dinhanhthi/learn-ai/7a8f5beee0a18dc5881e4070f7f218a27442c0ca/mooc/codecademy-data-science/course-5 Analyze Real Data with SQL/img/funnels.png -------------------------------------------------------------------------------- /mooc/codecademy-data-science/course-5 Analyze Real Data with SQL/pdf/page_visits_schema.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dinhanhthi/learn-ai/7a8f5beee0a18dc5881e4070f7f218a27442c0ca/mooc/codecademy-data-science/course-5 Analyze Real Data with SQL/pdf/page_visits_schema.pdf -------------------------------------------------------------------------------- /playground/LSTM_AutoEncoder_101.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# LSTM Autoencoder 101\n", 8 | "\n", 9 | "[**Anh-Thi DINH**](https://dinhanhthi.com) -- *Update: 1/1/2020.*\n", 10 | "\n", 11 | "***Aim:*** (There are some points which are not LSTM)\n", 12 | "\n", 13 | "- [x] Why PyTorch? (not Keras and TensorFlow)\n", 14 | "- [ ] Understand the basic idea.\n", 15 | "- [ ] Understand with examples.\n", 16 | "- [ ] Saving the references for later checking.\n", 17 | "- [ ] Apply in using with Pytorch." 18 | ] 19 | }, 20 | { 21 | "cell_type": "markdown", 22 | "metadata": {}, 23 | "source": [ 24 | "## References\n", 25 | "\n", 26 | "### Understand ideas\n", 27 | "\n", 28 | "1. [A Gentle Introduction to LSTM Autoencoders](https://machinelearningmastery.com/lstm-autoencoders/) - *machinelearningmastery*. → **Keras**.\n", 29 | "2. [Encoder-Decoder Long Short-Term Memory Networks](https://machinelearningmastery.com/encoder-decoder-long-short-term-memory-networks/) - *machinelearningmastery*. → **Keras**." 30 | ] 31 | }, 32 | { 33 | "cell_type": "markdown", 34 | "metadata": {}, 35 | "source": [ 36 | "## Why PyTorch?\n", 37 | "\n", 38 | "- Short training duration.\n", 39 | "- It can live on its own (like TF, but Keras).\n", 40 | "- Speed fast (like TF, more than Keras).\n", 41 | "- For large dataset.\n", 42 | "- Take full advantage of GPU.\n", 43 | "- Flexibility.\n", 44 | "- Debugging capabilities." 45 | ] 46 | }, 47 | { 48 | "cell_type": "markdown", 49 | "metadata": {}, 50 | "source": [ 51 | "## Learning by doing" 52 | ] 53 | }, 54 | { 55 | "cell_type": "code", 56 | "execution_count": 1, 57 | "metadata": {}, 58 | "outputs": [], 59 | "source": [ 60 | "use_colab = 0 # 1 if you use Google Colab to read/run this notebook\n", 61 | "\n", 62 | "if use_colab:\n", 63 | " folder_url = 'https://raw.githubusercontent.com/dinhanhthi/data-science-learning/master/mini-projects/data/'\n", 64 | " data_url = folder_url + './'\n", 65 | "else:\n", 66 | " dataset_url = './data/' # if you use localhost" 67 | ] 68 | }, 69 | { 70 | "cell_type": "code", 71 | "execution_count": 2, 72 | "metadata": {}, 73 | "outputs": [], 74 | "source": [ 75 | "import pandas as pd # import pandas package\n", 76 | "import numpy as np\n", 77 | "\n", 78 | "# show the plots inside the notebook\n", 79 | "%matplotlib inline " 80 | ] 81 | }, 82 | { 83 | "cell_type": "code", 84 | "execution_count": 3, 85 | "metadata": {}, 86 | "outputs": [], 87 | "source": [] 88 | } 89 | ], 90 | "metadata": { 91 | "kernelspec": { 92 | "display_name": "Python 3", 93 | "language": "python", 94 | "name": "python3" 95 | }, 96 | "language_info": { 97 | "codemirror_mode": { 98 | "name": "ipython", 99 | "version": 3 100 | }, 101 | "file_extension": ".py", 102 | "mimetype": "text/x-python", 103 | "name": "python", 104 | "nbconvert_exporter": "python", 105 | "pygments_lexer": "ipython3", 106 | "version": "3.7.4" 107 | } 108 | }, 109 | "nbformat": 4, 110 | "nbformat_minor": 2 111 | } 112 | -------------------------------------------------------------------------------- /playground/README.md: -------------------------------------------------------------------------------- 1 | # Project based learning 2 | 3 | This folder contains some **Machine Learning** / **Data Science** projects for understanding concepts while I learn ML/AI/DS. The bigger ones are [here](../projects). 4 | 5 | If you wanna know what we can learn from these mini projects, you can read [this full report](https://dinhanhthi.com/small-projects-to-understand-concepts). 6 | 7 | - My personal website: [dinhanhthi.com](https://dinhanhthi.com) 8 | - My note: [dinhanhthi.com/notes](http://dinhanhthi.com/notes). 9 | - Contact: [dinhanhthi@gmail.com](mailto:dinhanhthi@gmail.com) 10 | 11 | 12 | -------------------------------------------------------------------------------- /playground/data/labrador.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dinhanhthi/learn-ai/7a8f5beee0a18dc5881e4070f7f218a27442c0ca/playground/data/labrador.jpg -------------------------------------------------------------------------------- /playground/data/labrador_bw.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dinhanhthi/learn-ai/7a8f5beee0a18dc5881e4070f7f218a27442c0ca/playground/data/labrador_bw.jpg -------------------------------------------------------------------------------- /playground/data/labrador_compress.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dinhanhthi/learn-ai/7a8f5beee0a18dc5881e4070f7f218a27442c0ca/playground/data/labrador_compress.jpg -------------------------------------------------------------------------------- /playground/object-detection-lilian-weng.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import numpy as np\n", 10 | "import scipy.signal as sig\n", 11 | "data = np.array([[0, 105, 0], [40, 255, 90], [0, 55, 0]])\n", 12 | "G_x = sig.convolve2d(data, np.array([[-1, 0, 1]]), mode='valid')\n", 13 | "G_y = sig.convolve2d(data, np.array([[-1], [0], [1]]), mode='valid')" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 7, 19 | "metadata": {}, 20 | "outputs": [ 21 | { 22 | "data": { 23 | "text/plain": [ 24 | "array([[ 0],\n", 25 | " [-50],\n", 26 | " [ 0]])" 27 | ] 28 | }, 29 | "execution_count": 7, 30 | "metadata": {}, 31 | "output_type": "execute_result" 32 | } 33 | ], 34 | "source": [ 35 | "G_x" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 8, 41 | "metadata": {}, 42 | "outputs": [ 43 | { 44 | "data": { 45 | "text/plain": [ 46 | "array([[ 0, 50, 0]])" 47 | ] 48 | }, 49 | "execution_count": 8, 50 | "metadata": {}, 51 | "output_type": "execute_result" 52 | } 53 | ], 54 | "source": [ 55 | "G_y" 56 | ] 57 | }, 58 | { 59 | "cell_type": "code", 60 | "execution_count": null, 61 | "metadata": {}, 62 | "outputs": [], 63 | "source": [] 64 | } 65 | ], 66 | "metadata": { 67 | "kernelspec": { 68 | "display_name": "ds", 69 | "language": "python", 70 | "name": "python3" 71 | }, 72 | "language_info": { 73 | "codemirror_mode": { 74 | "name": "ipython", 75 | "version": 3 76 | }, 77 | "file_extension": ".py", 78 | "mimetype": "text/x-python", 79 | "name": "python", 80 | "nbconvert_exporter": "python", 81 | "pygments_lexer": "ipython3", 82 | "version": "3.11.9" 83 | } 84 | }, 85 | "nbformat": 4, 86 | "nbformat_minor": 2 87 | } 88 | -------------------------------------------------------------------------------- /projects/README.md: -------------------------------------------------------------------------------- 1 | # Projects 2 | 3 | This folder contains some "bigger" projects while I learn ML/AI/DS (the smaller ones for understanding concepts are [here](../mini-projects)). 4 | 5 | - My personal website: [dinhanhthi.com](https://dinhanhthi.com) 6 | - My "raw" note: [rawnote.dinhanhthi.com](http://rawnote.dinhanhthi.com). 7 | - My note: [dinhanhthi.com/notes](http://dinhanhthi.com/notes). 8 | - Contact: [dinhanhthi@gmail.com](mailto:dinhanhthi@gmail.com) 9 | 10 | 11 | -------------------------------------------------------------------------------- /projects/kaggle-titanic-disaster/README.md: -------------------------------------------------------------------------------- 1 | # 🚢 Titanic Disaster 2 | 3 | - [Kaggle's URL](https://www.kaggle.com/c/titanic). 4 | - [My Jupyter Notebook](./titanic_kaggle.ipynb). 5 | 6 | ## The challenge 7 | 8 | The sinking of the Titanic is one of the most infamous shipwrecks in history. 9 | 10 | On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew. 11 | 12 | While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others. 13 | 14 | In this challenge, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc). 15 | 16 | ## What Data Will I Use in This Competition? 17 | 18 | In this competition, you'll gain access to two similar datasets that include passenger information like name, age, gender, socio-economic class, etc. One dataset is titled `train.csv` and the other is titled `test.csv`. 19 | 20 | `Train.csv` will contain the details of a subset of the passengers on board (891 to be exact) and importantly, will reveal whether they survived or not, also known as the "ground truth". 21 | 22 | The `test.csv` dataset contains similar information but does not disclose the "ground truth" for each passenger. It's your job to predict these outcomes. 23 | 24 | Using the patterns you find in the `train.csv` data, predict whether the other 418 passengers on board (found in `test.csv`) survived. 25 | 26 | Check out the "Data" tab to explore the datasets even further. Once you feel you’ve created a competitive model, submit it to Kaggle to see where your model stands on our leaderboard against other Kagglers. 27 | 28 | ## Data's description 29 | 30 | ### Overview 31 | 32 | The data has been split into two groups: 33 | 34 | - training set (`train.csv`) 35 | - test set (`test.csv`) 36 | 37 | The training set should be used to build your machine learning models. For the training set, we provide the outcome (also known as the "ground truth") for each passenger. Your model will be based on "features" like passengers’ gender and class. You can also use feature engineering to create new features. 38 | 39 | The test set should be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each passenger. It is your job to predict these outcomes. For each passenger in the test set, use the model you trained to predict whether or not they survived the sinking of the Titanic. 40 | 41 | We also include `gender_submission.csv`, a set of predictions that assume all and only female passengers survive, as an example of what a submission file should look like. 42 | 43 | ### Data Dictionary 44 | 45 | |Variable|Definition|Key| 46 | |--- |--- |--- | 47 | |survival|Survival|0 = No, 1 = Yes| 48 | |pclass|Ticket class|1 = 1st, 2 = 2nd, 3 = 3rd| 49 | |sex|Sex|| 50 | |Age|Age in years|| 51 | |sibsp|# of siblings / spouses aboard the Titanic|| 52 | |parch|# of parents / children aboard the Titanic|| 53 | |ticket|Ticket number|| 54 | |fare|Passenger fare|| 55 | |cabin|Cabin number|| 56 | |embarked|Port of Embarkation|C = Cherbourg, Q = Queenstown, S = Southampton| 57 | 58 | ### Variable Notes 59 | 60 | - `pclass`: A proxy for socio-economic status (SES) 61 | - `1st` = Upper 62 | - `2nd` = Middle 63 | - `3rd` = Lower 64 | - `age`: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5 65 | - `sibsp`: The dataset defines family relations in this way... 66 | - `Sibling` = brother, sister, stepbrother, stepsister 67 | - `Spouse` = husband, wife (mistresses and fiancés were ignored) 68 | - `parch`: The dataset defines family relations in this way... 69 | - `Parent` = mother, father 70 | - `Child` = daughter, son, stepdaughter, stepson 71 | - Some children travelled only with a nanny, therefore `parch=0` for them. 72 | 73 | 74 | 75 | 76 | 77 | 78 | -------------------------------------------------------------------------------- /projects/kaggle-titanic-disaster/gender_submission.csv: -------------------------------------------------------------------------------- 1 | PassengerId,Survived 2 | 892,0 3 | 893,1 4 | 894,0 5 | 895,0 6 | 896,1 7 | 897,0 8 | 898,1 9 | 899,0 10 | 900,1 11 | 901,0 12 | 902,0 13 | 903,0 14 | 904,1 15 | 905,0 16 | 906,1 17 | 907,1 18 | 908,0 19 | 909,0 20 | 910,1 21 | 911,1 22 | 912,0 23 | 913,0 24 | 914,1 25 | 915,0 26 | 916,1 27 | 917,0 28 | 918,1 29 | 919,0 30 | 920,0 31 | 921,0 32 | 922,0 33 | 923,0 34 | 924,1 35 | 925,1 36 | 926,0 37 | 927,0 38 | 928,1 39 | 929,1 40 | 930,0 41 | 931,0 42 | 932,0 43 | 933,0 44 | 934,0 45 | 935,1 46 | 936,1 47 | 937,0 48 | 938,0 49 | 939,0 50 | 940,1 51 | 941,1 52 | 942,0 53 | 943,0 54 | 944,1 55 | 945,1 56 | 946,0 57 | 947,0 58 | 948,0 59 | 949,0 60 | 950,0 61 | 951,1 62 | 952,0 63 | 953,0 64 | 954,0 65 | 955,1 66 | 956,0 67 | 957,1 68 | 958,1 69 | 959,0 70 | 960,0 71 | 961,1 72 | 962,1 73 | 963,0 74 | 964,1 75 | 965,0 76 | 966,1 77 | 967,0 78 | 968,0 79 | 969,1 80 | 970,0 81 | 971,1 82 | 972,0 83 | 973,0 84 | 974,0 85 | 975,0 86 | 976,0 87 | 977,0 88 | 978,1 89 | 979,1 90 | 980,1 91 | 981,0 92 | 982,1 93 | 983,0 94 | 984,1 95 | 985,0 96 | 986,0 97 | 987,0 98 | 988,1 99 | 989,0 100 | 990,1 101 | 991,0 102 | 992,1 103 | 993,0 104 | 994,0 105 | 995,0 106 | 996,1 107 | 997,0 108 | 998,0 109 | 999,0 110 | 1000,0 111 | 1001,0 112 | 1002,0 113 | 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-------------------------------------------------------------------------------- /projects/kaggle-titanic-disaster/titanic_submission.csv: -------------------------------------------------------------------------------- 1 | PassengerId,Survived 2 | 892,0 3 | 893,0 4 | 894,0 5 | 895,0 6 | 896,1 7 | 897,0 8 | 898,1 9 | 899,0 10 | 900,1 11 | 901,0 12 | 902,0 13 | 903,0 14 | 904,1 15 | 905,0 16 | 906,1 17 | 907,1 18 | 908,0 19 | 909,0 20 | 910,1 21 | 911,0 22 | 912,0 23 | 913,0 24 | 914,1 25 | 915,0 26 | 916,1 27 | 917,0 28 | 918,1 29 | 919,0 30 | 920,0 31 | 921,0 32 | 922,0 33 | 923,0 34 | 924,1 35 | 925,1 36 | 926,0 37 | 927,0 38 | 928,1 39 | 929,1 40 | 930,0 41 | 931,0 42 | 932,0 43 | 933,0 44 | 934,0 45 | 935,1 46 | 936,1 47 | 937,0 48 | 938,0 49 | 939,0 50 | 940,1 51 | 941,0 52 | 942,0 53 | 943,0 54 | 944,1 55 | 945,1 56 | 946,0 57 | 947,0 58 | 948,0 59 | 949,0 60 | 950,0 61 | 951,1 62 | 952,0 63 | 953,0 64 | 954,0 65 | 955,1 66 | 956,0 67 | 957,1 68 | 958,1 69 | 959,0 70 | 960,1 71 | 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1274,1 385 | 1275,1 386 | 1276,0 387 | 1277,1 388 | 1278,0 389 | 1279,0 390 | 1280,0 391 | 1281,0 392 | 1282,1 393 | 1283,1 394 | 1284,0 395 | 1285,0 396 | 1286,0 397 | 1287,1 398 | 1288,0 399 | 1289,1 400 | 1290,0 401 | 1291,0 402 | 1292,1 403 | 1293,0 404 | 1294,1 405 | 1295,0 406 | 1296,0 407 | 1297,0 408 | 1298,0 409 | 1299,0 410 | 1300,1 411 | 1301,1 412 | 1302,1 413 | 1303,1 414 | 1304,1 415 | 1305,0 416 | 1306,1 417 | 1307,0 418 | 1308,0 419 | 1309,0 420 | -------------------------------------------------------------------------------- /reading-books/handson-ml2/testing.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stdout", 10 | "output_type": "stream", 11 | "text": [ 12 | "Python 3.8.2\r\n" 13 | ] 14 | } 15 | ], 16 | "source": [ 17 | "!python --version" 18 | ] 19 | }, 20 | { 21 | "cell_type": "code", 22 | "execution_count": 2, 23 | "metadata": {}, 24 | "outputs": [], 25 | "source": [ 26 | "import torch\n", 27 | "import torch.nn as nn\n", 28 | "import torch.nn.functional as F\n", 29 | "import torch.optim as optim" 30 | ] 31 | }, 32 | { 33 | "cell_type": "code", 34 | "execution_count": 3, 35 | "metadata": {}, 36 | "outputs": [ 37 | { 38 | "name": "stdout", 39 | "output_type": "stream", 40 | "text": [ 41 | "cuda is available? True\n", 42 | "device_count: 1\n", 43 | "current device: 0\n", 44 | "device name: GeForce GTX 1650\n" 45 | ] 46 | } 47 | ], 48 | "source": [ 49 | "print('cuda is available? ', torch.cuda.is_available())\n", 50 | "print('device_count: ', torch.cuda.device_count())\n", 51 | "print('current device: ', torch.cuda.current_device())\n", 52 | "print('device name: ', torch.cuda.get_device_name(0))" 53 | ] 54 | }, 55 | { 56 | "cell_type": "code", 57 | "execution_count": 7, 58 | "metadata": {}, 59 | "outputs": [ 60 | { 61 | "name": "stdout", 62 | "output_type": "stream", 63 | "text": [ 64 | "Num GPUs Available: 0\n" 65 | ] 66 | } 67 | ], 68 | "source": [ 69 | "import tensorflow as tf\n", 70 | "print(\"Num GPUs Available: \", len(tf.config.experimental.list_physical_devices('GPU')))" 71 | ] 72 | }, 73 | { 74 | "cell_type": "code", 75 | "execution_count": null, 76 | "metadata": {}, 77 | "outputs": [], 78 | "source": [] 79 | } 80 | ], 81 | "metadata": { 82 | "kernelspec": { 83 | "display_name": "Python 3", 84 | "language": "python", 85 | "name": "python3" 86 | }, 87 | "language_info": { 88 | "codemirror_mode": { 89 | "name": "ipython", 90 | "version": 3 91 | }, 92 | "file_extension": ".py", 93 | "mimetype": "text/x-python", 94 | "name": "python", 95 | "nbconvert_exporter": "python", 96 | "pygments_lexer": "ipython3", 97 | "version": "3.8.2" 98 | } 99 | }, 100 | "nbformat": 4, 101 | "nbformat_minor": 4 102 | } 103 | --------------------------------------------------------------------------------