{"id":15009416,"url":"https://github.com/prateekkumarsingh/python","last_synced_at":"2026-01-22T20:09:06.545Z","repository":{"id":45575503,"uuid":"103277459","full_name":"PrateekKumarSingh/Python","owner":"PrateekKumarSingh","description":"Day-wise Python Learning resources from basic concepts to advanced Python applications such as data science and Machine learning. 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Create, delete and nested with lists\n* Using Builtin functions\n    1. Format(), int(), float(), round(), floor(), ceil()\n\n### Day 5\n* OS module\n    1. Current working directory, new, remove directory and renaming files\n* Sys Module\n    1. Passing cmdline arguments\n    2. Stderr, stdout \n    3. System-specific parameters and functions\n\n### Day 6\n* Basic URLLIB module usecases\n    1. Requesting html response from a web url\n    2. Encoding the url parameters    \n* Sending web requests using URLLIB module with custom headers\n* Dowloading JSON data from a URL\n\n### Day 7\n* Regular expressions\n    1. Identifiers \\d \\D \\w \\W etc\n    2. Modifiers + $ ^ etc\n    3. Functions .findall() , .search() _\n\n### Day 8\n* List comprehensions and usecases\n    1. Example of regular and list comprehension approach\n    2. UseCase-1 : performing operations on each item in the list\n    3. UseCase-2 : filtering elements of a list, eg - Null, empty strings, negative numbers etc\n    4. UseCase-3 : list flattening - convert a 2D list to 1D list\n* String manipulations\n    1. Slicing a string\n    2. .split() and .join()\n    3. reversed()\n    4. .strip() , .lstrip() , .rstrip()\n    5. .rjust() .ljust(), .center()\n    6. UseCase - Printing data in tabular format using .center()\n\n### Day 9 \n* MINI PROJECTS\n    1. Dice Roll Simulator\n    2. Guess the Number\n    3. Hangman - Word guessing game\n\n### Day 10\n* Parsing websites\n    1. Extracting data from withing the HTML tags of websites using reglar expression and web request\n\n* TKinter module to make windows forms\n    1. Basic form with labels and buttons\n    2. Button onclick event handling\n    3. Change label text dynamically\n\n* MINI PROJECT\n\n    4. Calclator GUI (Using Tkinter module)\n\n### Day 11\n* Tkinter module to create MENU in windows forms\n* Add drop down menu items under each menu\n* Add functionalities to drop down menu items\n    1. File \u003e Save [Opens a File Dialog box to save the file]\n    2. File \u003e Exit\n    3. Tools \u003e Show Image\n    4. Tools \u003e Show Text\n* Threading Module\n    1. Creating a thread\n    2. Thread lock() , acquire() , release()  \n    3. Queue\n    \n### Day 12\n* CX Freeze module\n    1. Define setup files\n    2. Build executables (.exe) from Python scripts\n* MatPlotLib module\n    1. Loading coordinates from a csv file\n    2. Plotting graph\n    3. Scatter graph\n    4. Bar graph\n    5. Defining title, label, grid and legends\n    6. Styling graphs\n\n### Day 13\n* Socket programming\n    1. socket module\n    2. socket.AF_INET (Address Family = IPv4)\n    3. socket.SOCK_STREAM (Protocol = TCP) | socket.SOCK_DGRAM (Protocol = UDP) \n* Multi-threaded port scanner using socket programming\n* Listen\\Bind ports\n* Client\\Server system using socket programming\n\n### Day 14\n* Mini Project\n\n    5. Chat System using Socket Programming\n        * Telnet.exe clients can connect to a chat room on port 5555 of the server and start chat with other users\n        * Multi-threaded client/server chat system\n        * Broadcast [1-to-all] adnd private [1-to-1] messages\n        * Chat room admin can Kick user(s) out of chat room\n        * Poke users in a chat room\n        * Ability to leave the chat room\n\n### Day 15 \n* Pandas module\n    1. Convert dictionaries to Dataframes\n    2. Slicing dataframes\n    3. Making new columsn in dataframes\n* SKLearn and Quandl module\n    1. Get financial and economic datasets using Quandl\n    2. Performing mathematical operations on dataframe columns\n    3. Dataframe functions - .head() .tail() .shift() .fillna() dropna()\n### Day 16    \n* Train, test, predict data using Linear regression or Simple vector machine model\n    1. Features vs labels\n    2. Training and predicting using a model\n        1. Prepare training data and split in 2 parts, ~80% to train ~20% to test [ model_selection.train_test_split() ]\n        2. Define a classifier/model, like LinearRegression, SVM (Simple vector Machine) and then Train the classifier using .fit()\n        3. Test accuracy of the classifier with respect to test data from step 1 [~20% of data]\n        4. Predict -  Label = classifier.predict('Features')\n            ![](https://github.com/PrateekKumarSingh/Python/blob/master/Python%20Machine%20Learning/SampleFiles/StockPrediction.png)\n* Best fit line and how regression works\n    1. What is slope(m) and intercept(b)\n    2. Linear Regression = mX + b\n\n### Day 17\n* What are Squared error?\n* Squared error vs Absolute errors\n* R-Squared / Coeffcient of determination\n* Classification with K-Nearest neighbor (KNN)\n\n### Day 18\n* Euclidean distance\n\n    ![](https://github.com/PrateekKumarSingh/Python/blob/master/Python%20Machine%20Learning/SampleFiles/Euclidean_Distance.jpg)\n* Making your own k-NN (k-Nearest Neighbor) algorithm in python\n* Comparing the accuracy and confidence of your algorithm with SKLearn module's neighbors.KNeighborsClassifier()\n* Accuracy vs confidence in k-NN algorithm\n\n### Day 19\n* SKLearn Support Vector Machine (SVM) classifier\n* Making your own Support Vector Machine (SVM) algorithm in python [Courtesy: ![Harrison](https://pythonprogramming.net) ] \n\n### Day 20    \n* Browser Automation using Selenium web driver with Python\n* Python Web Scraping\n    1. Using URLLib module and Regular expressions\n    2. Using Beautiful Soup module   \n\n### Day 21\n* Soft Marging Support vector machines, kernels and CVXOPT\n* SKLearn KMeans() classifier and clustering data sets\n\n### Day 22\n* Applying SKLearn KMeans classifier on Titanic data set to see if it can classify survivors and deads accurately\n* Making your own custom K_Means() classifier algorithm in python\n* Applying custom K_Means() algorithm on Titanic data set\n\n## Folder/Files listing\n```\n.Root\n|   README.md\n|   \n+---.vscode\n|       launch.json\n|       tasks.json\n|       \n+---Python Basics\n|   |   01_Print_Function.py\n|   |   02_Comment.py\n|   |   03_Math.py\n|   |   04_Variables.py\n|   |   05_While_Loop.py\n|   |   06_For_Loop.py\n|   |   07_If_Else.py\n|   |   08_Function.py\n|   |   09_Global_Local_Variable.py\n|   |   10_Install_Modules.py\n|   |   11_Import_modules.py\n|   |   12_Write_Append_Read_File.py\n|   |   13_Class.py\n|   |   14_User_Input.py\n|   |   15_Statistics_Module.py\n|   |   16_Tuples_List.py\n|   |   17_Using_WebBrowser.py\n|   |   18_MultiDimensional_List.py\n|   |   19_Reading_CSV.py\n|   |   20_Try_Except.py\n|   |   21_Multiline_print.py\n|   |   22_Dictionaries.py\n|   |   23_Builtin_Functions.py\n|   |   24_OS_Module.py\n|   |   25_SYS_Module.py\n|   |   26_URLLIB_Module_Basic.py\n|   |   27_URLLIB_Module_Custom_Headers.py\n|   |   28_URLLIB_Module_with_JSON.py\n|   |   29_Regular_Expressions.py\n|   |   30_List_Comprehensions.py\n|   |   31_String_Manipulations.py\n|   |   32_Parsing_Websites.py\n|   |   33_TKINTER_Module.py\n|   |   34_TKINTER_Add_Menu.py\n|   |   35_Threading_Module.py\n|   |   36_Threading_Advanced.py\n|   |   37_CX_Freeze_and_Making_Exes.py\n|   |   38_MatPlotLib_Module.py\n|   |   39_Sockets_Programming.py\n|   |   40_Multithreaded_Port_Scanner.py\n|   |   41_Listen_And_Bind_Ports.py\n|   |   42_Client_Server_Systems_With_Sockets.py\n|   |   debug.log\n|   |   \n|   +---MiniProjects\n|   |       1_Dice_Roll_Simulator.py\n|   |       2_Guess_The_Number.py\n|   |       3_Hangman.py\n|   |       4_Calculator_GUI.py\n|   |       5_Chat_System_On_Socket_Programming.py\n|   |       readme.md\n|   |       \n|   +---Resources\n|   |       Python_3_Tips.jpg\n|   |       \n|   \\---SampleFiles\n|           coordinates1.csv\n|           coordinates2.csv\n|           example.csv\n|           GetHREF.py\n|           picture.jpg\n|           RequestWithHeader.txt\n|           \n+---Python Machine Learning\n|   |   01_Pandas_Module.py\n|   |   02_Sklearn_and_Quandl_module.py\n|   |   03_Regression_Train_Test_Predict.py\n|   |   04_Best_Fit_Line_and_Regression.py\n|   |   05_Classification_with_SKLEARN_K_Nearest_Neighbor_Algorithm.py\n|   |   06_KNN_Algorithm_using_Python.py\n|   |   07_Test_Accuracy_of_kNN_Classifier_on_Cancer_Data.py\n|   |   08_Classification_with_SKLEARN_Support_Vector_Machine_Algorithm.py\n|   |   09_Creating_a_SVM_from_scratch.py\n|   |   10_Soft_Margin_SVM_and_Kernels_with_CVXOPT.py\n|   |   11_Clustering_DataSets_with_KMeans_Algorithm.py\n|   |   12_KMeans_on_Titanic_DataSet.py\n|   |   13_Creating_KMeans_from_scratch.py\n|   |   14_Custom_KMeans_Algorithm_on_Titanic_dataset.py\n|   |   \n|   +---MiniProjects\n|   |       01_Twitter.py\n|   |       \n|   +---Resources\n|   |       Basic_Algebra.pdf\n|   |       Python_For_DataScience.jpg_large\n|   |       R_and_Python_DataScience.jpg\n|   |       \n|   \\---SampleFiles\n|           breast-cancer-wisconsin.txt\n|           Euclidean_Distance.jpg\n|           Intro to Regression.pdf\n|           linearregression.pickle\n|           StockPrediction.png\n|           titanic.xls\n|           \n+---Python Selenium\n|       01_Selenium_With_Python.py\n|       \n+---Python Web Scraping\n|       01_Using_URLLIB_and_REGEX.py\n|       02_Using_Beautiful_Soup.py\n\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprateekkumarsingh%2Fpython","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fprateekkumarsingh%2Fpython","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprateekkumarsingh%2Fpython/lists"}