Day-wise Python Learning resources from basic concepts to advanced Python applications such as data science and Machine learning. It also includes cheat-sheets, references which are logged daily to accelerate your learning.

My [day-wise] Python Learning journey

Python 3 Learning

• [Video] Basics concepts
• [Video] Machine Learning
• [Link] Selenium:Browser Automation

Python language reference

• https://docs.python.org/3/reference/

Python cheat sheets

• https://github.com/PrateekKumarSingh/CheatSheets
• https://becominghuman.ai/cheat-sheets-for-ai-neural-networks-machine-learning-deep-learning-big-data-678c51b4b463

Python quick reference cards

• http://www.cs.put.poznan.pl/csobaniec/software/python/py-qrc.html

• Print function
• Comments
• Math module and mathematical operations
• Loop - For, While
• if, else, elif

• Functions
• Global and Local Variables
• Install Modules

• Importing modules
• Read, write, append files
• Class
• Getting User Input
• Statistics Module
  1. • Mean, Median, Standard deviation, Variance
• Tuples and Lists
• Launching WebBrowser
• Multi-Dimensional List
• Reading CSV files
• Try and Except

• Multiline print
• Dictionaries
  1. Create, delete and nested with lists
• Using Builtin functions
  1. Format(), int(), float(), round(), floor(), ceil()

• OS module
  1. Current working directory, new, remove directory and renaming files
• Sys Module
  1. Passing cmdline arguments
  2. Stderr, stdout
  3. System-specific parameters and functions

• Basic URLLIB module usecases
  1. Requesting html response from a web url
  2. Encoding the url parameters
• Sending web requests using URLLIB module with custom headers
• Dowloading JSON data from a URL

• Regular expressions
  1. Identifiers \d \D \w \W etc
  2. Modifiers + $ ^ etc
  3. Functions .findall() , .search() _

• List comprehensions and usecases
  1. Example of regular and list comprehension approach
  2. UseCase-1 : performing operations on each item in the list
  3. UseCase-2 : filtering elements of a list, eg - Null, empty strings, negative numbers etc
  4. UseCase-3 : list flattening - convert a 2D list to 1D list
• String manipulations
  1. Slicing a string
  2. .split() and .join()
  3. reversed()
  4. .strip() , .lstrip() , .rstrip()
  5. .rjust() .ljust(), .center()
  6. UseCase - Printing data in tabular format using .center()

• MINI PROJECTS
  1. Dice Roll Simulator
  2. Guess the Number
  3. Hangman - Word guessing game

• Parsing websites

  1. Extracting data from withing the HTML tags of websites using reglar expression and web request

• TKinter module to make windows forms

  1. Basic form with labels and buttons
  2. Button onclick event handling
  3. Change label text dynamically

• MINI PROJECT

  4. Calclator GUI (Using Tkinter module)

• Tkinter module to create MENU in windows forms
• Add drop down menu items under each menu
• Add functionalities to drop down menu items
  1. File > Save [Opens a File Dialog box to save the file]
  2. File > Exit
  3. Tools > Show Image
  4. Tools > Show Text
• Threading Module
  1. Creating a thread
  2. Thread lock() , acquire() , release()
  3. Queue

• CX Freeze module
  1. Define setup files
  2. Build executables (.exe) from Python scripts
• MatPlotLib module
  1. Loading coordinates from a csv file
  2. Plotting graph
  3. Scatter graph
  4. Bar graph
  5. Defining title, label, grid and legends
  6. Styling graphs

• Socket programming
  1. socket module
  2. socket.AF_INET (Address Family = IPv4)
  3. socket.SOCK_STREAM (Protocol = TCP) | socket.SOCK_DGRAM (Protocol = UDP)
• Multi-threaded port scanner using socket programming
• Listen\Bind ports
• Client\Server system using socket programming

• Mini Project

  5. Chat System using Socket Programming
     • Telnet.exe clients can connect to a chat room on port 5555 of the server and start chat with other users
     • Multi-threaded client/server chat system
     • Broadcast [1-to-all] adnd private [1-to-1] messages
     • Chat room admin can Kick user(s) out of chat room
     • Poke users in a chat room
     • Ability to leave the chat room

• Pandas module
  1. Convert dictionaries to Dataframes
  2. Slicing dataframes
  3. Making new columsn in dataframes
• SKLearn and Quandl module
  1. Get financial and economic datasets using Quandl
  2. Performing mathematical operations on dataframe columns
  3. Dataframe functions - .head() .tail() .shift() .fillna() dropna()

• Train, test, predict data using Linear regression or Simple vector machine model
  1. Features vs labels
  2. Training and predicting using a model
     1. Prepare training data and split in 2 parts, ~80% to train ~20% to test [ model_selection.train_test_split() ]
     2. Define a classifier/model, like LinearRegression, SVM (Simple vector Machine) and then Train the classifier using .fit()
     3. Test accuracy of the classifier with respect to test data from step 1 [~20% of data]
     4. Predict - Label = classifier.predict('Features')
• Best fit line and how regression works
  1. What is slope(m) and intercept(b)
  2. Linear Regression = mX + b

• What are Squared error?
• Squared error vs Absolute errors
• R-Squared / Coeffcient of determination
• Classification with K-Nearest neighbor (KNN)

• Euclidean distance

  

• Making your own k-NN (k-Nearest Neighbor) algorithm in python

• Comparing the accuracy and confidence of your algorithm with SKLearn module's neighbors.KNeighborsClassifier()

• Accuracy vs confidence in k-NN algorithm

• SKLearn Support Vector Machine (SVM) classifier
• Making your own Support Vector Machine (SVM) algorithm in python [Courtesy: ]

• Browser Automation using Selenium web driver with Python
• Python Web Scraping
  1. Using URLLib module and Regular expressions
  2. Using Beautiful Soup module

• Soft Marging Support vector machines, kernels and CVXOPT
• SKLearn KMeans() classifier and clustering data sets

• Applying SKLearn KMeans classifier on Titanic data set to see if it can classify survivors and deads accurately
• Making your own custom K_Means() classifier algorithm in python
• Applying custom K_Means() algorithm on Titanic data set

.Root
|   README.md
|   
+---.vscode
|       launch.json
|       tasks.json
|       
+---Python Basics
|   |   01_Print_Function.py
|   |   02_Comment.py
|   |   03_Math.py
|   |   04_Variables.py
|   |   05_While_Loop.py
|   |   06_For_Loop.py
|   |   07_If_Else.py
|   |   08_Function.py
|   |   09_Global_Local_Variable.py
|   |   10_Install_Modules.py
|   |   11_Import_modules.py
|   |   12_Write_Append_Read_File.py
|   |   13_Class.py
|   |   14_User_Input.py
|   |   15_Statistics_Module.py
|   |   16_Tuples_List.py
|   |   17_Using_WebBrowser.py
|   |   18_MultiDimensional_List.py
|   |   19_Reading_CSV.py
|   |   20_Try_Except.py
|   |   21_Multiline_print.py
|   |   22_Dictionaries.py
|   |   23_Builtin_Functions.py
|   |   24_OS_Module.py
|   |   25_SYS_Module.py
|   |   26_URLLIB_Module_Basic.py
|   |   27_URLLIB_Module_Custom_Headers.py
|   |   28_URLLIB_Module_with_JSON.py
|   |   29_Regular_Expressions.py
|   |   30_List_Comprehensions.py
|   |   31_String_Manipulations.py
|   |   32_Parsing_Websites.py
|   |   33_TKINTER_Module.py
|   |   34_TKINTER_Add_Menu.py
|   |   35_Threading_Module.py
|   |   36_Threading_Advanced.py
|   |   37_CX_Freeze_and_Making_Exes.py
|   |   38_MatPlotLib_Module.py
|   |   39_Sockets_Programming.py
|   |   40_Multithreaded_Port_Scanner.py
|   |   41_Listen_And_Bind_Ports.py
|   |   42_Client_Server_Systems_With_Sockets.py
|   |   debug.log
|   |   
|   +---MiniProjects
|   |       1_Dice_Roll_Simulator.py
|   |       2_Guess_The_Number.py
|   |       3_Hangman.py
|   |       4_Calculator_GUI.py
|   |       5_Chat_System_On_Socket_Programming.py
|   |       readme.md
|   |       
|   +---Resources
|   |       Python_3_Tips.jpg
|   |       
|   \---SampleFiles
|           coordinates1.csv
|           coordinates2.csv
|           example.csv
|           GetHREF.py
|           picture.jpg
|           RequestWithHeader.txt
|           
+---Python Machine Learning
|   |   01_Pandas_Module.py
|   |   02_Sklearn_and_Quandl_module.py
|   |   03_Regression_Train_Test_Predict.py
|   |   04_Best_Fit_Line_and_Regression.py
|   |   05_Classification_with_SKLEARN_K_Nearest_Neighbor_Algorithm.py
|   |   06_KNN_Algorithm_using_Python.py
|   |   07_Test_Accuracy_of_kNN_Classifier_on_Cancer_Data.py
|   |   08_Classification_with_SKLEARN_Support_Vector_Machine_Algorithm.py
|   |   09_Creating_a_SVM_from_scratch.py
|   |   10_Soft_Margin_SVM_and_Kernels_with_CVXOPT.py
|   |   11_Clustering_DataSets_with_KMeans_Algorithm.py
|   |   12_KMeans_on_Titanic_DataSet.py
|   |   13_Creating_KMeans_from_scratch.py
|   |   14_Custom_KMeans_Algorithm_on_Titanic_dataset.py
|   |   
|   +---MiniProjects
|   |       01_Twitter.py
|   |       
|   +---Resources
|   |       Basic_Algebra.pdf
|   |       Python_For_DataScience.jpg_large
|   |       R_and_Python_DataScience.jpg
|   |       
|   \---SampleFiles
|           breast-cancer-wisconsin.txt
|           Euclidean_Distance.jpg
|           Intro to Regression.pdf
|           linearregression.pickle
|           StockPrediction.png
|           titanic.xls
|           
+---Python Selenium
|       01_Selenium_With_Python.py
|       
+---Python Web Scraping
|       01_Using_URLLIB_and_REGEX.py
|       02_Using_Beautiful_Soup.py