https://github.com/prateekkumarsingh/python
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.
https://github.com/prateekkumarsingh/python
python python-3-6 python-learning-journey
Last synced: 9 months ago
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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.
- Host: GitHub
- URL: https://github.com/prateekkumarsingh/python
- Owner: PrateekKumarSingh
- Created: 2017-09-12T13:55:56.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2025-01-26T07:31:38.000Z (12 months ago)
- Last Synced: 2025-03-23T19:23:08.717Z (10 months ago)
- Topics: python, python-3-6, python-learning-journey
- Language: Jupyter Notebook
- Homepage:
- Size: 11.5 MB
- Stars: 196
- Watchers: 18
- Forks: 109
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## Python
My [day-wise] Python Learning journey
## Resources
Python 3 Learning
* [Video] [Basics concepts](https://www.youtube.com/playlist?list=PLQVvvaa0QuDe8XSftW-RAxdo6OmaeL85M)
* [Video] [Machine Learning](https://www.youtube.com/playlist?list=PLQVvvaa0QuDfKTOs3Keq_kaG2P55YRn5v)
* [Link] [Selenium:Browser Automation](http://selenium-python.readthedocs.io/)
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
## Daily Log
### Day 1
* Print function
* Comments
* Math module and mathematical operations
* Loop - For, While
* if, else, elif
### Day 2
* Functions
* Global and Local Variables
* Install Modules
### Day 3
* 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
### Day 4
* Multiline print
* Dictionaries
1. Create, delete and nested with lists
* Using Builtin functions
1. Format(), int(), float(), round(), floor(), ceil()
### Day 5
* 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
### Day 6
* 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
### Day 7
* Regular expressions
1. Identifiers \d \D \w \W etc
2. Modifiers + $ ^ etc
3. Functions .findall() , .search() _
### Day 8
* 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()
### Day 9
* MINI PROJECTS
1. Dice Roll Simulator
2. Guess the Number
3. Hangman - Word guessing game
### Day 10
* 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)
### Day 11
* 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
### Day 12
* 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
### Day 13
* 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
### Day 14
* 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
### Day 15
* 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()
### Day 16
* 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
### Day 17
* What are Squared error?
* Squared error vs Absolute errors
* R-Squared / Coeffcient of determination
* Classification with K-Nearest neighbor (KNN)
### Day 18
* 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
### Day 19
* SKLearn Support Vector Machine (SVM) classifier
* Making your own Support Vector Machine (SVM) algorithm in python [Courtesy:  ]
### Day 20
* Browser Automation using Selenium web driver with Python
* Python Web Scraping
1. Using URLLib module and Regular expressions
2. Using Beautiful Soup module
### Day 21
* Soft Marging Support vector machines, kernels and CVXOPT
* SKLearn KMeans() classifier and clustering data sets
### Day 22
* 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
## Folder/Files listing
```
.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
```