100daysProgrammingChallenge_LearningPath

A learning path dedicated for the ones who want to start their journey of 100 day programming challenge.One needs to dedicate not more than 2 hours of the day on this. One hour on learning and one hour on implementing what is learned.

The path is to take you from a Toddler in Python to a Swordsman in Machine Learning ;)

Points to ponder

• Follow these things even if you know them do not skip any. By following and practicing the ones you already know will help you in making a momentum for the challenge.

• Resources are provided in the respective folders.

Overview

How to install Python Tutorial

Week 1

->Variables 

->Data Types 

->Conditional Statements 

Task 1 : Menu driven Switch case based Calculator

Week 2

->Loops 

->Pattern Printing 

->File Handling 

->Turtle 

Task 2 : Funky design using turtle

Week 3

->Functions 

->Lambda Function 

->map(), filter() , zip() and reduce() 

Task 3 : To implement the concepts learned in this week

Week 4

->OOPS concepts 

->Classes and Objects 

->Inheritance and its types 

Task 4 : To implement the concepts learned in this week

Week 5

->Exception Handling 

->Date and Time module 

->Time module 

Task 5 : A stop watch

Week 6

->Tkinter 

->google_trans 

->youtube_dl 

 ->Algovis

Task 6: Youtube Video Downloader

Week 7

->Speechrecog 

->Sending Mail (SMTP) 

->Text to speech 

->->OCR using pytesseract 

Task 7 : Writing content into a text file using speech recognition.

Week 8

->Sqllite3 

->Selenium  

->Beautiful soup

Task 8 : Scrapping websites via Selenium/Beautiful Soup and Speech Recognition.

Week 9

->Numpy 
    
->Lists vs Numpy array
    
->Pandas
    

Task 9 : Contrasting complexities between List and Numpy array & Converting a data frame to csv file.

Week 10

->Matplotlib 
    
->Seaborn 
    
->Basics of Tableau
    

Task 10 : Visualizing of Apple stock price via Nasdaq

Week 11

->Introduction to Machine Learning , types of learning 
    
->Data Preprocessing
    
->Linear Algebra
    

Task 11 : Start off with Titanic data set (data preprocessing)

Week 12

Regression 

    ->Linear
    
    ->Multiple 
    
    ->Polynomial
    
    ->Logistic
    

Task 12 : Kaggle the data sets out and start smashing them!

Week 13

Regression Continued

    ->Support Vector Regressor
    
    ->Decision Tree Regressor
    
    ->Random Forest Regressor
    
    ->XG Boost Regressor
    
    ->AdaBoost Regressor
    

Task 13 : Fish the datasets and compare the algorithms in terms of accuracy.

Week 14 (Last 9 Days)

Classification

    ->KNN
    
    ->Support Vector Classifier
    
    ->Naive Bayes
    
    ->Decision Tree Classifier
    
    ->Random Forest Classifier
    
    ->XG Boost Classifier
    
    ->AdaBoost Classifier
    
Computer Vision using Open CV

Extras (if you complete all these things before the deadline)

->Clustering

->PCA and LDA

->ANN , CNN , RNN