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Machine Learning Repository

Welcome to the Machine Learning Repository! This repository covers various aspects of data analytics and machine learning, providing a comprehensive resource for learners and practitioners.

1. Maths for Data Analytics

  • Statistics
  • Probability Distribution
  • Inferential Statistics

2. Data Toolkit/ Libraries (Numpy/Matplotlib)

Link to Notes for Command Line Interface: https://ritikaz.notion.site/Command-Line-e45c0d5c166f4b998b08ee9a43267052?pvs=4

  • Working with Files in Python
  • Inventory Management with Files
  • Inventory Management with JSON
  • Mastering Numpy Arrays
  • Automate OS with Python (Creating subdirectories/Creating Bulk Folders/ Read Text file in Bulk)
  • Libraries for Data Analysis (NumPy/Pandas)
  • Libraries for Data Visualization (Matplotlib)

3. Data Analytics

  • Getting started with Pandas
  • Data Preprocessing with Google Playstore
  • Introduction to EDA
  • Data Cleaning
  • Data Visualization
  • Data Analysis Intro
  • Black Friday Sales Data Analysis
  • Data Visualization on Heart Disease
  • GDP Analysis

4. Web Scraping

Projects :

  • BookScraper | Website : ToScrape
  • Scraping Quotes
  • Wikipedia Scraper (Search a Person and Scrape it's wikipedia)
  • Stock Image Scrapper (Downloading images from a website)
  • Stock Image Infinite Scroll Scraper (Scrapping website with infinte scroll enables)
  • Youtube Scrapper with Selenium (Scrape the video details for a youtube channel and analyse the csv file created)
  • Google Image Scraper and Download (Search for an image tag and download its images in bulk from google)

5. Streamlit

  • Getting Started with Streamlit : Learn how to write headings, text inputs and special inputs create forms, sliders and integrate scripts in streamlit
  • Data Visualization : Learn visualization with Matplotlib, seaborn and Plotly in streamlit
  • Page Beautification : How to work with columns, expanders and empty functionalities, echo and stop and switching tabs
  • Working with Data : How to create dataframe, upload files, convert image into various forms(jpg,jpeg,png) or rotate an image

6. SQL

Link to Notes: https://ritikaxg.notion.site/SQL-4577a3fb064d4134a8b44bd787aecad0?pvs=4

  • Basics of SQL : Writing DDL Queries, Relationship between Tables , Joins in SQL , Set Theory and Subqueries, Window functions, Working with Date and Time , Working with JSON, Time Series and Analysis, Data Preprocessing using SQL
  • Importing and Analysing datasets using SQL
  • Advance SQL : Views and Triggers, Dynamic SQL

7. Machine Learning

Link to Notes: https://ritikaxg.notion.site/Machine-Learning-9c95836f0558447dab2d739ae697245f?pvs=4

  • Linear Regression : How to implement a Linear Regression model from Scratch and using Sklearn Library
  • Multiple Linear Regression : Implementing MLR using Sklearn, Assumptions in Linear Regression, Ordinary Least Square (OLR) Method
  • Polynomial Regression
  • Support Vector Machine
  • Decision Tree and Random Forest Regressor
  • Learn various Classifictaion Algorithms with titanic dataset
  • KMeans Clustering Algorithm : Implementation and Elbow Method
  • Feature Engineering :
    1. Feature SElection with Correlation Matrix, Extra Tree Classifier and SelectKBest Method
    2. K-Fold Cross Validation
    3. PCA
    4. TSNE
  • Working with Datasets and Projects:
    1. EDA with Titanic Survival Dataset
    2. Intro to working with images in python with MNIST Dataset
    3. PUBG Game Prediction (Feature Engineering and Catboost Model)
    4. Human Activity Recognition with smartphone data (Learn Hyper-Parameter Tuning and Cross Validation with different classifictaion techniques)
    5. Solar Prediction : Learn Feature Selection with Correlation matrix, selectkbest, extratree classifier, feature engineering and transformers visualization also using multi layer perceptron to predict the model output.

Explore the folders and tutorials to enhance your knowledge in data analytics and machine learning. Happy learning!