100 Days of Machine Learning

Welcome to the 100 Days of Deep Learning Repository Managed by Muhammad Sheraz.This repository has been meticulously crafted to serve as an advanced and comprehensive guide for mastering the Machine Learning.This repository is a collection of code, resources, and notes for the "100 Days of Machine Learning" playlist from CampusX.I highly recommend checking out their playlist (https://youtube.com/playlist?list=PLKnIA16_Rmvbr7zKYQuBfsVkjoLcJgxHH&si=zLgPYRpotDPvsvhs) for in-depth learning on various machine learning topics.

Daily Topics

• Day 1:

  • What is Machine Learning?

• Day 2:

  • AI Vs ML Vs DL

• Day 3:

  • Types of Machine Learning for Beginners
  • Types of Machine Learning In Depth

• Day 4:

  • Batch Machine Learning
  • Offline Vs Online Learning

• Day 5:

  • Online Machine Learning
  • Online Learning
  • Online Vs Offline Machine Learning

• Day 6:

  • Instance-Based Vs Model-Based Learning

• Day 7:

  • Challenges in Machine Learning
  • Problems in Machine Learning

• Day 8:

  • Application of Machine Learning
  • Real Life Machine Learning Applications

• Day 9:

  • Machine Learning Development Life Cycle
  • MLDLC in Data Science

• Day 10:

  • Data Engineer Vs Data Analyst Vs Data Scientist Vs ML Engineer
  • Data Science Job Roles

• Day 11:

  • What are Tensors
  • Tensor In-depth Explanation

• Day 12:

  • Installing Anaconda For Data Science
  • Jupyter Notebook for Machine Learning
  • Google Colab for ML

• Day 13:

  • End to End Toy Project

• Day 14:

  • How to Frame a Machine Learning Problem
  • How to plan a Data Science Project Effectively

• Day 15:

  • Working with CSV files

• Day 16:

  • Working with JSON/SQL

• Day 17:

  • Fetching Data From an API

• Day 18:

  • Fetching data using Web Scraping

• Day 19:

  • Understanding Your Data

• Day 20:

  • Exploratory Data Analysis
  • EDA using Univariate Analysis

• Day 21:

  • Exploratory Data Analysis
    • Bivariate Analysis
    • Multivariate Analysis

• Day 22:

  • Pandas Profiling

• Day 23:

  • What is Feature Engineering?

• Day 24:

  • Feature Scaling
    • Standardization

• Day 25:

  • Feature Scaling
    • Normalization

• Day 26:

  • Encoding Categorical Data
    • Ordinal Encoding
    • Label Encoding

• Day 27:

  • Encoding Categorical Data
    • One Hot Encoding

• Day 28:

  • Column Transformer in Machine Learning
  • How to use ColumnTransformer in Sklearn

• Day 29:

  • Machine Learning Pipelines A-Z

• Day 30:

  • Function Transformer
    • Log Transform
    • Reciprocal Transform
    • Square Root Transform

• Day 31: -Power Transformer

  • Box - Cox Transform
  • Yeo - Johnson Transform

• Day 32:

  • Binning and Binarization
  • Discretization
  • Quantile Binning
  • KMeans Binning

• Day 33:

  • Feature Engineering
  • Handling Mixed Variables

• Day 34:

  • Handling Date and Time Variables

• Day 35:

  • Handling Missing Data
    • Complete Case Analysis

• Day 36:

  • Handling Missing Data
    • Numerical Data
    • Simple Imputer
    • Complete Case Analysis

• Day 37:

  • Handling Missing Categorical Data
    • Simple Imputer
    • Most Frequent Imputation
    • Missing Category Imp

• Day 38:

  • Missing Indicator
  • Random Sample Imputation
  • Handling Missing Data Part 4

• Day 39:

  • KNN Imputer
  • Multivariate Imputation
  • Handling Missing Data Part 5

• Day 40:

  • Multivariate Imputation by Chained Equations for Missing Value
  • MICE Algorithm
  • Iterative Imputer

Overview

The "100 Days of Machine Learning" series is a comprehensive guide that covers a wide range of machine learning concepts, frameworks, and applications. The original content in this repository is derived from CampusX's fantastic work in providing educational content on machine learning.

My Contributions

In addition to the original content, I have added some extra materials, examples, and explanations to enhance the learning experience. These additions aim to provide further clarification, practical insights, and additional resources to complement the existing curriculum.

Acknowledgments

CampusX: Thank you for creating the "100 Days of Machine Learning" playlist and providing valuable educational content.

Muhammad Sheraz