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100-Days-of-Code-Data-Science

Starting a 100 Days Code Challenge for Learning Data Science from Scratch is my goal on Learning Data Science in Machine Learning by:

  • Learning Fundamentals of Python
  • Python Libraries for Data Science
  • Data Manipulation and Preprocessing
  • Machine Learning Basics
  • Advanced Machine Learning Techniques
  • Deep Learning and Neural Networks
  • Model Evaluation and Deployment
  • Data Science Project and Wrap-Up

Machine learing


πŸ“… Calendar Progress

πŸ“… FEB 2025

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πŸ—‚οΈ Repository Structure

πŸ“˜ Python Fundamentals

Section Description
Basic_Python Covers fundamental syntax, control structures, functions, and core Python concepts
OOPS Object-Oriented Programming principles with practical implementations

πŸ“‚ Data Structures

Structure Coverage
Array Includes arrays, lists, strings, tuples, sets, and dictionaries with operations.

πŸ“Š Python Libraries for Data Science

Section Coverage
NumPay Numerical computing and array operations
Pandas Data manipulation and analysis
Matplotlib Data visualization and plotting
Seaborn Statistical data visualization
Sk Learn Machine learning algorithms and models

πŸ“– Data Manipulation and Preprocessing

Section Coverage
Data Manipulation feature scaling, encoding categorical data, data normalization preprocessing steps to improve data quality and model performance.

πŸ› οΈ Machine Learning Basics

Topics Use Case
Linear Algebra Vectors, Matrices
Statistics & Probability Mean, Variance, Probability Distributions
Calculus Derivatives, Gradients
Optimization Techniques Gradient Descent

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