machine-learning

Contributor: EkinIs

Introduction to Data Analysis & Machine Learning with Python

Welcome to this repository! Here you will find a collection of Jupyter notebooks designed to introduce you to the core Python libraries used in data analysis, visualization, and machine learning. This resource aims to build a foundational understanding that will help you progress smoothly into more advanced topics.

What You’ll Learn

1. NumPy:

   • Understanding arrays, vectorization, and fundamental numerical computations.
   • Performing basic mathematical operations and array manipulations efficiently.

2. Pandas:

   • Importing, cleaning, and exploring datasets.
   • Using DataFrames to handle tabular data, perform group-by operations, and handle missing data.
   • Performing data wrangling and basic exploratory data analysis (EDA).

3. Matplotlib:

   • Creating line plots, bar charts, histograms, scatter plots, and more.
   • Customizing plots with titles, labels, legends, and annotations.
   • Building clear, publication-quality visualizations.

4. scikit-learn:

   • Understanding the basics of machine learning models, both supervised and unsupervised.
   • Exploring essential tools like train-test splits, model evaluation, and hyperparameter tuning.
   • Training simple models such as linear regression and decision trees.