ranand19/machine-learning

Essential machine learning algorithms, concepts, examples and visualizations. Popular machine learning algorithms from scratch. Applications of machine learning.

Machine Learning

Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. Machine learning can also be unsupervised and be used to learn and establish baseline behavioral profiles for various entities and then used to find meaningful anomalies.

Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to "produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data.

Table of contents

• Regression
  • Tesla Stock Price Forecast
  • Linear Regression from Scratch
• Classification
  • K-Nearest Neighbors (k-NN)
    • Breast Cancer Wisconsin (k-NN)
    • K-Nearest Neighbors from Scratch
  • Support Vector Machine (SVM)
    • Breast Cancer Wisconsin (SVM)
    • Support Vector Machine from Scratch
• Clustering
  • Flat Clustering
    • K-Means Example
    • Titanic (k-Means)
    • K-Means from Scratch
  • Hierarchical Clustering
    • Mean Shift Example
    • Titanic (Mean Shift)
    • Mean Shift from Scratch

Machine Learning Algorithms

NumPy

Pandas

Matplotlib