Machine_Learning_Algorithms_from_Scratch and Machine-Learning-from-Scratch
About Machine_Learning_Algorithms_from_Scratch
milaan9/Machine_Learning_Algorithms_from_Scratch
This repository explores the variety of techniques and algorithms commonly used in machine learning and the implementation in MATLAB and PYTHON.
This project helps machine learning practitioners understand the inner workings of various machine learning algorithms. It provides practical implementations in MATLAB and Python, allowing users to see how common techniques like Decision Trees, Naive Bayes, and K-Means Clustering are built from the ground up. The output is a deeper conceptual understanding and runnable code examples.
About Machine-Learning-from-Scratch
curiousily/Machine-Learning-from-Scratch
Succinct Machine Learning algorithm implementations from scratch in Python, solving real-world problems (Notebooks and Book). Examples of Logistic Regression, Linear Regression, Decision Trees, K-means clustering, Sentiment Analysis, Recommender Systems, Neural Networks and Reinforcement Learning.
This project helps aspiring machine learning practitioners understand how core algorithms work by showing their complete implementation in Python. It takes raw data and demonstrates how to build models for tasks like predicting outcomes, grouping similar items, or recommending products. This is ideal for students, data scientists, or engineers who want to grasp the inner workings of machine learning.
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