Machine-Learning and Machine-Learning-from-Scratch
About Machine-Learning
Gautam-J/Machine-Learning
Implementation of different ML Algorithms from scratch, written in Python 3.x
This project helps students and aspiring data scientists understand how foundational machine learning algorithms work at a detailed level. It takes raw datasets and runs algorithms like Linear Regression, Logistic Regression, K-Nearest Neighbors, and K-Means, outputting step-by-step visualizations of their training process. It's designed for someone learning the mathematical and intuitive basis of machine learning.
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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