hrshtv/ml-from-scratch

Some Machine Learning algorithms implemented by me, mostly from scratch

Machine Learning Algorithms From Scratch

This is was mostly a 'practice' repository, containing some ML algorithms which I have implemented from scratch. I no longer update or maintain this.

1. K Nearest Neighbours: Recommends movies from the TMDB 5000 movies dataset based on the list of genres given as input.

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2. Logistic Regression: Predicts how likely peope are to buy a product based on their gender, age, and salary.

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3. Simple Neural Network: 2-layered neural network which mimics the XOR gate, implemented(vectorized) from scratch using NumPy.

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4. Digit Classification: Dataset used: MNIST
   • Contains a binary classifier that labels all 0s as 1 and rest all digits as 0.
   • Also contains an extension of the above classifier that classifies all 10 digits with an accuracy of 94%.
   • Both of the above networks are 2-layered and are implemented(vectorized) from scratch using NumPy.

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5. Decison Trees: Decision Tree classifier implemented from scratch in python. Dataset used: Banknote authentication dataset

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6. Support Vector Machine: A simple C-SVM binary classifier. Dataset used: Breast Cancer Wisconsin Dataset

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7. K-Means Clustering:
   • Dataset used: Synthetic 2-d data with N=5000 vectors and k=15 Gaussian clusters with different degree of cluster overlap
   • Implemented K-Means clustering algorithm. Used matplotlib to visualize clusters and centroids.

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8. Principal Component Analysis:
   • Dataset used: AT&T Database of Faces
   • Applied the Principal Component Analysis (PCA) algorithm for dimensionality reduction on face images.

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9. Moving Averages
   • Dataset used: Air Quality Data Set
   • Applied Simple Moving Average (SMA), Cumulative Moving Average (CMA), Weighted Moving Average (WMA), Exponentially Weighted Average (EWMA) on the dataset, all functions are written in NumPy.

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10. Convolutions
    • Edge Detection using kernels in NumPy
    • Automated Canny Edge Detector

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11. Histogram Equalization
    • Covers the theory behind histogram equalization

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Contributers:

• Harshit Varma (hrshtv)