Machine Learning Journey

This repository contains:

My notes on the book "Mathematics for Machine Learning" (PDF and HTML)
Many different machine learning models, either built from scratch, using only numpy, or using models from sklearn
An automatic differentiation system (MLlib.py) to create trainable neural networks.
Usage of the Automatic differentiation system for different tasks, like
1. Digit Classification (Digits.ipynb)
2. Iris dataset classification (Iris.ipynb)
3. Embedding (Embed.ipynb)
4. Reccurent Neural Network (RNN.ipynb)
5. Illustration of Automotic differentiation (AutoDiff.ipynb)
Data Narratives on a few datasets

Name		Name	Last commit message	Last commit date
Latest commit History 29 Commits
Extra Math for ML 954812ec99c841ff8058c715514b51e3		Extra Math for ML 954812ec99c841ff8058c715514b51e3
Tennis		Tennis
22110197_ES114_DN1.pdf		22110197_ES114_DN1.pdf
22110197_ES114_DN2.pdf		22110197_ES114_DN2.pdf
22110197_ES114_DN3.pdf		22110197_ES114_DN3.pdf
Analytic Geometry.pdf		Analytic Geometry.pdf
AutoDif.ipynb		AutoDif.ipynb
Continuous Optimisation.pdf		Continuous Optimisation.pdf
Digits.ipynb		Digits.ipynb
Embed.ipynb		Embed.ipynb
Five.ipynb		Five.ipynb
Five.pdf		Five.pdf
GuassianNB.ipynb		GuassianNB.ipynb
Iris.ipynb		Iris.ipynb
K_nearest.pdf		K_nearest.pdf
Kcenter.pdf		Kcenter.pdf
Kmeans.pdf		Kmeans.pdf
LOOCV.ipynb		LOOCV.ipynb
LOOCV.pdf		LOOCV.pdf
Linear algebra.pdf		Linear algebra.pdf
Linear.ipynb		Linear.ipynb
MLlib.py		MLlib.py
Matrix Decompositions.pdf		Matrix Decompositions.pdf
NaiveBayes.ipynb		NaiveBayes.ipynb
PCA.ipynb		PCA.ipynb
PCA.pdf		PCA.pdf
Probability.pdf		Probability.pdf
README.md		README.md
RNN.ipynb		RNN.ipynb
Regression.ipynb		Regression.ipynb
SVD.py		SVD.py
TwoD_FFT.py		TwoD_FFT.py
Vector Calculus.pdf		Vector Calculus.pdf
Wine.py		Wine.py
index.html		index.html
kCenter.py		kCenter.py
kMeans.py		kMeans.py

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