Collection of code covering various topics in Machine Learning
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Updated
Dec 16, 2017 - Jupyter Notebook
Collection of code covering various topics in Machine Learning
Principal Component Analysis and Linear Regression using Racket
Image compression using the K-means clustering algorithm | Dimensionality reduction using PCA
The implementations in this repository deal with clustering and dimensionality reduction for MNIST digits dataset. Kmeans clustering algorithm is implemented. Also different hierarchical clustering algorithms are tested. We also play with the PCA and TSNE embeddings of the MNIST dataset.
Assignments Solution for Foundations of Machine Learning Course
Principal Component Analysis and Cluster Analysis for lending club loan dataset of 27000 observations using K-means
Details of certified courses covered by me. Includes notes and solutions to programming exercises.
Introduction to Machine Learning & Deep Learning
Customer Segmentation Analysis Based on Products Purchased Using the K-Means Method
Classification of the states of Mexico according to their level of electoral complexity.
In this project we can see in action and in detail a big part of the ML pipeline (data wrangling,model building, model evaluation) that comprises different algorithms and approaches such as Decision Trees (RPART), Linear Discriminant Analysis (LDA), Gradient Boosting Machne (GBM), Random Forest (RF) Support Vector Machine (SVM) with or without M…
Implementation of Persistent Homology Transform
Identifying Customer Segments using unsupervised learning techniques
Using PCA to reduce the dimension of data and for factor Analysis on Boston Housing Data
linear algebra,math concepts and applications
Clustering Crypto. A project that explores the following areas: Pandas DataFrames, Data Cleaning, Scaling Data, K Means, Principal Component Analysis, hvPlot, and 3-D Plotting
Simple application to visualize the result of the PCA
Predicting that the patient is suffering from Heart Disease or Myocardial Infarction (MI) based on various parameters.
Contains nongraded challenge during FTDS Batch 001-Phase 1 at Hacktiv8
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