Vehicle detection using machine learning and computer vision techniques for Udacity's Self-Driving Car Engineer Nanodegree.

Resources for students in the Udacity's Machine Learning Engineer Nanodegree to work through Stanford's Convolutional Neural Networks for Visual Recognition course (CS231n).

All projects and lecture notes of the Udacity Machine Learning Engineer Nanodegree.

Notes and material for the "Machine Learning Engineer Nanodegree" (MLND) by Udacity.

An AWS Sagemaker Model developed for Nudity / NSFW Images Classification. This Model was developed during my Machine Learning Engineer Nanodegree.

Machine Learning Engineer Nanodegree portfolio, which includes projects and their notebooks/reports.

Udacity Machine Learning Engineer Nanodegree

Projects for Udacity's Machine Learning Engineer Nanodegree

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Starter Code for the Course 1 project of the Udacity Machine Learning Engineer Nanodegree Program

Predicting Global Supply Chain Outcomes for Essential HIV Medicines using Machine Learning Techniques. Capstone original project for Udacity Machine Learning Engineer Nanodegree.

Udacity Machine Learning Engineer Nanodegree

*****PROJECT SPECIFICATION: Machine Learning Capstone Analysis Project***** This capstone project involves machine learning modeling and analysis of clinical, demographic, and brain related derived anatomic measures from human MRI (magnetic resonance imaging) tests (http://www.oasis-brains.org/). The objectives of these measurements are to diagnose the level of Dementia in the individuals and the probability that these individuals may have Alzheimer's Disease (AD). In published studies, Machine Learning has been applied to Alzheimer’s/Dementia identification from MRI scans and related data in the academic papers/theses in References 10 and 11 listed in the References Section below. Recently, a close relative of mine had to undergo a sequence of MRI tests for cognition difficulties.The motivation for choosing this topic for the Capstone project arose from the desire to understand and analyze potential for Dementia and AD from MRI related data. Cognitive testing, clinical assessments and demographic data related to these MRI tests are used in this project. This Capstone project does not use the MRI "imaging" data and does not focus on AD, focusses only on Dementia. *****Conclusions, Justification, and Reflections***** [Student adequately summarizes the end-to-end problem solution and discusses one or two particular aspects of the project they found interesting or difficult.] The formulation of OASIS data (Ref 1 and 2) in terms of a dementia classification problem based on demographic and clinical data only (and without directly using the MRI image data), is a simplification that has major advantages and appeal. This means the trained model can classify whether an individual has dementia or not with about 87% accuracy, without having to wait for radiological interpretation of MRI scans. This can provide an early alert for intervention and initiation of treatment for those with onset of dementia. The assumption that the combined cross-sectional and longitudinal datasets would lead to dementia label classification of acceptable accuracy came out to be true. The method required careful data cleaning and data preparation work, converting it to a binary classification problem, as outlined in this notebook. At the outset it was not clear which algorithm(s) would be more appropriate for the binary and multi-label classification problem. The approach of spot checking the algorithms early for accuracy led to the determination of a smaller set of algorithms with higher accuracy (e.g. Gadient Boosting and Random Forest) for a deeper dive examination, e.g. use of a k-fold cross-validation approach in classifying the CDR label. The neural network benchmark model accuracy of 78% for binary classification was exceeded by the classification accuracy of the main output of this study, the trained Gradient Boosting and Random Forest classification models. This builds confidence in the latter model for further training with new data and further classification use for new patients.

Capstone Project Gold Price Prediction using Machine learning Approach for Udacity Machine Learning engineer Nanodegree Program

Machine Learning Engineer Nanodegree Program On Udacity

Machine Learning DevOps Engineer Nanodegree

Udacity Machine Learning Engineer Nanodegree - Capstone Project

Machine Learning Engineer Nanodegree Project Udacity

Udacity Nanodegree Machine Learning Engineer assignments & note-taking.

Evaluate stylistic visual similarity of clothing and jewelry. Machine Learning Engineer Nanodegree Capstone Project.

Udacity Machine Learning Engineer Nanodegree capstone proposal.

These are my notes of the Udacity Nanodegree Machine Learning DevOps Engineer.

Udacity Machine Learning Engineer Nanodegree Capstone Project : Dog breed classification using Convolutional Neural Networks

Capstone project for Udacity's Machine Learning Engineer nanodegree

Code for the AWS DeepRacer Scholarship Challenge - AWS and Udacity are teaming up to teach machine learning and prepare students to test their skills by participating in the world’s first autonomous racing league—the AWS DeepRacer League. Students with the top lap times will earn full scholarships to the Machine Learning Engineer Nanodegree program.

Project work for the Udacity Machine Learning Engineer Nanodegree

My capstone project for the Machine Learning Engineer Nanodegree from Udacity

End to End Sentiment Analysis Project (Udacity Machine Learning Engineer Nanodegree)

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Udacity Machine Learning Engineer Nanodegree Capstone Project