Data-Scientist-Udacity-Nanodegree-

Introduction

• Udacity Data Scientist Nanodegree Term 1 projects and material for term 2

• This repo contain the exercises, projects and the extra curricular material.

• this is the link for the certificate: https://confirm.udacity.com/SPCUMMK6

Table of Contents

0. Extra Curricular: contains review for numpy, pandas, visualizations. tools: python, matplotlib, seaborn.

Term 1

Lessons

1. Supervised Learning: linear regression, perceptron algorithm, decision trees, naive bayes, SVM, ensemble. tools: python, scikit-learn, matplotlib, seaborn.

2. Deep Learning: Neural Networks, gradient descent, keras, deep learning with pytorch.

3. Unsupervised Learning: Clustering, hierarchical and density-based clustering, gaussian mixture model, PCA, Random projections and ICA

Projects

1. Finding Donors for CharityML: Use supervised learning algorithms such as Ensemble Methods adaboost, Decision tree, Guassian Naive Bayes to identify individuals making more than $50,000 income. Accuracy 86.7%.

2. Image Classifier Project: Develop code for an image classifier built with PyTorch to recognize diferent species of flowers.

3. Identify Customer Segments: Used unsupervised learning techniques to identify segments of the population that form the core customer base for a mail-order sales company in Germany. The data was provided by Arvato company which required data preprocessing, data reduction and clustering using Kmeans.

Term 2

A link for term 2 repo

Licence

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. Please refer to Udacity Terms of Service for further information.