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02c-tools-data-visualizations

Data-X lecture on data visualization (matplotlib, seaborn, plotly)

applied_ml_using_google_cloud_platform_-gcp-

c.s-datasci

client-version-analysis

data-x

This repository is for the Data-X project materials

datasci-demog

Jupyter notebooks related to data science applications on demographic data using Python

datasharing

The Leek group guide to data sharing

dataviz_plaksha

Brief intro to data visualization theory and principles.

forecasting-automobile-sales

Regression Applications for Predicting Automobile Unit Sales

google-apps-script

housing-price_prediction

Collaborative model for detecting bubbles in real state asset class

interview-prep

intro_aws

intro_dataviz

DATA-X: m130 - Introduction to Visual Principles Using Matplotlib and Seaborn. Provides users with the necessary foundations for building and understanding current state of the art visualizations. An additional aim is to provide users with an understanding of both the theory and techniques of various visualization paradigms. Finally, this series of notebooks seeks to provide sufficient knowledge to users so that they may build & evaluate various visualization systems, read & discuss visualization literature, and successfully convey visual information.

intro_flask

DATA-X: m320 - Flask - Easy Web Development for Rapid Deployment. Provides a quick overview of how to set up a barebones Flask environment. This material can then be used to learn how to productionize ML models, build dynamic dashboard, and build complete websites -- quickly and easily.

intro_numpy

DATA-X: m110 - Numpy - Introduction to Numerical Analysis Using NumPy. These materials introduce developers and data scientists to numerical analysis and data manipulation using NumPy. NumPy is the numerical analysis backbone to several popular open source analysis and machine learning packages.

intro_pandas

DATA-X: m120 - Pandas - Introduction to Data Analysis Using Pandas. Pandas is a commonly used, yet powerful, software library written for Python that is built for expedient data manipulation and analysis. This notebook aims to introduce the syntax, data structures, and manipulation operations commonly seen in Pandas.

intro_regularization_crossvalidation_ludwig

intro_tensorflow

DATA-X: m410 - TensorFlow - Shallow Neural Networks; An Introduction to TensorFlow V.2. Tensorflow (TF) is an open-source library used for dataflow, differentiable programming, symbolic math, and machine learning applications such as deep learning neural networks. TF's flexible architecture allows for easy deployment across varied processing platforms. This notebook covers advanced topics in machine learning. However, it does not require any prior knowledge in machine learning. The goal of this notebook is to teach a user how to deploy a TF model, as well as to provide the user guidance on how to tackle the more nuanced topics.

mentoring

mentoring

ms-hackathon22

Improving Customer Onboarding Experience Using Discrete Event Simulation

nlp_-_nlu_productionized_ml_using_docker

paradigm

Paradigm Projects at UC Berkeley Engineering

pardigm-riskex

Data Mining, Time Series Analysis, and NLP on Bitcoin Related News Events

preprocessing_nlp

radial_plots

Different types of radial plots using Matplotlib and Seaborn

reg_clas_tf_ludwig

DATA-X: m420 - Bread & Butter Deep Learning: Regression and Classification using TensorFlow v2 and Ludwig. This notebook covers advanced topics in machine learning. However, it does not require any prior knowledge in machine learning. The goal of this notebook is to teach a user how to deploy deep learning regression and classification models, using structured data. This is task is so common to machine learning, that it is pretty much the bread and butter of ML engineers.

synthetic_data_and_d3_dashboard

thesis-sharelatex

ucv-data-project1-groupn

Class project on assigning bonuses based on sales using matlab