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- Data Visualization Workshop
- Introduction to Tableau (Summer 2021)
- Data Visualization using Python (Matplotlib and Seaborn)
- Data Visualization Using Python - Interactive Plots (Bokeh)
- Data Visualization using Microsoft PowerPoint and Excel
- Data Visualization with R Part 1: Intro to R
- Data Visualization with R Part 2: Tidyverse/Tidy Data and dplyr
- Data Visualization with R Part 3 - Web Scraping with OpenRefine API
- Data Visualization with R Part 4: ggplot2
- Data Visualization with R Part 5 - Sentiment Analysis
- Data Visualization using Tableau (Summer 2020)

- Machine Learning WorkshopToggle Dropdown
- Introduction to Python
- Machine Learning Overview (using Python)
- Preparing your data for Machine Learning
- Machine Learning using Matlab
- Supervised Learning 1 - Linear Classifiers
- Supervised Learning 2 - Tree Based Models
- Application 1 - Sentiment Analysis
- Application 2 - Dimensionality Reduction
- Application 3 - Time Series Data
- Unsupervised Learning - Clustering Analysis
- Model Validation and Selection
- Fairness and Bias in Machine Learning
- Explainable AI - An Overview
- Introduction to Reinforcement Learning

- Machine Learning and Deep Learning Workshop - 2021Toggle Dropdown
- Introduction to Neural Networks
- Intro to Automated Machine Learning: Hyper-Parameter Tuning
- Introduction to NLP part1 - text processing
- Hyper-Parameter Tuning: Bayesian Optimization
- Introduction to NLP Part 2 - Neural Networks
- Introduction to Julia
- Introduction to Computer Vision with Neural Networks
- Intro to Python visualization tools: Seaborn and ipywidgets.
- Data Scraping and Analysis with Python
- Intro to Reinforcement Learning on an optimization perspective.

- Machine Learning and Deep Learning Workshop - 2022Toggle Dropdown
- Data Scraping and Analysis with Python
- Introduction to Neural Networks
- Introduction to Computer Vision with Neural Networks
- Intro to Hyperparameter Optimization: Black-Box Optimization Approaches
- Introduction to Generative adversarial networks (GANs)
- Introduction to Recommender Systems
- Intro to Parallel Computing
- Introduction to Python in Data Science
- Intro to Supervised and Unsupervised Machine Learning Algorithms
- Data Scraping and Analysis with Python
- Intro to Java and Algorithms Part 1
- Intro to Java and Algorithms Part 2
- Introduction to Nueral Network
- Introduction to Web API and Database
- Intro to RNN and LSTM
- Introduction to Transformers in Image Processing
- Intro to Hyperparameter Optimization: Bayesian Optimization

- Machine Learning and Deep Learning Workshop - 2023Toggle Dropdown

Master the basics of data analysis by manipulating basic data structures such as vectors, matrices, and data frames. Also, learning about conditional statements, loops, and vector functions.

- R Markdown FileR markdown file used during the workshop
- PDF FilePdf file of the R file used during the workshop

- Last Edited: Sep 6, 2023 10:09 AM
- URL: https://guides.lib.purdue.edu/d-velop
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