Predict winning NFL games from game stats using logistic regression. Then find out which game stats are most important to your model.

Analyze NFL Stats with Python

Use sentiment analysis and natural language processing to discover how Taylor Swift's underlying themes and vibes have changed throughout her career.

Analyze Taylor Swift Lyrics with Python

Visualize traffic safety data and analyze the relationship between collisions and smartphone usage over time

Analyze Traffic Safety Data with Python

Build a professional web application for image classification using Streamlit and Hugging Face transformers, featuring real-time predictions, classification history, and analytics visualization.

Build an AI Image Classification Dashboard with Streamlit

Apply your knowledge of neural network architectures by building and training various models for classification and time series prediction tasks using popular real-world datasets.

Build Neural Networks for Classifying Digits and Time-Series Predictions

In this project you'll make your own Connect Four AI and try to beat ours

Build Your Own Connect Four AI

Classify tumors as either malignant or benign using K-Nearest Neighbors.

Cancer Classifier

Get started with ChatGPT by writing a short story!

ChatGPT as a Creative Writing Assistant

Practice ChatGPT prompt engineering while learning a new language!

ChatGPT for Learning a New Language

In this project, learn how to use ChatGPT effectively for data analysis insights.

ChatGPT Project: Effective Prompt Engineering

In this project, you will create a neural network that classifies mortality due to heart disease.

Classification

Build a deep learning model with TensorFlow to detect and classify infections from real X-ray scans.

Classify X-rays With Machine Learning

Explore how image reconstruction quality affects zero-shot classification performance of a pretrained CLIP model on CIFAR-10.

Classifying CIFAR-10 with Pretrained CLIP: Original vs Reconstructed Images

In this project, you will use a Convolutional Neural Network (CNN) to classify galaxies.

Classifying Galaxies

Fine-tune pre-trained BERT transformers from Hugging Face in PyTorch to classify IMDB movie reviews.

Classifying IMDB Movie Reviews with BERT Transformers

Train and evaluate a CNN to detect tumors in histopathological scans from the PatchCamelyon dataset.

Classifying Microscopic Histopathology Images with PyTorch

Use your knowledge of dplyr and tidyr to clean up this dataset containing a bunch of census data collected by the US government.

Cleaning US Census Data

In this project, we're going to practice with the skill kit in Alexa Skill so you can hone your skills and feel confident taking them to the real world.
Why? Intents are the questions you want to ask Alexa. Let's edit some previous work to solidify your skills.

Create a New Intent 

Bring together all of your data skills skills into a portfolio project. Clean, analyze, and visualize, a dataset and then compile your results into a portfolio-worthy report.

Data Scientist Final Portfolio Project

You will create a regression deep learning model that predicts an applicant's graduate admissions chances based on various parameters, such as grades, test scores, and program rating.

Deep Learning Regression with Admissions Data

Given that a student answered a question correctly, what is the probability that she really knows the material?

Dr. Dirac's Statistics Midterm

In this project you will categorize emails using a Naive Bayes Classifier.

Email Similarity

Predicting employee attrition is challenging for HR departments, as it helps organizations retain valuable talent and reduce turnover costs. However, AI models used in such decisions must be transparent and interpretable, ensuring that HR professionals can trust and understand the predictions.

In this project, we will use the IBM HR Analytics Employee Attrition Dataset to predict whether an employee will likely leave the company. We will train different machine learning models and apply Explainable AI (XAI) techniques to interpret their predictions.

By the end of this project, we will have a fully interpretable pipeline that can help HR professionals make informed, fair, and data-driven decisions regarding employee retention.

Explainable AI in Employee Attrition Prediction

This project demonstrates how to use the LIME (Local Interpretable Model-agnostic Explanations) library to understand and explain predictions made by a machine learning model for breast cancer diagnosis. Using the Wisconsin Breast Cancer [dataset](https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic), we analyze how individual features, such as mean radius, texture, and smoothness, influence the model's decision to classify a tumor as benign or malignant. By visualizing LIME values, we gain insights into the contribution of each feature to the model's predictions, enabling better transparency and trust in AI-driven healthcare diagnostics.

Explaining Breast Cancer Diagnosis Predictions with LIME

This project demonstrates how to use SHAP (SHapley Additive exPlanations) to interpret the predictions of a machine learning classification model for breast cancer diagnosis. Using the Wisconsin Breast Cancer [dataset](https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic), we analyze how individual features, such as mean radius, texture, and smoothness, influence the model's decision to classify a tumor as benign or malignant. By visualizing SHAP values, we gain insights into the contribution of each feature to the model's predictions, enabling better transparency and trust in AI-driven healthcare diagnostics.

Explaining Breast Cancer Diagnosis Predictions with SHAP

Begin to grasp the environmental impact of transformer models by experimenting with decoders and the CodeCarbon emissions tracking library.

Exploring Transformers and their Carbon Footprint

Use decision trees to predict what continent a flag comes from based on features like color and shape.

Find the Flag

Finetune a generative language model on the text of Mary Shelley's Frankenstein, using either QLoRA or performing a full finetune.

Finetuning a Generative Language Model

Use deep learning to predict forest cover type based on cartographic variables.

Forest Cover Classification

In this project you will learn about the differences between GPT-1 and GPT-2 models and practice your ability to build your own GPT model from scratch on a training dataset of your choice.

From GPT-1 to GPT-2

Create neural networks in PyTorch to generate text using Mary Shelley's novel Frankenstein.

Generating Text with Frankenstein Using PyTorch 

Get practice using Python lists by keeping track of grades in multiple school subjects.

Gradebook

In this project, you will be using K-Means clustering and scikit-learn to cluster images of handwritten digits.

Handwriting Recognition using K-Means

Fit a line to data about the honeybee population decline in the United States.

Honey Production

In this project, you will design, train, and evaluate a neural network model performing the task of regression to predict the life expectancy of countries using this dataset using Python and TensorFlow!

Implementing Neural Networks

Practice your usage of Python lists to organize some pizza data.

Len's Slice

Use clustering algorithms to investigate the way people think about masculinity with data from FiveThirtyEight.

Masculinity Survey

In this project, you will analyze some data from OKCupid, an app that focuses on using multiple choice and short answers to match users. You'll apply your machine learning knowledge to build, train, and test predictions that you draw from the data.

OKCupid Date-A-Scientist

Use Principal Component Analysis (PCA) to transform features from telescope data to classify particles!

PCA Project

Train Perceptrons to work like logic gates! This simple neuron can act as an AND or an OR gate.

Perceptron Logic Gates

Use transformers to fine-tune a vision transfomer (ViT) for image classification and evaluate an object detection model (e.g., DETR) on the Oxford-IIIT Pet Dataset.

Pet Image Classification and Detection with Transformers

Calculate and visualize MLB player strike zones based on their real data.

Predict Baseball Strike Zones With Machine Learning

Use Logistic Regression to predict credit card fraud.

Predict Credit Card Fraud

In this project you will use a Logistic Regression model to predict whether or not a passenger survived the sinking of the RMS Titanic.

Predict Titanic Survival

Build a wine quality classifier with logistic regression using regularization.

Predict Wine Quality with Regularization

Create a neural network in PyTorch to predict electric vehicle charging loads.

Predicting Electric Vehicle Charging Loads

Create a neural network in PyTorch to predict hotel cancellations.

Predicting Hotel Booking Cancellations

Use random forests to predict the income of a person based on census data.

Predicting Income with Random Forests

In this project, you'll use linear regression and national survey data to predict the income of an individual based off of social characteristics like age, gender, and education.

Predicting Income with Social Data

Create a data science portfolio project to demonstrate your technical skills in building and training neural networks with PyTorch.

PyTorch Portfolio Project

Use your knowledge of supervised machine learning models for regression to predict the rating of a restaurant.

Regression Cumulative Project

Train reinforcement learning agents to play CartPole using Monte Carlo algorithms with Python and Gymnasium.



Solve Cart Pole with Reinforcement Learning

Train reinforcement learning agents to play Twenty-One using Q-learning and SARSA algorithms with Python and Gymnasium

Solve Twenty-One with Reinforcement Learning

Use your knowledge of supervised machine learning models to find patterns in social media data.

Supervised Learning Cumulative Project

For this project, you will be analyzing text message data using natural language processing techniques and libraries.

Text Message Analysis

Perform data transformations on a clothing company customer review dataset!

Transforming Data into Features

In this project, you will use real tweets and classifier models to find patterns in the way people use social media.

Twitter Classification

In this project you will use word embeddings to analyze the inaugural addresses of the United States of America presidents.

U.S.A. Presidential Vocabulary

Use your understanding of unsupervised learning and clustering to find patterns in a survey conducted about masculinity.

Unsupervised Learning Cumulative Project

Implement wrapper methods on a dataset relating to health and eating habits!

Projects in machine learning

All projects in machine learning

Analyze NFL Stats with Python

Analyze Taylor Swift Lyrics with Python

Analyze Traffic Safety Data with Python

Build an AI Image Classification Dashboard with Streamlit

Build Neural Networks for Classifying Digits and Time-Series Predictions

Build Your Own Connect Four AI

Cancer Classifier

ChatGPT as a Creative Writing Assistant

ChatGPT for Learning a New Language

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