An Interactive Approach to Understanding Deep Learning with Keras
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Updated
Dec 8, 2022 - Jupyter Notebook
An Interactive Approach to Understanding Deep Learning with Keras
Utilizing Kaggle Data and Real-World Data for Data Science and Prediction in Python, R, Excel, Power BI, and Tableau.
Awesome list of AutoML frameworks - curated by @oskar-j
Apply supervised machine learning techniques and an analytical mind on data collected for the U.S. census to help CharityML (a fictitious charity organization) identify people most likely to donate to their cause
Useful tools for constructing species distribution models
Recommender systems with collaborative filtering created with Apache Mahout framework. The system uses a Music Recommendation dataset for research purposes as input, but you can train it and predict recommendations with any other dataset.
skrobot is a Python module for designing, running and tracking Machine Learning experiments / tasks. It is built on top of scikit-learn framework.
From a dataset provided by a leading commercial bank in Vietnam, profile customers of the bank and predict who are likely to churn.
Applying Supervised learning techniques on data to help CharityML identify people most likely to donate to their cause.
Sameer Girolkar's AIML practice Notebooks
The data from this survey are used for a wide range of equipment design, sizing, and tariffing applications within the military and have many potential commercial, industrial, and academic applications.
Project with examples of different recommender systems created with the Surprise framework. Different algorithms (with a collaborative filtering approach) are explored, such as KNN or SVD.
In this project we will try to predict if the person has diabetes has or not.
18 Projects in AI & ML
Applied reinforcement learning to build a simulated vehicle navigation agent. This project involved modeling a complex control problem in terms of limited available inputs, and designing a scheme to automatically learn an optimal driving strategy based on rewards and penalties.
Machine learning prediction project, R studio, 2019.
Austin Housing Price Predictions is a start-to-finish regression project which includes image processing, NLP, Neural Networks, transfer learning, and model ensembling.
A project featuring use of statistical techniques for exploratory data analysis and data mining techniques for predicting the quality of wine. 🍷🍸🍹
A Predictive Model for Marketing Campaigns
I used this notebook to discuss different supervised learning approaches. In the notebook you can find evaluations of a logistic regression, a K-Nearest-Neighboor, a Support Vector Machine, a Decision Tree and the ensemble methods Random Forest, AdaBoost and XGBoost Classifyer.
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