Material and note of the course of Applied ML in Python

This material has been created based on the tutorials of the course 14.388 Inference on Causal and Structural Parameters Using ML and AI in the Department of Economics at MIT taught by Professor Victor Chernozukhov. All the scripts were in R and we decided to translate them into Python, so students can manage both programing languages. Jannis Kueck and V. Chernozukhov have also published the original R Codes in Kaggle. In adition, we included tutorials on Heterogenous Treatment Effects Using Causal Trees and Causal Forest from Susan Athey’s Machine Learning and Causal Inference course. We aim to add more empirical examples were the ML and CI tools can be applied using both programming languages.

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Repo for the Applied ML course for Rebelway

This repository contains notes and code for the DSBA 6156: Applied Machine Learning course taught at the University of North Carolina at Charlotte in the spring of 2023.

Assignments from Applied AI ML Course

This Jupyterbook has been created based on the tutorials of the course 14.388 Inference on Causal and Structural Parameters Using ML and AI in the Department of Economics at MIT taught by Professor Victor Chernozukhov. All the scripts were in R and we decided to translate them into Julia, so students can manage both programing languages. Jannis Kueck and V. Chernozukhov have also published the original R Codes in Kaggle. In adition, we included tutorials on Heterogenous Treatment Effects Using Causal Trees and Causal Forest from Susan Athey’s Machine Learning and Causal Inference course. We aim to add more empirical examples were the ML and CI tools can be applied using both programming languages.

Final Project for the CS22W: Machine Learning with Graphs course at Stanford University (http://web.stanford.edu/class/cs224w/) in Autumn '21. We applied graph ML techniques and networks (such as the Graph Convolutional Network, GraphSAGE Network and Graph Isomorphism Network) to the ogbl-ddi dataset (https://ogb.stanford.edu/docs/linkprop/#ogbl-ddi) for drug-drug interactions.

Collection of DL/ML models for a graduate Applied AI course

Lab code of course Applied Machine Learning. (Source code from ML in Action) 武汉大学信管本科课程《应用机器学习（实验）》我的实验记录代码

Applied Machine Learning CourseWork UCL MSc

Marketing Data Science is a first of a series of courses on Business Data Science. The course was constructed to be a meeting point between Marketing and Data Science. A marketing framework analysis is proposed composed of four blocs: Profiling, Segmentation, Targeting and Recommendation. For each of these blocs a Data Science analysis is applied. The pivoting question is: How to better understand your customer. Throughout the course we will use a single database to apply the different concepts and Data Science techniques. Three tools will be presented and used: SQL, Power BI and Microsoft Azure ML If you are a marketer who want to be introduced to the Data-Driven analysis field, then this course is for you. If you have a technical background (IT professional, Developer) and you inspire to become a Data Scientist, then you can take advantage of the Marketing Framework Analysis to introduce you to the business skills a has to know.

ML course by esokolov HSE Applied Mathematics and Computer science

This is an introductory course to AI/ML applied to drug discovery

course projects completed by me as a part of the EPFL Extension School Applied Machine Learning specialization

Applied Machine learning course from Coursera

Applied Mathematics for Machine Learning - A comprehensive course covering essential mathematical foundations for data science and machine learning.

This repo contains modules and assignment for the applied Machine Learning course on Coursera

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Repository for course project for the Applied ML course

MIT Applied Data Science and ML Course Projects.

My final project in my Applied AI & ML course.

Командный проект по анализу контрастности текста и фона с использованием ML методов. HSE Applied ML Course 2024/25

Course project in Applied ML, completed during MSc degree at the University of Edinburgh

Course materials, lecture notes, and assignments for the Applied Machine Learning course. Covers classical ML to deep learning and modern architectures (CNNs, RNNs, Transformers, LLMs).

Solution of Sampling Assignment of Course UCS654. Used various techniques to balance dataset. Applied different sampling techniques on different ML models.

This project was done under the course Foundations of Data Science and it deals with Water Quality Analysis dataset where four ML Models haven been built and Hypothesis Testing and Feature Engineering has been applied

An ML-powered clinical pathway recommendation system that helps healthcare providers determine the most appropriate next procedure for patients based on their demographics and conditions. Built as part of the Applied AI in Healthcare course at Karolinska Institutet.