Nguyễn Minh Mẫn's starred repositories
-Supervised-Machine-Learning
(Supervised) Machine Learning Instructor: Paul Clough (paul.clough@peakindicators.com | p.d.clough@sheffield.ac.uk) This session will introduce libraries and functions in R for performing Machine Learning (ML). Machine Learning is typically viewed as a sub-field within Artificial Intelligence (AI): “The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages” (Oxford English Dictionary, 2018). The most common forms of ML are: (i) supervised learning (e.g., prediction/regression and classification); (ii) unsupervised learning (e.g., clustering); and (iii) reinforcement learning. This session will focus on supervised ML. We will start by reviewing linear regression that forms a good starting point for understanding machine learning and may be something you are already familiar with. Then we will explore further algorithms that can be used for regression and classification. We will mainly focus on using the caret package for ML, but (as usual) there are many ways of doing things in R and multiple packages that can be used for ML1 . Note: this session is very much a hands-on overview of supervised machine learning and some of the R functions that can be used. For a more theoretical overview you might find the book “An Introduction to Statistical Learning with Applications in R” and the accompanying videos helpful2
F-O-Prediction-Stock-market
Model Implied Volatility Surface dynamics based on the training dataset (training_data.csv), i.e., 2.5 years of volatility surface data is made available for model calibration. Participants are encouraged to do exploratory data analysis on the training data to find any patterns, trends, dependencies among tenors and use any dimension reduction methods before modelling the temporal dynamics of implied volatility surface. More generally, the problem calls for an ML technique to represent dynamics of a matrix-valued stochastic process.
Heart_Attack_Risk_Predictor
In this project we will Make an app which will help us predict the risk of a Heart Attack a person have. We will do use various Algorithms to predict the result and see which one suits best and then we will use Auto ML Library EVAL ML to predict the results.
-ML-model-to-check-spread-of-covid-19
Here, we have created an ML model to check the spread of covid-19 cases on the basis of three datasets. We have used linear regression Algorithm to do it.
Cuisine-Classification
Developed and Deployed an ML Model to do Image Classification by recognizing different types of Cuisines of India. [Inception v3, Flask, TensorFlow]
Emergency-ChatBot
A crude implementation of a chat-bot using ML; responds to queries about what to do during an emergency.