Showcase of MLflow capabilities

Assignment-04-Simple-Linear-Regression-1. Q1) Delivery_time -> Predict delivery time using sorting time. Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization, Feature Engineering, Correlation Analysis, Model Building, Model Testing and Mo…

Assignment-04-Simple-Linear-Regression-2. Q2) Salary_hike -> Build a prediction model for Salary_hike Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization. Correlation Analysis. Model Building. Model Testing. Model Predictions.

Yolact++ training with custom dataset (coco.json format) in Google Colab

mask rcnn training with coco-like dataset. You can use for trainnig your own coco.json (polygon) dataset in Google Colab.

Supervised-ML-Decision-Tree-C5.0-Entropy-Iris-Flower-Using Entropy Criteria - Classification Model. Import Libraries and data set, EDA, Apply Label Encoding, Model Building - Building/Training Decision Tree Classifier (C5.0) using Entropy Criteria. Validation and Testing Decision Tree Classifier (C5.0) Model

📝🔍🖼️ A deep learning application for retrieving images by searching with text.

It involves prediction of House prices in Melbourne using Machine Learning. It involved concepts of Data extraction, Data Preprocessing, Data Visualisation, Data Aggregation, Model Creation and Testing. It comes under Supervised Learning.

The aim is to gain insights into similarity between countries and regions of the world by experimenting with different cluster amounts.

Used libraries and functions as follows:

Q2) Salary_hike -> Build a prediction model for Salary_hike Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization. Correlation Analysis. Model Building. Model Testing. Model Predictions.

Automated Machine Learning Framework for predicting drinking water quality

Credit Card Fraud Detection Using Machine Learning

Implements an entire machine learning pipeline to train and evaluate a Random Forest Classifier on labeled gait data for walking. Data generated during the experiment has led to helpful insights in to the problem domain.

The primary objective of this project was to build and deploy an image classification model for Scones Unlimited, a scone-delivery-focused logistic company, using AWS SageMaker.

This project promulgates an automated end-to-end ML pipeline that trains a biLSTM network for sentiment analysis, experiment tracking, benchmarking by model testing and evaluation, model transitioning to production followed by deployment into cloud instance via CI/CD

Image Classifiers are used in the field of computer vision to identify the content of an image and it is used across a broad variety of industries, from advanced technologies like autonomous vehicles and augmented reality, to eCommerce platforms, and even in diagnostic medicine.

Conducted predictive classification modelling and performance evaluation for several models used to predict the political affiliation (target variable) of random U.S citizens.

The main objective is to understand the relationship between diffeent variable and testeing many Regression model and choosing the efficent one them predincting new points