In this project, we are going to use binary leaf images and extracted features, including shape, margin, and texture to accurately identify plant species using different benchmark classification techniques.
Image Processing
Feature selection
Classifier comparison
Benchmarking
Prediction
Identify Plant Species with Image Classification - Benchmarking Classifiers
In this project, we are going to use binary leaf images and extracted features, including shape, margin, and texture to accurately identify plant species using different benchmark classification techniques.
In this project, we are going to work on a German credit dataset project using classification techniques like Decision Tree, Neural Networks etc to Classify Loan Applications using R.
Application of Logistic Regression
Decision Tree based rules
Neural Network
Benchmarking
Feature selection
and more...
Train a Neural Network to Classify Loan Applications using Decision Trees
In this project, we are going to work on a German credit dataset project using classification techniques like Decision Tree, Neural Networks etc to Classify Loan Applications using R.
In this project, we are going to predict customer churn using Artificial Neural networks and Deep Learning and show you how to model an ANN in R with the keras package.
Deep learning Basics
Keras in R
Churn prediction
Lime use in R
Feature Importance in R
Forecast Customer Churn by building a Neural Network in R using Keras
In this project, we are going to predict customer churn using Artificial Neural networks and Deep Learning and show you how to model an ANN in R with the keras package.
Data Science Project in R- Predict the churn customer of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.
Understand the customer behavior
Understand reasons for churn
What are the top factors
How to retain customers
Apply multiple classification models
Predict Churn for a Telecom Company using Logistic Regression Classification in R
Data Science Project in R- Predict the churn customer of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.
In this hackerday, we are going to develop an algorithm that will automatically identify the boundaries of the car images which will help to remove the photo studio background.
Understanding Image Masking
Different Exploratory methods of Image Analysis
Similar and Duplicate Analysis
Working on Convolution Neural Network
Evaluation of Image Algorithm
Carvana Image Masking Challenge
In this hackerday, we are going to develop an algorithm that will automatically identify the boundaries of the car images which will help to remove the photo studio background.
In this exercise, we will explore a data set on wine quality. The objective is to explore which chemical properties influence the quality of red wines.
Data Exploration
Asking the right questions for analysis
Data Visualisation
Storytelling
Applying regression models
Best Seller
Asses Wine Qualities by Implementing Regression Model in R Markdown and Storyboarding
In this exercise, we will explore a data set on wine quality. The objective is to explore which chemical properties influence the quality of red wines.
In this machine learning and IoT project, we are going to test out the experimental data using various predictive models and train the models and break the energy usage.
Multiple linear regression,
Support vector machine with radial kernel,
Random forest and
Gradient boosting machines (GBM).
Use of statistical models with repeated cross validation and evaluated in a testing set
Machine Learning or Predictive Models in IoT - Energy Prediction Use Case
In this machine learning and IoT project, we are going to test out the experimental data using various predictive models and train the models and break the energy usage.
In this project, we are trying to uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.
Application of linear regression
Application of non-linear regression
Application of LASSO and elastic net regression
Application of XGBoost model
Interpretation of models
Predict Macro Economic Trends using XGBoost, Linear Regression, Lasso in R
In this project, we are trying to uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.
In this project, we will predict which products their existing customers will use in the next month based on their past behavior and that of similar customers.
BFSI Domain Understanding
Recommendation Engine Building
Advanced Exploratory Data Analysis
Visualization using multiple Advanced Functions
Classification Models for Parallel Processing
and more...
Can you pair products with people?
In this project, we will predict which products their existing customers will use in the next month based on their past behavior and that of similar customers.
In this project, we'll work through a real-world scenario using the Cortana Intelligence Suite tools, including the Microsoft Azure Portal, PowerShell, and Visual Studio.
The Data Science Process
CIS Platform components
Administration concepts
Tools installation and overview
PowerShell Interface
and more...
Analytics Workload using Microsoft Cortana, Azure, Power Shell and Visual Studio
In this project, we'll work through a real-world scenario using the Cortana Intelligence Suite tools, including the Microsoft Azure Portal, PowerShell, and Visual Studio.
In this project, you will build a model to predict the purchase amount of customer against the various product which will help them to create personalized offer for customers against different products.
Advanced Exploratory Data Analysis
Advanced Feature Engineering
Visualization using multiple Advanced Functions
Regressor Modelling Technique
Ensemble models
and more...
Create personalized offers for customers based on spending habits
In this project, you will build a model to predict the purchase amount of customer against the various product which will help them to create personalized offer for customers against different products.
In this data science project, you will be working on building a machine learning model that can identify nerve structures in a data set of ultrasound images of the neck. This will help enhance catheter placement and contribute to a more pain free future.
How to decide which segmentation method to use
Deciding on data set to be used for segmentation
Subjective or objective segmentation
Implementation using R or Python
Comparison of various model results
and more...
Data Science Project - Ultrasound Nerve Segmentation
In this data science project, you will be working on building a machine learning model that can identify nerve structures in a data set of ultrasound images of the neck. This will help enhance catheter placement and contribute to a more pain free future.
Given a customer's search query and the returned product in text format, your predictive model needs to tell whether it is what the customer was looking for.
Scikit-learn
Pandas
Numpy
Random Forrest algorithm
Home Depot Product Search Relevance
Given a customer's search query and the returned product in text format, your predictive model needs to tell whether it is what the customer was looking for.
Build an auto-access machine learning model based on the historical data to determine the access privileges based on the employees job role and the resource he applied for.
Scikit-learn
Pandas
Numpy
Random Forrest algorithm
Predict Employee Computer Access Needs Using Random Forest, Scikit, Pandas in Python
Build an auto-access machine learning model based on the historical data to determine the access privileges based on the employees job role and the resource he applied for.
Forecast the business for the upcoming years by Exploring Hidden Trends, Calculating Machine Productivity , Extrapolation and Assumptions and Summarizing Answers through Visualizations.
Plotting in Excel.
Use of Pivot table in Excel.
Extrapolation in Excel.
Getting Trend in Excel.
Dynamic Charting.
and more...
Data Analysis Project - Design the business plan for distributing insurance to customers
Forecast the business for the upcoming years by Exploring Hidden Trends, Calculating Machine Productivity , Extrapolation and Assumptions and Summarizing Answers through Visualizations.
Learn to classify the sentiment of sentences from the Rotten Tomatoes dataset. You will be asked to label phrases on a scale of five values: negative, somewhat negative, neutral, somewhat positive, positive.
Text Mining and
Sentiment Classification
Information extraction from Text
Learn use of library "tm"
Learn use of libraries wordcloud, cluster
and more...
Perform Sentiment Analysis on Movie Reviews using K-means Clustering in R
Learn to classify the sentiment of sentences from the Rotten Tomatoes dataset. You will be asked to label phrases on a scale of five values: negative, somewhat negative, neutral, somewhat positive, positive.
Data Science Project in R-Predict the sales for each department using historical markdown data from the Walmart dataset containing data of 45 Walmart stores.
Timeseries Dataset
Conditional statements in R
Loops in R
Data manipulation in R
plyr - R package
and more...
Walmart Sales Forecasting Data Science Project
Data Science Project in R-Predict the sales for each department using historical markdown data from the Walmart dataset containing data of 45 Walmart stores.
In this challenge, we will complete the analysis of what sorts of people were likely to survive.You will learn to use various machine learning tools to predict which passengers survived the tragedy.
Learn machine learning in Python
Python Numpy functions
Learn Python plotting using Matplotlib data science library
Explore Titanic Dataset using Python Pandas
Data Types in Python
and more...
Data Science Project -Predicting survival on the Titanic
In this challenge, we will complete the analysis of what sorts of people were likely to survive.You will learn to use various machine learning tools to predict which passengers survived the tragedy.