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12 Real World Case Studies For Machine Learning




Machine Learning Training Insitute


Graduates and Technology Aspirants


Online and Offline Classes


Week Days and Week Ends

Duration :

1.5  hrs in weekdays and 3hrs during Weekend

Machine Learning What will you learn?

•Learn the core concepts of Machine Learning.
•Learn to manage application state with Machine Learning.
•How to create an Machine Learning project from scratch.
•Different tips on how to handle the Machine Learning interviews.
•Learn Machine Learning from scratch & understand core programming concept
•Learn Machine Learning at your own pace with quality learning videos.
•Learn all about Machine Learning from basic to advanced with interactive tutorials.
•How to setup a Machine Learning script and Interface in real time development.
•Learn to build applications on the most flexible enterprise platform for Distributed applications.

12 real world case studies for machine learning Training Highlights

•Post training offline support available
•We  Groom up your documents and profiles
•Learn Core concepts from Leading Instructors
•Personal attention and guidance for every student
•Assignments and test to ensure concept absorption.
•Hands On Experience – will be provided during the course to practice
•Make aware of code competence in building extensive range of applications using Python
•This Instructor-led classroom course is designed with an aim to build theoretical knowledge supplemented by ample hands-on lab exercises

Who are eligible for Machine Learning

•Backend Developer, mongodb, Sql Server, vba, Node.js, cube, ssis, ssrs, ssas, Architectural Design, html, Advanced Excel, analytics, Advanced Analytics
•HPSM, HPAM, HP PPM, HPBSM, Python, SAP Apo, SAP APO DP, SAP APO SNP, Testing, HP DMA, SAP MM, Mainframe Developer, ETL Testing, JAVA Developer
•java, .Net Developer, Selenium Testing, Production Support, Business Analyst, UI Developer, Manual Testing, Sevice Desk Engineer, Unix Support
•Python, Odoo, Openerp, Odoo 8, Open Erp, .Net, Java Jsp, Software Development, Android App, Ios App Developer, Android, IOS
•Websphere Message Broker, Ibm Bpm, Odm, Cognos Bi, Filenet, Tivoli, Datapower, Redhat Linux, Cloud Computing, Mobile Testing, Devops, Java, .Net, Python


•Data and NoteBook Resources
•REGRESSION CASE STUDY : Retail Store Sales Prediction
•Intro and Business Challenge
•General Overview on Regression Metrics
•Basic Data imports
•Visualization and EDA
•Feature Engineering
•Model Building and Evaluation
•CLASSIFICATION CASE STUDY : Telstra Telecom Network Disruptions Challenge
•General Overview of Classification Metrics
•Data import and Data engineering
•Feature Selection
•Model prediction and Evaluation
•Balancing the dataset and RePredicting
•REGRESSION CASE STUDY : Restaurant Sales Prediction
•Model fitting and Evaluation ( Part 1 )
•Model fitting and Evaluation ( Part 2 )
•Semi-Supervised Learning
•CLASSIFICATION CASE STUDY : Credit Card Fraud Detection
•General Overview on Classification metrics
•Importing Data
•Feature Engineering and Model prediction
•Balancing Dataset by Under Sampling
•Balancing Dataset by Over Sampling
•REGRESSION CASE STUDY : Inventory Prediction
•Intro and Basic Data Cleaning
•Feature Engineering and Visualization
•Data Import and Some Basic Checks
•Model Building and Evaluation Process
•Balancing the Dataset
•Refitting the Model on New Dataset
•REGRESSION CASE STUDY : Caterpillar Tube Assembly Pricing
•General Overview of Regression Metrics
•Data import and Feature Engineering
•Feature Engineering ( Part 2)
•Feature Engineering ( Part 3)
•Model Building (Part 2)
•CLASSIFICATION CASE STUDY : Breast Cancer Prediction
•Data Import and Basic Data Clearning
•Visualization, Feature Scaling and Encoding
•Model Fitting and checking the Feature Importance
•Balancing the Dataset and Feature Selection
•REGRESSION CASE STUDY : Coal Production Estimation
•Data Import and Some Basic Cleaning
•CLASSIFICATION CASE STUDY : Heart Diseases Prediction
•Data import and Basic Data Cleaning
•Some Bug Fixes
•Balancing the Dataset and Refitting the Models
•CLASSIFICATION CASE STUDY : Predict whether a Customer Shall Sign a Loan or Not
•Data Import
•Basic Feature Engineering and Visualization
•Feature Engineering ( Part 2 )
•REGRESSION CASE STUDY : Player Salary Prediction
•Feature Engineering and visualization ( Part 1 )
•Feature Engineering and visualization ( Part 2 )
•Outlier Detection and Removal
•Feature Scaling
•Feature Encoding
•Model Fitting and Evalution
•Suggestion to Improve this model
•What Next ?