Awesome Case Study and Explanation. I feel like I got what i wanted
12 Real-World Case Studies For Machine Learning
Learn Machine Learning by working with real-world case studies. Become an industry grandmaster on Kaggle
Development ,Data Science,Machine Learning
Lectures -102
Duration -11 hours
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Course Description
Machine Learning course by Tutorials Point provides real-world case studies for your better understanding. You can grasp the subject fundamentally from numerous courses and even get familiar with algorithm development and Machine Learning in theory. This course will cover the applications and case studies from the industry.
Course Overview:
This course will assist you in bridging the knowledge gap between those who understand machine learning and those who can really put it to use in the real world. Understanding machine learning and using it in practice are two completely different things.
You will learn how to work with large, complicated data sets and use machine-learning approaches to produce effective outcomes in this course. These case studies can be included in your Portfolio, which can improve your resume as well.
The Case Studies that are covered in this course:
Regression Case Studies:
Retail Store Sales Prediction.
Restaurant Sales Prediction.
Inventory Prediction for Optimum Inventory Management.
Tube Assembly Pricing for Optimizing the Manufacturing Facility.
Coal Production Estimation.
Sports Player Salary Prediction.
Classification Case Studies:
Diabetes Prediction for Preventive Care.
Telecom Network Disruptions Prediction for Planning Preventive Maintenance.
Breast Cancer Prediction for Preventive Care.
Credit Card Fraud Detection.
Heart Diseases Prediction for Preventive Care.
Predict whether a Customer Shall Sign a Loan or Not.
Goals
Get experience with the Machine Learning application.
Visualize and conduct exploratory data analysis on difficult real-world Datasets Using Mayplotlib, Seaborn, and Plotly.
Add industry case studies to your portfolio.
Learn Feature Engineering for large and complicated data collections.
Use large and complicated data sets to learn feature selection.
Enhance and fine-tune hyperparameters.
Get knowledge of advanced algorithms such as XGBoost, CatBoost, LightGBM, etc.
Discover regularisation.
Recognize and experience the complexity of Machine Learning problems in the real world.
Improve your ability to handle problem statements with Machine Learning.
Prerequisites
Basics of Machine Learning.
Python Programming.
Jupyter Notebook.
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
2 Lectures
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Introduction 03:37 03:37
-
Data and NoteBook Resources
REGRESSION CASE STUDY : Retail Store Sales Prediction
7 Lectures

CLASSIFICATION CASE STUDY : Telstra Telecom Network Disruptions Challenge
10 Lectures

REGRESSION CASE STUDY : Restaurant Sales Prediction
9 Lectures

CLASSIFICATION CASE STUDY : Credit Card Fraud Detection
7 Lectures

REGRESSION CASE STUDY : Inventory Prediction
7 Lectures

CLASSIFICATION CASE STUDY : Diabetes Prediction
8 Lectures

REGRESSION CASE STUDY : Caterpillar Tube Assembly Pricing
9 Lectures

CLASSIFICATION CASE STUDY : Breast Cancer Prediction
7 Lectures

REGRESSION CASE STUDY : Coal Production Estimation
7 Lectures

CLASSIFICATION CASE STUDY : Heart Diseases Prediction
9 Lectures

CLASSIFICATION CASE STUDY : Predict whether a Customer Shall Sign a Loan or Not
7 Lectures

REGRESSION CASE STUDY : Player Salary Prediction
11 Lectures

Conclusion
1 Lectures

Instructor Details

Akshay Deep Lamba
Course Certificate
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