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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 and AI ML,Artificial Intelligence
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.
12 Real-World Case Studies For Machine Learning 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
What will you learn in this course:
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
What are the prerequisites for this course?
Basics of Machine Learning
Python Programming
Jupyter Notebook
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
2 Lectures
- 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
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