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What you'll learn?
- Get Hand-on on the Application part of machine learning
- Learn and Add Industry Case Studies to your Portfolio
- Learn to Visualize and Do Exploratory data analysis on Complex real World datasets using Mayplotlib, Seaborn and Plotly
- Learning Feature Engineering on Big and Complex Data sets
- Learning Feature Selection on Big and Complex Data sets
- Learn to Optimize and Fine Tune Hyperparameters
- Learn Advance Algorithms like XGBoost, CatBoost, LightGBM etc..
- Learn about Regularization
- Understand and experience the Real world complexity of Machine Learning Problems
Course Overview
12 Real World Case Studies for Machine Learning
Master Machine Learning by getting your hands dirty on Real Life Case studies. Be A Kaggle and Industry Grand master
You might know the theory of Machine Learning and know how to create algorithms. But as you know you must get your hands Dirty on Real-World Case Studies. There are so many courses which teaches the basic of Machine Learning But do not cover the Applications. In this course, We will Cover applications and Case Studies from the Industry.
This course will help you bridge the gap between a person who knows machine learning and a person who actually know how to apply Machine Learning in real world. Knowing Machine learning and Applying it in the real world is totally different.
This course will help you tackle big and complex data set and apply machine learning techniques to achieve good results. These Case Studies will also enhance your resume as you can add these to your Portfolio.
Below are the Case Studies we shall cover in this course:-
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REGRESSION Case Studies
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Retail Store Sales Prediction
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Restaurant Sales Prediction
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Inventory Prediction for Optimum Inventor Management
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Tube Assembly Pricing for Optimizing the Manufacturing Facility
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Coal Production Estimation
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Sport Player Salary Prediction
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CLASSIFICATION Case Studies
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Diabetes Prediction for Preventive Care
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Telecom Network Disruptions Prediction for Planning Preventive Maintenance
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Breast Cancer Prediction for Preventive Care
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Credit Card Fraud Detection
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Heart Diseases Prediction for Preventive Care
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Predict whether a Customer Shall Sign a Loan or Not
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We know that you're here because you value your time and Money.By getting this course, you can be assured that the course will explain everything in detail and if there are any doubts in the course, we will answer your doubts in less than 12 hours.
All the project Files are available for you.
So, What are you waiting for? Go Click on the Buy button and let's explore the exciting journey of Machine Learning Case Studies.
I will be waiting for you inside the course...
Cosmic
Pre-requisites
- Basics of Machine Learning
- Python Programming
- Jupyter Notebook
Target Audience
- Beginners interested in enhancing their Knowledge on the Application part of Machine Learning
- Intermediates interested in enhancing their Knowledge on the Application part of Machine Learning
- Professionals interested in enhancing their Knowledge on the Application part of Machine Learning
- Anyone willing to Understand how Machine learning is applied to Real Life Problems can take this course.
Curriculum 112 Lectures 11:55:15
Section 1 : Introduction
- Lecture 2 :
- Data and NoteBook Resources
Section 2 : REGRESSION CASE STUDY : Retail Store Sales Prediction
- Lecture 1 :
- Intro and Business Challenge
- Lecture 2 :
- General Overview on Regression Metrics
- Lecture 3 :
- Basic Data imports
- Lecture 4 :
- Visualization and EDA
- Lecture 5 :
- Feature Engineering
- Lecture 6 :
- Model Building and Evaluation
- Lecture 7 :
- Model Building and Evaluation
- Lecture 8 :
- Conclusion
Section 3 : CLASSIFICATION CASE STUDY : Telstra Telecom Network Disruptions Challenge
- Lecture 1 :
- Intro and Business Challenge
- Lecture 2 :
- General Overview of Classification Metrics
- Lecture 3 :
- Data import and Data engineering
- Lecture 4 :
- Feature engineering
- Lecture 5 :
- Feature engineering
- Lecture 6 :
- Feature engineering
- Lecture 7 :
- Feature Selection
- Lecture 8 :
- Model prediction and Evaluation
- Lecture 9 :
- Balancing the dataset and RePredicting
- Lecture 10 :
- Conclusion
Section 4 : REGRESSION CASE STUDY : Restaurant Sales Prediction
- Lecture 1 :
- Intro and Business Challenge
- Lecture 2 :
- General Overview on Regression Metrics
- Lecture 3 :
- Basic Data Imports
- Lecture 4 :
- Visualization and EDA
- Lecture 5 :
- Feature Engineering
- Lecture 6 :
- Model fitting and Evaluation ( Part 1 )
- Lecture 7 :
- Model fitting and Evaluation ( Part 2 )
- Lecture 8 :
- Semi-Supervised Learning
- Lecture 9 :
- Conclusion
Section 5 : CLASSIFICATION CASE STUDY : Credit Card Fraud Detection
- Lecture 1 :
- Intro and Business Challenge
- Lecture 2 :
- General Overview on Classification metrics
- Lecture 3 :
- Importing Data
- Lecture 4 :
- Feature Engineering and Model prediction
- Lecture 5 :
- Balancing Dataset by Under Sampling
- Lecture 6 :
- Balancing Dataset by Over Sampling
- Lecture 7 :
- Conclusion
Section 6 : REGRESSION CASE STUDY : Inventory Prediction
- Lecture 1 :
- Intro and Business Challenge
- Lecture 2 :
- General Overview on Regression Metrics
- Lecture 3 :
- Intro and Basic Data Cleaning
- Lecture 4 :
- Feature Engineering and Visualization
- Lecture 5 :
- Feature Engineering and Visualization
- Lecture 6 :
- Model Prediction and Evaluation
- Lecture 7 :
- Conclusion
Section 7 : CLASSIFICATION CASE STUDY : Diabetes Prediction
- Lecture 1 :
- Intro and Business Challenge
- Lecture 2 :
- General Overview of Classification Metrics
- Lecture 3 :
- Data Import and Some Basic Checks
- Lecture 4 :
- Visualization and EDA
- Lecture 5 :
- Feature Engineering
- Lecture 6 :
- Model Building and Evaluation Process
- Lecture 7 :
- Balancing the Dataset
- Lecture 8 :
- Refitting the Model on New Dataset
- Lecture 9 :
- Conclusion
Section 8 : REGRESSION CASE STUDY : Caterpillar Tube Assembly Pricing
- Lecture 1 :
- Intro and Business Challenge
- Lecture 2 :
- General Overview of Regression Metrics
- Lecture 3 :
- Data import and Feature Engineering
- Lecture 4 :
- Feature Engineering
- Lecture 5 :
- Feature Engineering ( Part 2)
- Lecture 6 :
- Feature Engineering ( Part 3)
- Lecture 7 :
- Model Building and Evaluation
- Lecture 8 :
- Model Building (Part 2)
- Lecture 9 :
- Conclusion
Section 9 : CLASSIFICATION CASE STUDY : Breast Cancer Prediction
- Lecture 1 :
- Intro and Business Challenge
- Lecture 2 :
- General Overview of Classification Metrics
- Lecture 3 :
- Data Import and Basic Data Cleaning
- Lecture 4 :
- Visualization, Feature Scaling and Encoding
- Lecture 5 :
- Model Fitting and checking the Feature Importance
- Lecture 6 :
- Balancing the Dataset and Feature Selection
- Lecture 7 :
- Conclusion
Section 10 : REGRESSION CASE STUDY : Coal Production Estimation
- Lecture 1 :
- Intro and Business Challenge
- Lecture 2 :
- General Overview of Regression Metrics
- Lecture 3 :
- Data Import and Some Basic Cleaning
- Lecture 4 :
- Visualization and EDA
- Lecture 5 :
- Feature Engineering
- Lecture 6 :
- Model Building and Evaluation
- Lecture 7 :
- Conclusion
Section 11 : CLASSIFICATION CASE STUDY : Heart Diseases Prediction
- Lecture 1 :
- Intro and Business Challenge
- Lecture 2 :
- General Overview of Classification Metrics
- Lecture 3 :
- Data import and Basic Data Cleaning
- Lecture 4 :
- Visualization and EDA
- Lecture 5 :
- Feature Engineering
- Lecture 6 :
- Model Building and Evaluation
- Lecture 7 :
- Some Bug Fixes
- Lecture 8 :
- Balancing the Dataset and Refitting the Models
- Lecture 9 :
- Conclusion
Section 12 : CLASSIFICATION CASE STUDY : Predict whether a Customer Shall Sign a Loan or Not
- Lecture 1 :
- Intro and Business Challenge
- Lecture 2 :
- General Overview of Classification Metrics
- Lecture 3 :
- Basic Data Imports
- Lecture 4 :
- Basic Feature Engineering and Visualization
- Lecture 5 :
- Feature Engineering ( Part 2 )
- Lecture 6 :
- Model Prediction and Evaluation
- Lecture 7 :
- Conclusion
Section 13 : REGRESSION CASE STUDY : Player Salary Prediction
- Lecture 1 :
- Intro and Business Challenge
- Lecture 2 :
- General Overview of Regression Metrics
- Lecture 3 :
- Data Import
- Lecture 4 :
- Feature Engineering and visualization ( Part 1 )
- Lecture 5 :
- Feature Engineering and visualization ( Part 2 )
- Lecture 6 :
- Outlier Detection and Removal
- Lecture 7 :
- Feature Scaling
- Lecture 8 :
- Feature Encoding
- Lecture 9 :
- Model Fitting and Evalution
- Lecture 10 :
- Suggestion to Improve this model
- Lecture 11 :
- Conclusion
Section 14 : Predict Whether a Patient will show up on a Medical Appointment or Not
- Lecture 1 :
- Business Challenge and General Overview
- Lecture 2 :
- Data Import
- Lecture 3 :
- Feature Engineering
- Lecture 4 :
- Visualization (Part 1 )
- Lecture 5 :
- Visualization (Part 2 )
- Lecture 6 :
- Model Building and Fitting
- Lecture 7 :
- OverSampling of Data
- Lecture 8 :
- Refitting of Model
- Lecture 9 :
- Conclusion
Section 15 : Conclusion
- Lecture 1 :
- Conclusion
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Instructor
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Hi, We are Cosmic .
Professionally, We are Entrepreneurs with over six years of experience in finance, Tech, Real Estate etc. We are trained at Mobile App Development, Web Development, Artificial Intelligence, Finance etc.
In my courses, you will very well understand how I combine theory and Real Life Case studies to give you the best in-Class Training in any topic IÂ teach.
I am looking forward to sharing my passion and knowledge with you!
Looking forward to working together!
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