12 Real World CaseStudies for Machine Learning
Created by Akshay Deep Lamba, Last Updated 23-Jun-2020, Language:English
12 Real World CaseStudies for Machine Learning
Master Machine Learning by getting your hands dirty on Real Life Case studies. Be A Kaggle and Industry Grandmaster.
Created by Akshay Deep Lamba, Last Updated 23-Jun-2020, Language:English
What Will I Get ?
- 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
- Make your Self better in Tackling Machine Learning problem statements.
Requirements
- Basics of Machine Learning
- Python Programming
- Jupyter Notebook
Description
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:-
REGRESSION Case Studies
Retail Store Sales Prediction
Restaurant Sales Prediction
Inventory Prediction for Optimum Inventor Management
Tube Assembly Pricing for Optimizing the Manufacturing Facility
Coal Production Estimation
Sport 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
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
Course Content
-
Introduction
2 Lectures 00:03:37-
Introduction
Preview00:03:37 -
Data and NoteBook Resources
-
-
REGRESSION CASE STUDY : Retail Store Sales Prediction
7 Lectures 00:45:39-
Intro and Business Challenge
Preview00:02:14 -
General Overview on Regression Metrics
Preview00:11:06 -
Basic Data imports
00:05:07 -
Visualization and EDA
00:06:46 -
Feature Engineering
00:11:39 -
Model Building and Evaluation
00:07:38 -
Conclusion
00:01:09
-
-
CLASSIFICATION CASE STUDY : Telstra Telecom Network Disruptions Challenge
10 Lectures 01:18:20-
Intro and Business Challenge
Preview00:05:56 -
General Overview on Classification Metrics
00:22:06 -
Data import and Data engineering
00:07:54 -
Feature engineering
00:07:39 -
Feature engineering ( Part 2)
00:14:18 -
Feature engineering ( Part 3)
00:04:10 -
Feature Selection
00:03:52 -
Model prediction and Evaluation
00:03:48 -
Balancing the dataset and RePredicting
00:07:07 -
Conclusion
00:01:30
-
-
REGRESSION CASE STUDY : Restaurant Sales Prediction
9 Lectures 00:55:34-
Intro and Business Challenge
00:04:14 -
General Overview on Regression Metrics
00:11:06 -
Basic Data Imports
00:04:31 -
Visualization and EDA
00:08:06 -
Feature Engineering
00:03:26 -
Model fitting and Evaluation ( Part 1 )
00:05:23 -
Model fitting and Evaluation ( Part 2 )
00:02:06 -
Semi-Supervised Learning
00:15:03 -
Conclusion
00:01:39
-
-
CLASSIFICATION CASE STUDY : Credit Card Fraud Detection
7 Lectures 01:14:19-
Intro and Business Challenge
00:12:51 -
General Overview on CLASSIFICATION Metrics
00:22:06 -
Importing Data
00:07:11 -
Feature Engineering and Model prediction
00:09:03 -
Balancing Dataset by Under Sampling
00:08:40 -
Balancing Dataset by Over Sampling
00:11:33 -
Conclusion
00:02:55
-
-
REGRESSION CASE STUDY : Inventory Prediction
7 Lectures 00:59:40-
Intro and Business Challenge
00:03:20 -
General Overview on Regression Metrics
00:11:06 -
Intro and Basic Data Cleaning
00:09:36 -
Feature Engineering and Visualization
00:16:25 -
Feature Engineering and Visualization ( Part 2 )
00:10:25 -
Model Prediction and Evaluation
00:07:44 -
Conclusion
00:01:04
-
-
CLASSIFICATION CASE STUDY : Diabetes Prediction
8 Lectures 00:44:48-
Intro and Business Challenge
00:02:50 -
General Overview on Classification Metrics
00:22:06 -
Data Import and Some Basic Checks
00:03:02 -
Visualization and EDA
00:03:30 -
Feature Engineering
00:03:34 -
Model Building and Evaluation Process
00:05:13 -
Balancing the Dataset
00:03:19 -
Conclusion
00:01:14
-
-
REGRESSION CASE STUDY : Caterpillar Tube Assembly Pricing
9 Lectures 00:51:53-
Intro and Business Challenge
00:05:25 -
General Overview on Regression Metrics
00:11:06 -
Data import and Feature Engineering
00:08:15 -
Feature Engineering
00:08:07 -
Feature Engineering ( Part 2)
00:04:05 -
Feature Engineering ( Part 3)
00:06:12 -
Model Building and Evaluation
00:03:03 -
Model Building (Part 2)
00:04:02 -
Conclusion
00:01:38
-
-
CLASSIFICATION CASE STUDY : Breast Cancer Prediction
7 Lectures 01:08:14-
Intro and Business Challenge
00:04:19 -
General Overview on CLASSIFICATION Metrics
00:22:06 -
Data Import and Basic Data Clearning
00:07:09 -
Visualization, Feature Scaling and Encoding
00:10:15 -
Model Fitting and checking the Feature Importance
00:12:56 -
Balancing the Dataset and Feature Selection
00:10:17 -
Conclusion
00:01:12
-
-
REGRESSION CASE STUDY : Coal Production Estimation
7 Lectures 00:44:04-
Intro and Business Challenge
00:02:04 -
General Overview on Regression Metrics
00:11:06 -
Data Import and Some Basic Cleaning
00:04:05 -
Visualization and EDA
00:05:06 -
Feature Engineering
00:13:01 -
Model Building and Evaluation
00:07:37 -
Conclusion
00:01:05
-
-
CLASSIFICATION CASE STUDY : Heart Diseases Prediction
9 Lectures 00:56:35-
Intro and Business Challenge
00:03:10 -
General Overview on CLASSIFICATION Metrics
00:22:06 -
Data import and Basic Data Cleaning
00:04:11 -
Visualization and EDA
00:08:01 -
Feature Engineering
00:03:20 -
Model Building and Evaluation
00:06:33 -
Some Bug Fixes
00:03:28 -
Balancing the Dataset and Refitting the Models
00:04:42 -
Conclusion
00:01:04
-
-
CLASSIFICATION CASE STUDY : Predict whether a Customer Shall Sign a Loan or Not
7 Lectures 00:53:24-
Intro and Business Challenge
00:04:52 -
General Overview on CLASSIFICATION Metrics
00:22:06 -
Data Import
00:04:27 -
Basic Feature Engineering and Visualization
00:07:50 -
Feature Engineering ( Part 2 )
00:04:12 -
Model Prediction and Evaluation
00:09:11 -
Conclusion
00:00:46
-
-
REGRESSION CASE STUDY : Player Salary Prediction
11 Lectures 00:49:15-
Intro and Business Challenge
00:02:29 -
General Overview on Regression Metrics
00:11:06 -
Data Import
00:01:56 -
Feature Engineering and visualization ( Part 1 )
00:04:09 -
Feature Engineering and visualization ( Part 2 )
00:08:00 -
Outlier Detection and Removal
00:03:56 -
Feature Scaling
00:03:47 -
Feature Encoding
00:03:41 -
Model Fitting and Evaluation
00:06:34 -
Suggestion to Improve this model
00:02:25 -
Conclusion
00:01:12
-
-
Conclusion
1 Lectures 00:00:27-
Conclusion
00:00:27
-

Akshay Deep Lamba
Hustler
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 I teach.
I am looking forward to sharing my passion and knowledge with you!
Looking forward to working together!