Machine Learning Practical: 6 Real-World Applications

Machine Learning - Get Your Hands Dirty by Solving Real Industry Challenges with Python

4.55 (2945 reviews)
Udemy
platform
English
language
Data Science
category
instructor
22,343
students
8.5 hours
content
Mar 2024
last update
$79.99
regular price

What you will learn

You will know how real data science project looks like

You will be able to include these Case Studies in your resume

You will be able better market yourself as a Machine Learning Practioneer

You will feel confident during Data Science interview

You will learn how to chain multiple ML algorithms together to achieve the goal

You will learn most advanced Data Visualization techniques with Seaborn and Matplotlib

You will learn Logistic Regression

You will learn L1 Regularization (Lasso)

You will learn Random Forest Classifier

Description

So you know the theory of Machine Learning and know how to create your first algorithms. Now what? 

There are tons of courses out there about the underlying theory of Machine Learning which don’t go any deeper – into the applications.


This course is not one of them.

Are you ready to apply all of the theory and knowledge to real life Machine Learning challenges?  

Then welcome to “Machine Learning Practical”.


We gathered best industry professionals with tons of completed projects behind.

Each presenter has a unique style, which is determined by his experience, and like in a real world, you will need adjust to it if you want successfully complete this course. We will leave no one behind!


This course will demystify how real Data Science project looks like. Time to move away from these polished examples which are only introducing you to the matter, but not giving any real experience.


If you are still dreaming where to learn Machine Learning through practice, where to take real-life projects for your CV, how to not look like a noob in the recruiter's eyes, then you came to the right place!


This course provides a hands-on approach to real-life challenges and covers exactly what you need to succeed in the real world of Data Science.

 

There are most exciting case studies including:

●      diagnosing diabetes in the early stages

●      directing customers to subscription products with app usage analysis

●      minimizing churn rate in finance

●      predicting customer location with GPS data

●      forecasting future currency exchange rates

●      classifying fashion

●      predicting breast cancer

●      and much more!

 

All real.

All true.

All helpful and applicable.

And another extra:

 

In this course we will also cover Deep Learning Techniques and their practical applications.

So as you can see, our goal here is to really build the World’s leading practical machine learning course.

If your goal is to become a Machine Learning expert, you know how valuable these real-life examples really are. 

They will determine the difference between Data Scientists who just know the theory and Machine Learning experts who have gotten their hands dirty.

So if you want to get hands-on experience which you can add to your portfolio, then this course is for you.

Enroll now and we’ll see you inside.

Content

Introduction

Welcome to the course!
BONUS: Learning Paths
Where to get the materials

Breast Cancer Classification

Introduction
Business Challenge
Updates on Udemy Reviews
Challenge in Machine Learning Vocabulary
Data Visualisation
Model Training
Model Evaluation
Improving the Model
Conclusion

Fashion Class Classification

Business Challenge
Challenge in Machine Learning Vocabulary
Data Visualisation
Model Training Part I
Model Training Part II
Model Training Part III
Model Training Part IV
Model Evaluation
Improving the Model
Conclusion

Directing Customers to Subscription Through App Behavior Analysis

Fintech Case Studies Introduction
Introduction
Data
Features Histograms
Correlation Plot
Correlation Matrix
Feature Engineering - Response
Feature Engineering - Screens
Data Pre-Processing
Model Building
Model Conclusion
Final Remarks

Minimizing Churn Rate Through Analysis of Financial Habits

Introduction
Data
Data Cleaning
Features Histograms
Pie Chart Distributions
Correlation Plot
Correlation Matrix
One-Hot Encoding
Feature Scaling & Balancing
Model Building
K-Fold Cross Validation
Feature Selection
Model Conclusion
Final Remarks

Predicting the Likelihood of E-Signing a Loan Based on Financial History

Introduction
Data
Data Housekeeping
Histograms
Correlation Plot
Correlation Matrix
Feature Engineering
Data Preprocessing
Model Building Part 1
Model Building Part 2
Grid Search Part 1
Grid Search Part 2
Model Conclusion
Final Remarks

Credit Card Fraud Detection

Case Study
Machine Learning Vocabulary
Set Up
Data Visualization
Data Preprocessing
Deep Learning Part 1
Deep Learning Part 2
Splitting the Data
Training
Metrics
Confusion Matrix
Machine Learning Classifiers
Random Forest
Decision Trees
Sampling
Undersampling
Smote
Final remarks
THANK YOU bonus video

Bonus Lectures

***YOUR SPECIAL BONUS***

Screenshots

Machine Learning Practical: 6 Real-World Applications - Screenshot_01Machine Learning Practical: 6 Real-World Applications - Screenshot_02Machine Learning Practical: 6 Real-World Applications - Screenshot_03Machine Learning Practical: 6 Real-World Applications - Screenshot_04

Reviews

Mallikarjuna
June 20, 2023
functions were not explained in detail: for example, for the function below what are the components c, gamma and kernel, why they are used? param_grid = {'C': [.1,1,10,100], 'gamma': [1,.1,.01,.001], 'kernel': ['rbf']}
Jeremy
January 4, 2023
i am going through this section to gain a better understanding - as i will be taking part in kaggle's competition
EDWALD
December 20, 2022
This course exposes to real-life case studies such as Breast Cancer. It also explains how Machine Learning is being used in life and indeed lives up to the name of the course! Thank you Ryan, Ligency 1, Ligency and Rony!
Anna
December 15, 2022
Really great course for people who already had begun their journey with ML but want to expand their horizons
Jainam
November 29, 2022
Lot's of errors and confusion. it is not a good course for someone who is starting to learn machine learning.
Rico
September 11, 2022
Based on section 2: Confusion matrix is wrongly interpreted, the confusion matrix needs to be transposed in order to match the position of the type I and II error. Furthermore they don't explain that you need to take care of multicollinearity and use cross validation in order to check the robustness of the model.
Serdar
August 22, 2022
only 1 part is recorded in the last months. This is crazy. You will get a lot of errors wwwwwhile trying to run the code!!!
David
August 1, 2022
The instructors need to accurately state their interpretations of the results - without this the examples are incomplete.
Ziyou
July 17, 2022
Very useful case studies for understanding those abstract knowledge! High recommend to dive in for ML beginners!! Thank you for who make the class!
Alexandre
June 3, 2022
Teaches a lot about "what" and "how" to do things with practical examples. I felt it was lacking about the "why" they did things certain way.
Anagha
May 25, 2022
Python codes need to be changed. Coded exactly the same as the tutor taught still getting error as there's no such command available here!!!
Marcelo
May 2, 2022
Some codes need to be updated. Also could rename the course to 6 Real-World Classification Applications.
Sebastian
May 1, 2022
the teacher has an awesome way of explaining even complex theories in a very easy but professional way!!! one of the best on udemy that i have seen so far!
Furkan
April 6, 2022
I give 3 star.Why Because of between Section-2 and Section 5 courses have different instructor and SOUND QUALİTY TOO TOO BAD.Instructor change after by after.Instructor changes from time to time.And a few videos is not fully related each other.
Alina
April 5, 2022
This was a super full course and great explanations. As a beginner in ML, this was very easy to follow and understand.

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1879510
udemy ID
8/27/2018
course created date
8/7/2019
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