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650 reviews on Udemy

Machine Learning Practical: 6 Real-World Applications

Machine Learning - Get Your Hands Dirty by Solving Real Industry Challenges with Python
Instructor:
Kirill Eremenko
6,037 students enrolled
English [Auto-generated]
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

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 as a final bonus:

 

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.

Introduction

1
Welcome to the course!
2
BONUS: Learning Paths
3
Where to get the materials

Breast Cancer Classification

1
Introduction
2
Business Challenge
3
Updates on Udemy Reviews
4
Challenge in Machine Learning Vocabulary
5
Data Visualisation
6
Model Training
7
Model Evaluation
8
Improving the Model
9
Conclusion

Fashion Class Classification

1
Business Challenge
2
Challenge in Machine Learning Vocabulary
3
Data Visualisation
4
Model Training Part I
5
Model Training Part II
6
Model Training Part III
7
Model Training Part IV
8
Model Evaluation
9
Improving the Model
10
Conclusion

Directing Customers to Subscription Through App Behavior Analysis

1
Fintech Case Studies Introduction
2
Introduction
3
Data
4
Features Histograms
5
Correlation Plot
6
Correlation Matrix
7
Feature Engineering - Response
8
Feature Engineering - Screens
9
Data Pre-Processing
10
Model Building
11
Model Conclusion
12
Final Remarks

Minimizing Churn Rate Through Analysis of Financial Habits

1
Introduction
2
Data
3
Data Cleaning
4
Features Histograms
5
Pie Chart Distributions
6
Correlation Plot
7
Correlation Matrix
8
One-Hot Encoding
9
Feature Scaling & Balancing
10
Model Building
11
K-Fold Cross Validation
12
Feature Selection
13
Model Conclusion
14
Final Remarks

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

1
Introduction

Section will be published sooner than you expect!

2
Data
3
Data Housekeeping
4
Histograms
5
Correlation Plot
6
Correlation Matrix
7
Feature Engineering
8
Data Preprocessing
9
Model Building Part 1
10
Model Building Part 2
11
Grid Search Part 1
12
Grid Search Part 2
13
Model Conclusion
14
Final Remarks

Credit Card Fraud Detection

1
Case Study
2
Machine Learning Vocabulary
3
Set Up
4
Data Visualization
5
Data Preprocessing
6
Deep Learning Part 1
7
Deep Learning Part 2
8
Splitting the Data
9
Training
10
Metrics
11
Confusion Matrix
12
Machine Learning Classifiers
13
Random Forest
14
Decision Trees
15
Sampling
16
Undersampling
17
Smote
18
Final remarks
19
THANK YOU bonus video
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9 hours on-demand video
2 articles
Full lifetime access
Access on mobile and TV
Certificate of Completion