Mô tả

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.

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Yêu cầu

Nội dung khoá học

8 sections

Introduction

4 lectures
Welcome to the course!
01:38
Learning Paths
00:33
Where to get the materials
00:02
Study Tips For Success
00:46

Breast Cancer Classification

8 lectures
Introduction
00:45
Business Challenge
02:50
Challenge in Machine Learning Vocabulary
07:14
Data Visualisation
16:57
Model Training
08:06
Model Evaluation
10:13
Improving the Model
21:59
Conclusion
02:46

Fashion Class Classification

10 lectures
Business Challenge
04:39
Challenge in Machine Learning Vocabulary
06:09
Data Visualisation
15:24
Model Training Part I
08:05
Model Training Part II
07:05
Model Training Part III
09:58
Model Training Part IV
15:15
Model Evaluation
09:00
Improving the Model
02:35
Conclusion
03:46

Directing Customers to Subscription Through App Behavior Analysis

12 lectures
Fintech Case Studies Introduction
01:42
Introduction
02:13
Data
03:53
Features Histograms
09:46
Correlation Plot
05:17
Correlation Matrix
07:02
Feature Engineering - Response
09:17
Feature Engineering - Screens
09:58
Data Pre-Processing
10:21
Model Building
12:53
Model Conclusion
03:59
Final Remarks
02:09

Minimizing Churn Rate Through Analysis of Financial Habits

14 lectures
Introduction
02:13
Data
08:16
Data Cleaning
04:59
Features Histograms
09:20
Pie Chart Distributions
09:57
Correlation Plot
08:14
Correlation Matrix
09:29
One-Hot Encoding
06:25
Feature Scaling & Balancing
11:08
Model Building
08:26
K-Fold Cross Validation
04:44
Feature Selection
07:54
Model Conclusion
04:48
Final Remarks
02:43

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

14 lectures
Introduction
07:48
Data
08:11
Data Housekeeping
05:34
Histograms
10:08
Correlation Plot
05:17
Correlation Matrix
07:04
Feature Engineering
05:11
Data Preprocessing
09:48
Model Building Part 1
07:29
Model Building Part 2
10:11
Grid Search Part 1
12:25
Grid Search Part 2
09:50
Model Conclusion
03:06
Final Remarks
03:31

Credit Card Fraud Detection

19 lectures
Case Study
03:30
Machine Learning Vocabulary
03:15
Set Up
03:07
Data Visualization
03:17
Data Preprocessing
04:21
Deep Learning Part 1
03:56
Deep Learning Part 2
07:23
Splitting the Data
06:05
Training
02:52
Metrics
03:59
Confusion Matrix
05:29
Machine Learning Classifiers
07:42
Random Forest
03:45
Decision Trees
02:51
Sampling
02:15
Undersampling
05:15
Smote
03:44
Final remarks
03:00
THANK YOU Video
02:40

Special Offer

1 lectures
***YOUR SPECIAL BONUS***
00:29

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