Machine Learning Practical: 6 Real-World Applications
4.3 (1,483 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
12,151 students enrolled

Machine Learning Practical: 6 Real-World Applications

Machine Learning - Get Your Hands Dirty by Solving Real Industry Challenges with Python
4.3 (1,487 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
12,151 students enrolled
Last updated 5/2020
English
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Current price: $139.99 Original price: $199.99 Discount: 30% off
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This course includes
  • 8.5 hours on-demand video
  • 3 articles
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll 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
Course content
Expand all 82 lectures 08:38:00
+ Introduction
3 lectures 02:32
BONUS: Learning Paths
00:51
Where to get the materials
00:02
+ Breast Cancer Classification
9 lectures 01:11:59
Updates on Udemy Reviews
01:09
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 01:21:56
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 01:18:30
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 01:38:36
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 01:45:33

Section will be published sooner than you expect!

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 01:18:26
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 bonus video
02:40
+ Bonus Lectures
1 lecture 00:28
***YOUR SPECIAL BONUS***
00:28
Requirements
  • You need to know Python (Machine Learning A-Z level is enough) in order to complete this course.
  • You need to know how to set up your working environment (Anaconda, Jupyter Notebook, Spyder)
  • This should not be your first Machine Learning course. You need to understand main concepts.
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 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.

Who this course is for:
  • Data Science and Machine Learning enthusiasts who want to understand how real data science projects look like.
  • Anyone with Machine Learning and Python knowledge who wants to practice their skills