Setup Menus in Admin Panel

Course Details

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.

Course Curriculum

1.1 Welcome to the course!
1.1 Welcome to the course! 00:00:00
1.2 Where to get the materials
1.2 Where to get the materials 00:00:00
2.1 Introduction
2.1 Introduction 00:00:00
2.2 Business Challenge
2.2 Business Challenge 00:00:00
2.3 Challenge in Machine Learning Vocabulary
2.3 Challenge in Machine Learning Vocabulary 00:00:00
2.4 Data Visualisation
2.4 Data Visualisation 00:00:00
2.5 Model Training
2.5 Model Training 00:00:00
2.6 Model Evaluation
2.6 Model Evaluation 00:00:00
2.7 Improving the Model
2.7 Improving the Model 00:00:00
2.8 Conclusion
2.8 Conclusion 00:00:00
3.1 Business Challenge
3.1 Business Challenge 00:00:00
3.2 Challenge in Machine Learning Vocabulary
3.2 Challenge in Machine Learning Vocabulary 00:00:00
3.3 Data Visualisation
3.3 Data Visualisation 00:00:00
3.4 Model Training Part I
3.4 Model Training Part I 00:00:00
3.5 Model Training Part II
3.5 Model Training Part II 00:00:00
3.6 Model Training Part III
3.6 Model Training Part III 00:00:00
3.7 Model Training Part IV
3.7 Model Training Part IV 00:00:00
3.8 Model Evaluation
3.8 Model Evaluation 00:00:00
3.9 Improving the Model
3.9 Improving the Model 00:00:00
3.10 Conclusion
3.10 Conclusion 00:00:00
4.1 Fintech Case Studies Introduction
4.1 Fintech Case Studies Introduction 00:00:00
4.2 Introduction
4.2 Introduction 00:00:00
4.3 Data
4.3 Data 00:00:00
4.4 Features Histograms
4.4 Features Histograms 00:00:00
4.5 Correlation Plot
4.5 Correlation Plot 00:00:00
4.6 Correlation Matrix
4.6 Correlation Matrix 00:00:00
4.7 Feature Engineering - Response
4.7 Feature Engineering – Response 00:00:00
4.8 Feature Engineering - Screens
4.8 Feature Engineering – Screens 00:00:00
4.9 Data Pre-Processing
4.9 Data Pre-Processing 00:00:00
4.10 Model Building
4.10 Model Building 00:00:00
4.11 Model Conclusion
4.11 Model Conclusion 00:00:00
4.12 Final Remarks
4.12 Final Remarks 00:00:00
5.1 Introduction
5.1 Introduction 00:00:00
5.2 Data
5.2 Data 00:00:00
5.3 Data Cleaning
5.3 Data Cleaning 00:00:00
5.4 Features Histograms
5.4 Features Histograms 00:00:00
5.5 Pie Chart Distributions
5.5 Pie Chart Distributions 00:00:00
5.6 Correlation Plot
5.6 Correlation Plot 00:00:00
5.7 Correlation Matrix
5.7 Correlation Matrix 00:00:00
5.8 One-Hot Encoding
5.8 One-Hot Encoding 00:00:00
5.9 Feature Scaling & Balancing
5.9 Feature Scaling & Balancing 00:00:00
5.10 Model Building
5.10 Model Building 00:00:00
5.11 K-Fold Cross Validation
5.11 K-Fold Cross Validation 00:00:00
5.12 Feature Selection
5.12 Feature Selection 00:00:00
5.13 Model Conclusion
5.13 Model Conclusion 00:00:00
5.14 Final Remarks
5.14 Final Remarks 00:00:00
6.1 Introduction
6.1 Introduction 00:00:00
6.2 Data
6.2 Data 00:00:00
6.3 Data Housekeeping
6.3 Data Housekeeping 00:00:00
6.4 Histograms
6.4 Histograms 00:00:00
6.5 Correlation Plot
6.5 Correlation Plot 00:00:00
6.6 Correlation Matrix
6.6 Correlation Matrix 00:00:00
6.7 Feature Engineering
6.7 Feature Engineering 00:00:00
6.8 Data Preprocessing
6.8 Data Preprocessing 00:00:00
6.9 Model Building Part 1
6.9 Model Building Part 1 00:00:00
6.10 Model Building Part 2
6.10 Model Building Part 2 00:00:00
6.11 Grid Search Part 1
6.11 Grid Search Part 1 00:00:00
6.12 Grid Search Part 2
6.12 Grid Search Part 2 00:00:00
6.13 Model Conclusion
6.13 Model Conclusion 00:00:00
6.14 Final Remarks
6.14 Final Remarks 00:00:00
7.1 Case Study
7.1 Case Study 00:00:00
7.2 Machine Learning Vocabulary
7.2 Machine Learning Vocabulary 00:00:00
7.3 Set Up
7.3 Set Up 00:00:00
7.4 Data Visualization
7.4 Data Visualization 00:00:00
7.5 Data Preprocessing
7.5 Data Preprocessing 00:00:00
7.6 Deep Learning Part 1
7.6 Deep Learning Part 1 00:00:00
7.7 Deep Learning Part 2
7.7 Deep Learning Part 2 00:00:00
7.8 Splitting the Data
7.8 Splitting the Data 00:00:00
7.9 Training
7.9 Training 00:00:00
7.10 Metrics
7.10 Metrics 00:00:00
7.11 Confusion Matrix
7.11 Confusion Matrix 00:00:00
7.12 Machine Learning Classifiers
7.12 Machine Learning Classifiers 00:00:00
7.13 Random Forest
7.13 Random Forest 00:00:00
7.14 Decision Trees
7.14 Decision Trees 00:00:00
7.15 Sampling
7.15 Sampling 00:00:00
7.16 Undersampling
7.16 Undersampling 00:00:00
7.17 Smote
7.17 Smote 00:00:00
7.18 Final remarks
7.18 Final remarks 00:00:00

Course Reviews

N.A

ratings
  • 5 stars0
  • 4 stars0
  • 3 stars0
  • 2 stars0
  • 1 stars0

No Reviews found for this course.

PRIVATE COURSE
  • PRIVATE
  • 1 week, 3 days
0 STUDENTS ENROLLED
    Copyright @2019