Machine Learning Practical- 6 Real-World Applications

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:

鈼?nbsp;     diagnosing diabetes in the early stages

鈼?nbsp;     directing customers to subscription products with app usage analysis

鈼?nbsp;     minimizing churn rate in finance

鈼?nbsp;     predicting customer location with GPS data

鈼?nbsp;     forecasting future currency exchange rates

鈼?nbsp;     classifying fashion

鈼?nbsp;     predicting breast cancer

鈼?nbsp;     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鈥檚 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鈥檒l see you inside.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome to the course!

Lecture 2: Learning Paths

Lecture 3: Where to get the materials

Chapter 2: Breast Cancer Classification

Lecture 1: Introduction

Lecture 2: Business Challenge

Lecture 3: Challenge in Machine Learning Vocabulary

Lecture 4: Data Visualisation

Lecture 5: Model Training

Lecture 6: Model Evaluation

Lecture 7: Improving the Model

Lecture 8: Conclusion

Chapter 3: Fashion Class Classification

Lecture 1: Business Challenge

Lecture 2: Challenge in Machine Learning Vocabulary

Lecture 3: Data Visualisation

Lecture 4: Model Training Part I

Lecture 5: Model Training Part II

Lecture 6: Model Training Part III

Lecture 7: Model Training Part IV

Lecture 8: Model Evaluation

Lecture 9: Improving the Model

Lecture 10: Conclusion

Chapter 4: Directing Customers to Subscription Through App Behavior Analysis

Lecture 1: Fintech Case Studies Introduction

Lecture 2: Introduction

Lecture 3: Data

Lecture 4: Features Histograms

Lecture 5: Correlation Plot

Lecture 6: Correlation Matrix

Lecture 7: Feature Engineering – Response

Lecture 8: Feature Engineering – Screens

Lecture 9: Data Pre-Processing

Lecture 10: Model Building

Lecture 11: Model Conclusion

Lecture 12: Final Remarks

Chapter 5: Minimizing Churn Rate Through Analysis of Financial Habits

Lecture 1: Introduction

Lecture 2: Data

Lecture 3: Data Cleaning

Lecture 4: Features Histograms

Lecture 5: Pie Chart Distributions

Lecture 6: Correlation Plot

Lecture 7: Correlation Matrix

Lecture 8: One-Hot Encoding

Lecture 9: Feature Scaling & Balancing

Lecture 10: Model Building

Lecture 11: K-Fold Cross Validation

Lecture 12: Feature Selection

Lecture 13: Model Conclusion

Lecture 14: Final Remarks

Chapter 6: Predicting the Likelihood of E-Signing a Loan Based on Financial History

Lecture 1: Introduction

Lecture 2: Data

Lecture 3: Data Housekeeping

Lecture 4: Histograms

Lecture 5: Correlation Plot

Lecture 6: Correlation Matrix

Lecture 7: Feature Engineering

Lecture 8: Data Preprocessing

Lecture 9: Model Building Part 1

Lecture 10: Model Building Part 2

Lecture 11: Grid Search Part 1

Lecture 12: Grid Search Part 2

Lecture 13: Model Conclusion

Lecture 14: Final Remarks

Chapter 7: Credit Card Fraud Detection

Lecture 1: Case Study

Lecture 2: Machine Learning Vocabulary

Lecture 3: Set Up

Lecture 4: Data Visualization

Lecture 5: Data Preprocessing

Lecture 6: Deep Learning Part 1

Lecture 7: Deep Learning Part 2

Lecture 8: Splitting the Data

Lecture 9: Training

Lecture 10: Metrics

Lecture 11: Confusion Matrix

Lecture 12: Machine Learning Classifiers

Lecture 13: Random Forest

Lecture 14: Decision Trees

Lecture 15: Sampling

Lecture 16: Undersampling

Lecture 17: Smote

Lecture 18: Final remarks

Lecture 19: THANK YOU Video

Chapter 8: Congratulations!! Dont forget your Prize 馃檪

Lecture 1: Bonus: How To UNLOCK Top Salaries (Live Training)

Instructors

SuperDataScience Team
Helping Data Scientists Succeed

Rony Sulca
Senior Product Analyst at Influur

Ligency Team
Helping Data Scientists Succeed

Rating Distribution

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!