Machine Learning Practical: 6 Real-World Applications

Machine Learning Practical: 6 Real-World Applications

Machine Learning - Get Your Hands Dirty by Solving Real Industry Challenges with Python

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

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 is the target audience?

• 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

Course content

Introduction

• ·         Welcome to the course! - 01:38
• ·         Where to get the materials - 00:02

Breast Cancer Classification

• ·         Introduction - 00:45
• ·         Business Challenge - 02:50
• ·         Updates on Udemy Reviews - 02:42
• ·         Challenge in Machine Learning Vocabulary - 07:14
• ·         Data Visualization - 16:57
• ·         Model Training - 08:06
• ·         Model Evaluation - 10:13
• ·         Improving the Model - 21:59
• ·         Conclusion - 02:46

Fashion Class Classification

• ·         Business Challenge - 04:39
• ·         Challenge in Machine Learning Vocabulary - 06:09
• ·         Data Visualization - 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

• ·         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

• ·         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

• ·         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

• ·         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
• ·         Under sampling - 05:15
• ·         Smote - 03:44
• ·         Final remarks - 03:00

About the instructors

Kirill Eremenko

Data Scientist

My name is Kirill Eremenko and I am super-psyched that you are reading this!

Professionally, I am a Data Science management consultant with over five years of experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and today I leverage Big Data to drive business strategy, revamp customer experience and revolutionize existing operational processes.

From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. I am also passionate about public speaking, and regularly present on Big Data at leading Australian universities and industry events.

To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you!

Hadelin de Ponteves

AI Entrepreneur

Hi. My name is Hadelin de Ponteves. Always eager to learn, I invested a lot of my time in learning and teaching, covering a wide range of different scientific topics. 

Today I am passionate about Machine Learning, Deep Learning and Artificial Intelligence. I will do my very best to convey my passion for AI to you. I have gained diverse experience in this field. I have an Engineering master's degree with a specialization in Data Science. I spent one year doing research in Machine Learning, working on innovative and exciting projects. Then a work experience at Google where I implemented some Machine Learning models for business analytics. 

Eventually, I realized I spent most of my time doing analysis and I gradually needed to feed my creativity so I became an entrepreneur. My courses will combine the two dimensions of analysis and creativity, allowing you to learn all the analytic skills required in Data Science, by applying them on creative ideas.

Looking forward to working together!

Dr. Ryan Ahmed, Ph.D., MBA

Professor and Best-selling Udemy Instructor

Ryan Ahmed is a best-selling Udemy instructor who is passionate about education and technology. Ryan's mission is to make quality education accessible and affordable to everyone. Ryan holds a Ph.D. degree in Engineering from McMaster* University, with focus on Mechatronics and Electric Vehicle (EV) control. He also received a Master’s of Applied Science degree from McMaster, with focus on Artificial Intelligence (AI) and an MBA in Finance from the DeGroote School of Business. 

Ryan held several engineering positions at Fortune 100 companies globally such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada. Ryan has taught several courses on Engineering, Science, Technology and Mathematics to over 15,000+ students globally. He has over 20 published journal and conference research papers on AI, Machine learning and EV controls. He is the recipient of the best paper award at the IEEE Transportation Electrification Conference and Expo (iTEC 2012) in Detroit, MI, USA. 

Ryan is a Stanford Certified Project Manager (SCPM), certified Professional Engineer (P.Eng.) in Ontario, a member of the Society of Automotive Engineers (SAE), and a member of the Institute of Electrical and Electronics Engineers (IEEE). He is also the program Co-Chair at the 2017 IEEE Transportation and Electrification Conference (iTEC’17) in Chicago, IL, USA.

* McMaster University is one of only four Canadian universities consistently ranked in the top 100 in the world.

SuperDataScience Team

Helping Data Scientists Succeed

Hi there,

We are the SuperDataScience Social team. You will be hearing from us when new SDS courses are released, when we publish new podcasts, blogs, share cheat sheets and more!

We are here to help you stay on the cutting edge of Data Science and Technology. 

See you in class,

Sincerely,

The Real People at SuperDataScience

Rony Sulca

Data Scientist at MoneyLion

I am a Data Scientist by trade, Astrophysicist by Degree, and Teacher by Heart. 

Before becoming a Data Scientist, I realized that data is everywhere, taking over every aspect of my life. In my roles as an Undergraduate Research Assistant and a Data Analyst, I was exposed to working with large datasets, ranging from YouTube Analytics to Galaxy Cluster Pressure Profiles. Having the opportunity to work with data about a platform (YouTube) that I really love helped me love my work even more. I was able to carry this passion into MoneyLion, where I truly began to dive myself into the nitty gritty of Data Science, working with Database technologies like MongoDB and Redshift, developing efficient data warehouse ETL processes using Python and R, building predictive models for decision-making, building and maintaining R packages, building dashboards to track statistical significance of marketing campaigns, and manipulating data with a variety of tools like ElasticSearch, DataGrip, and more.

As a kid, immigrating to the US in 2005, I was very impressionable and excited about new things I learned. I spent a tremendous amount of time researching and watching videos about science and our universe. This passion for learning made me excited about explaining what I knew to others. As Neil deGrasse Tyson once explained, when I learned something new about science I felt like asking the person next to me, “Have you heard this!?”.

Now, I prepare to make my way into this new world of teaching Data Science. Whether it is teaching to my peers or friends, or fulfilling my dream of teaching an Online Course, I will continue to share the wonders of Data Science to all willing to listen. At the same time, I will keep diving deeper into this complex world of Data, learning as much as I can!

If you are looking for a Data Aficionado, a project partner, an employee, a friend, or just a helping hand, feel free to message!

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https://www.udemy.com/machine-learning-practical/