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30-Day Money-Back Guarantee
Development Data Science Machine Learning

Machine Learning Practical: 6 Real-World Applications

Machine Learning - Get Your Hands Dirty by Solving Real Industry Challenges with Python
Rating: 4.3 out of 54.3 (2,054 ratings)
15,821 students
Created by Dr. Ryan Ahmed, Ph.D., MBA, Rony Sulca, Ligency Team
Last updated 4/2021
English
English [Auto]
30-Day Money-Back Guarantee

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
Curated for the Udemy for Business collection

Course content

8 sections • 82 lectures • 8h 37m total length

  • Preview01:38
  • BONUS: Learning Paths
    00:33
  • Where to get the materials
    00:02

  • Preview00:45
  • Preview02:50
  • Updates on Udemy Reviews
    01:09
  • Preview07:14
  • Data Visualisation
    16:57
  • Model Training
    08:06
  • Model Evaluation
    10:13
  • Improving the Model
    21:59
  • Conclusion
    02:46

  • Preview04:39
  • 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

  • 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

  • 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

  • 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

  • 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

  • ***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

Featured review

Elemento 24
Elemento 24
22 courses
18 reviews
Rating: 5.0 out of 52 months ago
The course indeed lived up to it what it said in the beginning. The course exposes oneself to the various real-life applications of Machine Learning and how ML is exploited in the various fields of life.

Instructors

Dr. Ryan Ahmed, Ph.D., MBA
Professor & Best-selling Udemy Instructor, 200K+ students
Dr. Ryan Ahmed, Ph.D., MBA
  • 4.5 Instructor Rating
  • 19,450 Reviews
  • 225,970 Students
  • 28 Courses

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 Mechanical 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 fault detection and an MBA in Finance from the DeGroote School of Business. 

Ryan held several engineering positions at Fortune 500 companies globally such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada. Ryan has taught several courses on Science, Technology, Engineering and Mathematics to over 200,000+ students globally. He has over 15 published journal and conference research papers on state estimation, AI, Machine learning, battery modeling and EV controls. He is the co-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.



Rony Sulca
Data Scientist at MoneyLion
Rony Sulca
  • 4.3 Instructor Rating
  • 2,054 Reviews
  • 15,821 Students
  • 1 Course

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!

Ligency Team
Helping Data Scientists Succeed
Ligency Team
  • 4.5 Instructor Rating
  • 483,019 Reviews
  • 1,735,395 Students
  • 112 Courses

Hi there,

We are the Ligency PR and Marketing team. You will be hearing from us when new courses are released, when we publish new podcasts, blogs, share cheatsheets 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 Ligency

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