Machine Learning Practical: 6 Real-World Applications (Udemy.com)
Machine Learning - Get Your Hands Dirty by Solving Real Industry Challenges with Python
Created by: SuperDataScience Team
Last updated June 2024
What you will 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
How it compares
Against the other 155 rated Udemy courses in Machine Learning on CourseDuck:
- Rating4.3 out of 5higher than 36% of them
- Reviews3,279#29 of 156 by number of reviews
- Length8.6 hourslonger than 58% of them
- Last updatedJune 2024more recently than 61% of them
Quality Score
Overall Score : 86 / 100
Course 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 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’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.
Instructor Details
- 4.3 Rating
3,279 Reviews
SuperDataScience Team
Hi there,
We are the SuperDataScience team. You will hear from us when new SuperDataScience 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,
SuperDataScience Team!
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Reviews
By Gregory smith on 6/23/2026
The current course (June 2026) is a good intro to these topics. However datasets links are currently broken meaning that either you can only follow along by yourself, or you must hunt the datasets online. In addition, some of the tools have been deprecated. These days you can always use LLM to find quick solutions, or generally look it up. But... if they do not fix the links and do not make a note or update the tutorials with notes about the changes in the use of some of the libraries used, maybe this course should not be available anymore.
By Wenyi Ooi on 12/5/2023
All 3 Lecturers gave amazing guides on ML from different perspectives and ways, the first with attractive case studies in medical field and shopping, the second with many analytical models demonstrated, the third and also the best for me, with in-depth explanation on ML neutal network mechanism. All exploiting Scikit library for ML and confusion matrix for precision analysis.
By Ben Beale on 4/11/2021
This course actually helped me a LOT with a big school project a couple years ago. I went on my way once I had gotten what I needed from it at the time, but I have slowly finished it in pieces since then. Thankfully, I’m comfortable enough with Python to where I only needed to review the data science concepts to continue after the time away.
By Vishesh Mittal on 1/24/2021
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.
By Jennifer Sunderlin on 11/14/2020
great
By Kevin Coughlan on 11/14/2020
section 2-3: code/results discrepancies. code not in .py form makes things messy for me as i want to use an IDE. lots of detail skimmed over and explanations are poor so far
rest: better
By Piotr Beke on 11/7/2020
a good example well explained inspiring to play with data and Jupyter
By Nurcholis Machfud on 11/7/2020
diharapkan demo menggunakan jupyter notebook.
By Geza Balaton on 10/25/2020
Thank you for the practical examples, I really enjoyed the course.
By Prasang Thakur on 10/15/2020
no good explanation to the approach used
By Sajith Charuvil Sadasivan on 10/11/2020
good course
By Arvind Singh Kandasi on 10/9/2020
beautifully explained









