The Machine Learning Process A-Z

with Ken Jee and Jeff Li

There is so much more to data science than just model tuning. This course covers the entire end-to-end machine learning process—from scoping the problem all the way to its productionization.

6 hours 145 lessons
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145 High Quality Lessons
0 Practical Tasks
6 Hours of Content
Certificate of Achievement

Course Overview

Data science education focuses too much on the algorithm itself. In reality, we can have only four lines of code and use them for a variety of problems. The heavy lift of an ML model is the end-to-end process. Jeff Li and Ken Jee walk you step-by-step through this process, so you can successfully take your next project from start to finish. You will learn everything you need to know to set your projects up for success. The Machine Learning Process A-Z course gives you a deep understanding of what machine learning really is. It helps you understand when you should and shouldn’t use this powerful tool. Jeff and Ken break down the specifics of the different problems you can encounter and how machine learning is used in specific domains. In the second part of the course, you will learn the entire modeling process. Jeff and Ken show you how to pull real results and make the ML model work for others, not just yourself. You will learn how to perform essential steps like data preprocessing. In this section, they also show you how to deal with null values and outliers. Next, you’ll see how to explore your data to frame your analysis. Additionally, the course deals with some of the visualization techniques that can help you to see the relationships in your data. After that, we go into feature engineering—one of the most important steps for improving your model’s results. That leads to cross-validation and how to handle bias and variance trade-off in your analysis. Finally, the instructors touch briefly on the model tuning process and how to productionize your work and documentation.

Topics covered

Cross ValidationData modelingdata preprocessingDealing with Imbalanced DataExploratory Data AnalysisFeature Engineeringmachine learningMachine Learning ProcessModel Evaluation

What You'll Learn

Many courses jump right into the algorithms, but in this one, Jeff and Ken teach you the real skills needed to leverage machine learning into actual results. You will learn how to perform data preprocessing, engineer your model’s features, and finally productionize your project.

Focus on the end-to-end ML process
Data preprocessing techniques
Data exploration and analysis framing
Feature engineering
Model tuning and productionization


Student feedback


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This course literarily saves me dozens of hours of time from watching various YouTube videos and looking up codes on the Internet. You’d find this course useful whether you’re doing your first portfolio project, or you’re a working professional. The companion notebooks and GitHub repo are golden! In the past, it took me dozens of hours to google the exact same thing on specific codes. Jeff and Ken did most of the heavy lifting for you, so that you can focus on working on your projects. Most importantly, Jeff has worked in well-known and reputable companies. So you know that his content and material are trustworthy. Though there’re a lot of “Kaggle Champions” on YouTube giving a walk-through on an ML project, I value that Jeff and Ken’s teaching a lot because of his years of experience in major tech companies. This course stands out to me because it talks about the end-to-end process of building ML models. This is a topic that even some bootcamps don’t talk about it a lot. They either don’t spend much time to talk about it thoroughly, or they gloss over it, or they assume that “you would figure it out” along the way. So this course solves that problem to give you a walk through on that process. You would notice that each lecture video’s length is about several minute long to keep it bite-sized. And the coding notebook walkthrough videos are also thorough to cover the lecture video’s portions. So you’ll get both the theoretical knowledge and practical experience in this case. After watching this course, you should be able to do a Machine Learning project from start to finish, end-to-end all by yourself. I look forward to the Part 2 of the course – Machine Learning Algorithms with Jeff Li and Ken Jee.
I wanted to provide feedback on the course I recently completed. Overall, I found the course to be good and informative, but I had difficulties completing it due to my lack of understanding of the material. I found that Jeffrey Li's teaching style was not effective for me as he moved quickly through the coding portion without providing sufficient explanation. On the other hand, I appreciated Ken Jee's teaching style as his explanations were clear and helped me gain a full understanding of the material. I believe that Jeffrey may be a better instructor for others, but for my learning style, Ken was the more effective teacher. Thank you for the opportunity to provide feedback and I hope that this information can be used to improve the course for future students.
This course was organized, well laid out and well constructed. Ken and Jeff did a great job explaining some very complicated material and making it uncomplicated. I really appreciated the size of the lectures as it keeps you interested and engaged. They were able to take some complicated functions and explain them in laymen's terms to make them understandable. I am really happy I purchased this course and would highly recommend it to anyone who wants to get into ML. Thank you very much, really appreciate you taking the time and effort in putting together this course.
The course was informative. As someone who went through a Data Science bootcamp, I still was able to leave this course learning new techniques. The course could use some cleaning up with the order and the notebooks (which I'm not sure was updated since I started, stopped and pick back up the course a few weeks later) but I adjusted accordingly. They appropriate chose the level in which to jump into this course as it is not necessarily for beginners. I'd rate it a 5/5. I look forward to more courses from Jeff and Ken in the future.
This course is mostly good. The review of each Collab notebook after the videos in each section is very good. One criticism: it seems as if the creators assume the viewer has certain knowledge of some things, thereby never clarifying on some things (for example, using certain terms but never telling its definition). This course of bite-sized videos can be used as a glossary for personal projects you're doing or a companion to other ML courses that dive deeper into certain subjects.
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Ken Jee

“Companies don’t pay data scientists all this money to just write "" and "model.predict()". Having a thorough end-to-end process is how data scientists create business value for their employers. This is also how a data scientist levels up to a senior and eventually principal DS.”

Ken Jee

220K Subscribers on Youtube

The Machine Learning Process A-Z

with Ken Jee and Jeff Li

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