I’m going through the Udemy course Complete Machine Learning and Data Science: Zero to Mastery and writing down my observations/lecture notes.

This is the eigth part of the blog post series.

TL;DR (A Review of The Complete Course)

The program is a praise-worthy introduction to data science and machine learning with Python. The instructors focus on practical skills and convey an enormous topic in a captivating and friendly way.
The course is light on math and statistics skills, and you will have to learn those on your own.

15. Storytelling + Communication: How to Present Your Work

This part of the Udemy course covers communication tips and sharing your work.

The most important question you can ask is: “Who is it for?”

Then you can ask: “What questions will they have?”, “What needs do they have?”, “What concerns can I address?”, “What do they need to know?”.

Have a conversation with the target audience.

Who is your target audience?
There are two main groups: people on your team (boss, team members, etc.) and people outside your organization (clients, customers, etc.).

Recognize that your work is not for everyone.

The instructor advises you to break down your long-term projects into smaller time frames. Smaller frames are easier to manage and to plan.

Ask yourself: “What did I work on today?“ (1-3 points).

  • What’s working?
  • What’s not working?
  • What could be improved?

“What I’m working on next?”

  • What’s my next course of action (based on the above)?
  • Why?
  • What’s holding me back?

Document those thoughts, so you can share them with your team.

Daniel also offers tips on what to do before you have a job with the Weekend Project Principle.

During the week, build up your skills by going through courses, books, or other resources. On the weekend, work on your projects.
For example, design a project which you can do in six weeks. Either work on something that interests you or work on a project that relates to the company you want to apply to.
If it doesn’t work out, you only lose six weeks.
Ideally, you have a story to tell and something to show.

Document your work. Write a blog.
Strive for progress instead of perfection.

When you communicate with others, remember that it might be evident to you, but not to others.

Instead of trying to guess what others need to know, ask.

“What story are you trying to tell?” Document your work and share it.

Opinion

I like the advice. It’s practical, and Daniel links to some other great resources.

I also appreciate that the course offers a section on communication skills at all. I’m sure that many other courses skip it.

16. Career Advice + Extra Bits

This part covers various topics like learning how to learn, how to use Git, and how to contribute to open-source.

The instructors have some open-source projects on Github where you can make your first contribution.

17. + 18. Learn Python

The section covers Python fundamentals. I skipped these as I’m comfortable with the programming language.

Final Sections

Now we wrap up odds and ends, for example, some extra resources to learn math or how to continue your learning journey.

Recap of the Course

Overall the course strikes me as an exemplary entry-level course for data science and machine learning.

The instructors are friendly, engaging, and knowledgeable. They can explain the concepts to beginners.
I especially appreciate the 6 Step Framework for Approaching Machine Learning Projects that guides you through each machine learning project.

I had no prior knowledge of data science and machine learning. I now feel confident that I know the basics of machine learning and how to work with the most important Python libraries and tools.

Both teachers bring an encouraging atmosphere to the online learning experience. They take care to make the class accessible to beginner programmers.

The course focusses on a practical approach. You’ll quickly get your hands dirty.

I would have liked a more thorough exploration of the underlying mechanisms behind the machine learning models. There are no explanations on the theory behind the models.
You will have to fill that gap yourself. The instructors provide links to more resources, but it’s on you to follow up on that.

As a bonus, I would appreciate a simple guide on how to deploy a machine learning model.

The “Zero to Mastery” brand comes with a massive online Discord community. The Discord channels are busy, and you can discuss the course there. Both instructors are very active in the community, and they will promptly answer questions.

All in all, I thoroughly enjoyed the class. The instructors make the topics fun and provide a magnificent gateway to data science and machine learning with Python.


Go to the other parts of the series: