After Specialisation :: suggestions

Hello, I’ve almost completed the specialization. It remains only the last programming assignment, I am working on it.

My feeling is that I am not able to master by myself what I learnt. Using these notions in practise is really different from passing the quiz or the assignments.

  1. Does my feeling make sense ? or these basics learnt are sufficient to go ahead and is reasonable to go deeper when I am on one specific project ?

I feel myself less confident on the last part, probability and inference theory.
I studied it more than 20 years ago, without having the opportunity to apply what I’ve learnt. So I forgot a lot of parts. I worked ad software engineer.
In fact, I tried to re-study using book from university “Introduction to the Theory of Statistics, 3rd Edition - Softcover Alexander M. Mood; Franklin A. Graybill; Duane C. Boes” but especially for the second parts I found myself in trouble: I lost some maths and skill to read maths formalisms (way of write concepts ).

  1. Do you think I can find an easier book without loosing too much value ? Does this math level is really needed for working as Machine learning engineer ?

3. As you probably understood, I am trying to switch to different role, from software engineer field to AI/ML engineer (In reality I am an Engineering manager trying to switch to AI Engineering manager, but I like to understand the hands-on parts always).
It is hard entering the market with a new role, but I am trying. What are the next steps you can suggest me ?

thx to all.

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Hey @campa,

Congratulations on completing the specialization; it’s a very good step to start your career path as a Machine Learning engineer. I’d recommend you to enroll in Machine Learning Specialization as well.

Now, onto your question. Firstly, it’s normal to feel that you are not able to master it. Completing a specialization won’t make you a perfect machine learning engineer. It’s not that easy; it requires more effort and work. There is still a gap between academic research papers and shipping a product into an environment. I mean by that the theoretical part is somewhat different from the production and practical environment, so you need to get your hands on practice more.

Secondly, math is important and valuable in the artificial intelligence field, but does this mean you can’t be a machine learning engineer without a math background? Well, there are people who can build models and solve problems without a math background. They don’t fully understand what’s happening under the hood, so math here is what differentiates a machine learning engineer who really understands what’s going on under the hood from an amateur one.

In my point of view, a machine learning engineer is a combination of a software engineer and a data scientist. So, becoming a machine learning engineer doesn’t mean you have to leave your previous role. In fact, you are still a software engineer. Always think before diving into a project: “Does this project really need Machine Learning? Will it add value, or can we solve it with basic software engineering techniques?”

Again, congratulations on completing the specialization; it’s the first step. I suggest you get more hands-on practice by starting with simple projects, and over time, you will dive into larger projects.

Keep learning!


The Math for Machine Learning course just provides fundamental background for the more in-depth specializations, like the Machine Learning Specialization, or the Deep Learning Specialization.

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