How To Build A Compelling And Winning Machine Learning Portfolio That Will Get You That Job

Getting a job in the machine learning field requires more than just knowledge and skills. Standing tall among your equals requires your ability to actually demonstrate how skilful you are.

You need to convince your prospective employer you are the right person. And how do you go about this?

This blog will walk you through all you need to know to build an extremely impressive and winning machine learning portfolio.

Beyond skills: When all you need is proof!

Well, let’s go straight to the point.

If you’ve got it, show the evidence! This is how to build your machine learning portfolio as a beginner.

If your dream is to get a job in a machine learning career field, first you need to constantly develop your capacity on key job responsibilities and expectations.

The second thing you need to do is to show you have the capacity.

These two points are quite different.

Many beginners often concentrate on the former to the detriment of the latter.

They have attempted almost all online courses out there. They have immersed themselves in different boot camps and even finished up with internships.

Gut guess what? The only thing they lack is the craft of showcasing their capabilities!

Am I telling your story, here? Does this resonate with your current situation?

While these endeavours are necessary, I can tell you, they are not sufficient in getting you that job.

So, what are you missing?

Maybe you don’t know how to tell your stories. Perhaps you don’t know how to document and project your worths.

Can you see, now?

Ok. Let me put it this way:

Imagine you are boarding a plane without a passport. I can tell you, even when your tickets are authentic, you ain’t going nowhere!

As an aspiring machine learning career starter, don’t ever be caught up in this.

Please don’t!

Note this:

Getting a job in a machine learning career field, especially in this era of stiff competition, requires more than knowledge and capacity. Standing tall among your equals requires your ability to actually demonstrate how skilful you are.

You need to present clear and interesting documentation of what you’ve got.

You must understand the technique of persuasion. Your employer must be convinced you are the right person.

And how do you go about this?

The bitter truth is: you won’t automatically get that job just because you possess all the required skills and experience.

Hunting for a machine learning job? You need proof beyond the CV.

Nobody cares about what you can do until you show them.

This is a general rule of life!

Great you have the qualities, but nobody gives a damn if you can’t smartly show them!

The interviewer or the potential employer needs evidence from you to be convinced.

You will be appraised by your employer based on how much they know of you, not how much you think of yourself.

Olusegun Omisakin

For you to show you are really serious about starting a career in machine learning, you need to show it.

Yes, you have developed your skills. Accepted, you have experimented with different machine learning projects. And you are sure of what you are bringing on board.

But how do you expect the potential employer to really believe you are capable of what your resume has demonstrated?

You need proof beyond the CV.

You need a higher layer of the capacity validation strategy. You need smart documentation of those relevant projects and practical skills you possess of which the employer is in dire need.

You need a portfolio- the one that can deliver you that job.

Guess what? The most interesting part is the fact that it doesn’t matter your level of practical experience or years of learning machine learning.

You can start building your portfolio right from day one of your learning journey. You don’t have to be a guru before you start collating your skills in machine learning.

Here is my value proposition to you: At the end of this blog…

This article will open your eyes to and equip you with the A-to-Z of a step-by-step approach to developing your own attention-grabbing and winning machine learning portfolio from the scratch.

I will walk you through everything you need to know and learn about developing a winning machine learning portfolio as a beginner.

• You will know how to build a winning machine learning portfolio and raise your confidence in hunting for a machine learning career job.
• You will be able to revolutionalise your machine learning job-hunting strategy by adapting a winning- and result-focused portfolio building strategy.
• Your portfolio will gain more exposure in the industry and you will have more interview leads.
• You will improve your confidence and composure before, during and after your job interview.

Also, read these:

How To Start A Career In Machine Learning: A Complete Guide To Career Transition.

Reasons You Don’t Need to Learn Machine Learning (and what to do instead).

What is a machine learning portfolio?

You: I have the required skills.

Employer: But, how do I know?

A machine learning portfolio, like any other portfolio, is a collection of machine learning projects, practical skills and experiences that proves or showcases a candidate’s capacity for the job she/he is applying for.

A portfolio aims to serve as evidence of the capacity required for the job.

It also assists the employers to properly assess the candidate in the cause of a job interview.

While a great portfolio is highly important in demonstrating how the candidate has handled different projects, it is not, nonetheless, the only success factors in job hunting and interview specifically.

Why do you need to build a machine learning portfolio?

Reason 1: Unlike your CV, a machine learning portfolio is evidence of your professed expertise.

Your CV is what you said of yourself. Your portfolio is what others see of you.

Olusegun Omisakin

How will an employer know you are whom you claim to be when there is no evidence of a practical machine learning project you can showcase?

Reason 2: A machine learning portfolio helps your visibility.

Your portfolio, when well built, helps your brand visibility.

Your portfolio can create such a professional perception for you even when you are not known by many people in your industry.

As long as you design it in such a way that it’s easily shareable through social media, your website or any other platform, a great portfolio is always the best way to go!

Reason 3: Your portfolio is your best deal for landing a machine learning job, internship or freelance projects interview.

I can’t emphasize this enough! One of the great channels of landing that cool machine learning job is through developing a fantastic project portfolio.

Nothing else can beat this.

Beyond all you can do and the skills you have acquired so far, there is only one thing that stands between you and your dream job- your ability to convince your employer you are the right person!

This is where your portfolio comes in. It helps you tell the story better and smarter too! Building your portfolio helps you to be motivated.

Reason 4: Building up a collection of completed machine learning projects can help you leverage on future projects, keep your focused and get motivated.

When you document your journey and see how far and deep you have gone, it helps you to want to go even farther and deeper.

When you create time to document your learning journey, you will be amazed to see how close you are to your goal.

That is how your portfolio can be a great source of motivation for you.

Reason 5: A well-designed and structured machine learning portfolio authenticates your authority, uniqueness and originality.

Building your own machine learning portfolio obviously announces your arrival and creates space for you.

It creates a point of reference by which your expertise is evaluated and judged.

There is no other simple way to register your originality than having a smart portfolio people can reference.

But what is an employer actually looking for in a machine learning portfolio?

You: Here is my CV, credentials, awards and other relevant documents.

Employer: But I can’t see anything!

Don’t forget, in the whole process of searching and hunting for jobs, it is not about you, it is about what you can offer. It is really about how you can solve problems.

So, a typical employer is simply looking for someone who can create and add value to his/her business by way of solving problems, generating more revenue and identifying opportunities for the business.

An employer is interested in candidates who can display the required skill sets for the job

Of course, in addition to hard technical skills, companies are looking for candidates with soft skills such as, the ability to communicate, take initiative, collaborate with others and lead an innovative team.

A well-rounded portfolio should, therefore, exhibit a candidate’s capacity in terms of these skills in a well-documented format.

Also, read these:

10 Interesting Project Ideas to Prepare You for Machine Learning Role.

How to Learn Machine Learning The Self-Starter Way: Complete Guide.

How do you build an excellent machine learning portfolio? Here are the 6 golden rules of a winning machine learning portfolio.

We all know it. Most machine learning portfolios suck. But yours doesn’t have to be like that.

First, let’s talk about the key ingredients (the must-haves) of a winning portfolio.

Why do most machine learning portfolios suck?

We will highlight critical features of a good machine learning portfolio a beginner may benchmark in developing a portfolio that wins.

If you get it right with the points listed below, your portfolio will sure stand out from the rest.

The following are the properties of an excellent portfolio:

1. Choose your domain.

Don’t be a generalist and don’t hyper-niche either! Horn the most relevant skills that are in demand by companies and industries in your domain.

In addition, you need to tailor your machine learning projects portfolio towards specific machine learning jobs you are targeting and the general required skills in your domain.

Don’t fall into the trap of showcasing every project you have ever worked on.

It’s not about the numbers of projects. It’s about the relevance of the projects in showcasing your skills specifically for the advertised job_._

Differentiate yourself by being innovative. Build up your portfolio with projects that excite the interviewer or employer.

2. Priority matters.

Don’t just build it. Build it with purpose.

Concentrate on the projects that match the type of machine learning job you are applying for and that matter to your prospective employer.

Apart from making your portfolio outlook appears clean and smart, prioritizing your experience also helps you save a lot of time in building your portfolio.

Identify the roles you are interested in and explore the core responsibilities of the job you are applying for.

Following these guidelines will ultimately help you identify what projects to include in your portfolio.

3. Emphasis your all-round skills across all facets of project workflow.

Exhibit your skills in data analysis, model building, result presentation and interpretation and also end-to-end machine learning project implementation right from inception to a real-world model evaluation.

Above all, how your projects and, indeed, your entire portfolio is structured is very important.

Each project should emphasise a clear objective and process much more than the accuracy of the final results.

4. Include additional components which demonstrate your extra-portfolio engagements/skills.

While these components may not directly relate to the core projects, they can actually make you stand out from the rest.

Additional information like your competition participation, professional blog contents, articles/paper written, online questions about machine learning you answered, etc.

5. Make your portfolio publicly available and shareable (including on social media)

This is one of the most important must-haves of your portfolio.

If you can’t share it, the essence is defeated!

You should be able to share your portfolio link with people easily.

You can even put the link in your social media accounts.

6. Make your portfolio clean, attractive and engaging.

The scope of each of the project must be small enough to accommodate the relevant information. Also, don’t underestimate the power of clear communication.

The entire scope of your portfolio must be understandable with clear communication of intent and purpose.

Also, read these:

Start Here With Machine Learning: A Beginner-Friendly Step-By-Step Procedure

7 Costly Mistakes Beginners Make When Starting A Machine Learning Career (plus tips on how to avoid them).

What type of information can I include in my portfolio?

The ultimate strategy in building an excellent machine learning portfolio is to start as early as possible. It takes deliberate and intentional planning.

It doesn’t matter your level of learning. All it takes is to understand your purpose and document your journey.

A machine learning portfolio doesn’t have to be perfect!

All you need is a clean purpose-driven step-by-step documentation of your skills in all ramifications.

You can always improve the structure, contents and even the layout.

I highlight below some ideas that qualify to be featured in your machine learning portfolio:

• Document your insights from your first exposure to learning machine learning, especially through those free online resources. These include, but not limited to, machine learning blogs, websites, YouTube videos, etc.
• Curate in a logical manner your course work and other presentations relating to online courses from MOOC, boot camps, YouTube, etc.
• Arrange your notes, reviews and summary from machine learning books you have read.
• Document your machine learning competitions participation from any platform. Show your success and the process.
• Reference the most valuable and purposeful machine learning blog contents either on your own personal blog or as a guest writer.
• Itemize your machine learning articles or manuscripts published or unpublished with emphasis on the issue, process and literature gap filled.
• Document the conferences you have attended, papers you presented or your general participation.

Cool examples of machine learning portfolios

Anna-Lena Popkes

Key properties:

• Anna-Lena has a cool personal website with easy navigation

• Very attractive home page with her picture, full name and specialization statement.

• Detailed page categories showcasing skills, projects, resume, papers and other relevant information.

• Inclusion of contact details such as email address, phone number and social media accounts, etc.

Gerin Berg

Key properties:

• Gerin's landing page projected the most important contents such as his mission, his current works (blog) and his portfolio

• The graphical demonstration style of portfolio projects is also cool.

• The inclusion of the blog section to showcase his recent and ongoing activities makes his website stand out. This is a cool way to establish his authority and authenticity.

• His page arrangement also features key information such as portfolio, services, blog and contact.

• Gerin also included his social media accounts. This makes it easy to contact him.

Harrison Jansma

Key properties:

• Harrison’s website looks great, clear and neat with the white background
• The website page arrangement emphasizes his core professional details such as personal details, portfolio, latest projects, area of interest, skills and resume.
• Featuring other details such as his activities on GitHub and Medium further validates his professional branding.
• Harrison’s web also includes his contacts and social medial information

Let me hear from you. Keep learning. Keep growing. Go for it.

Also, read these articles:

1. How To Start A Career In Machine Learning: A Complete Guide To Career Transition.
2. Reasons You Don’t Need to Learn Machine Learning (and what to do instead).
3. 10 Interesting Project Ideas to Prepare You for Machine Learning Role.
4. How to Learn Machine Learning The Self-Starter Way: Complete Guide.
5. Start Here With Machine Learning: A Beginner-Friendly Step-By-Step Procedure
6. 7 Costly Mistakes Beginners Make When Starting A Machine Learning Career (plus tips on how to avoid them).
7. A Complete Guide To Learning Machine Learning In Your Spare Time: An enthusiast’s approach
8. 5 Key Most In-Demand Soft Skill Sets You Need to Develop as a Machine Learning Career Starter