Do not build an ML portfolio without these designs

A is a new resume – it's what replaces real work experience.

But right now, your projects are either useless fillers or you didn't take them seriously, that's why you're not the next interview.

So this time, I will break down the types of projects that are important to top tier companies actually Look, so you can stop posting dead end apps and start organizing conversations.

Let's make your portfolio the review it needs to be.

3-5 simple projects

The perfect base for your portfolio is 3-5 “easy” or “easy”.

This won't move the needle on hiring, but it will give the portfolio an initial weight.

Think of these simple projects as “Firm-Up Reps” in the gym. It's not the heavy lifting that builds big muscles, but they establish the basic mechanics, consistency, and discipline needed before tackling a bigger challenge.

The main goal of these programs is to create and build without a guided lesson, and to really get you to solve problems.

It's about “Optics” and making sure your resume, GitHub, and Linkedin profiles are up and running and populating.

However, take about a month to build these small projects, make sure they are of sufficient quality and done quickly with chatgpt.

Plan to build a variety of different projects, each using different tools, datasets, and machine learning algorithms.

If you're looking for some inspiration, check out this repo I made about 5 years ago, which contains examples of these simple projects when I was trying to get my first job.

GitHub – EGIGHOWELL / Data-Science-Projects: A selection of small Data Science projects.
A selection of small data science projects. Contribute to the development of Egrowell / Data-Science-Project by building…GitHub.com

One thing I will say is that these projects may be below today's standards, as the field is becoming more and more competitive.

So, below is a list of important goals that your simple projects should meet to make them useful:

• A variety of algorithms– try to install Grown trees, Neural networks, and converging algorithms like K-way and DBSCAN in your projects.
• Novel data –It is much better to find a realistic and realistic dataset that reflects the data you will encounter in the real world. This will impress employers and interviewers more, directly demonstrating your Data Science and Machine Learning skills.
• – Deal with someone– To decide what should be opened for your projects, it is better to start by answering some questions that you think will be interesting to find out from the data. A personal touch is always better.

An end-to-end project

If you want to work in machine learning, you need to know how to implement your own algorithm.

“The model in Jupyter Atebook has zero business value”

You have probably heard this phrase from me and others over and over again.

Having a Transformer model that's too complex, with too much personality means nothing but making real decisions impossible.

Companies and hiring managers know this, and frankly, all they care about is whether your model keeps or makes money and that their bottom line increases.

Actually that is food.

Therefore, you want to demonstrate to potential employers that you can build and submit an algorithm at the end of your portfolio.

Your project should include the following:

• Data collection and storage.
• Reversal data.
• Exemplary training and evaluation.
• Model delivery (via API, web app, VPs, etc).
• Analysis and presentation of your results.

This project is very common for beginners to create because it requires some skills and learn a little bit of software engineering.

Some of the things you will need to learn are:

What I don't want you to do is be intimidated and overwhelmed by the list.

Start small and learn important things as you go; You certainly won't need to use everything I just mentioned.

And as always, make it as personal as possible; This will keep you motivated, and is a great talking point in conversations.

If you want a real life example, then check out one of my previous YouTube videos where I walk through the last complete project I did which created Stock Price Forecasts and made my portfolio.

A Research-Based Project

I often recommend that people add some research element to their portfolio.

One way is to reuse their favorite research paper.

You will learn a lot in this process:

• Understand the complex calculations associated with cutting-edge models.
• Use Movidicals models from scratch or using simple libraries.
• Think creatively and use your knowledge with new ideas.
• Improve your understanding of current trends in the field and which top researchers are working on it.

And the best part is that the majority, literally 99%, of voters do not do this, so you will stop quickly.

Some useful websites for finding papers:

Recycling paper is very difficult. I've tried several times in the past, and I couldn't get it 100% right, but I learned a lot in the process.

Another way to include research in your portfolio is through reading papers and cross-referencing papers by writing about them online or even at a payrage club.

The latter is what I used to set up at my previous company, and it was useful. I wrote various papers such as:

It taught me how to translate some of the most technical topics in the world right now into a digestible hour presentation.

This is a skill that companies really want, as many professionals in the field do not have it.

If you don't currently work for a company where you can set up something like this, there are plenty of conflict groups and community groups out there.

One team I recommend is Yannic Kilcher's feud. He is a machine learning researcher and developer who creates YouTube videos breaking down research papers.

Write technical documents

Most people think that their essays need to be “down to earth.”

What if I told you that ours is just an excuse, and your blog doesn't need to be unique to get you to work?

If you look at mine, most of the posts are about basic math, data science and machine learning concepts.

To date, I have written well over 150 and over 60 advice articles.

This started on my own as a way to learn more about the field; I don't care if people like them or not, as they were only with me.

This is the attitude you should have.

Start by writing down what you are currently learning or want to learn. There is no need to win.

Having a blog brings many ideas to your career and skills:

• It strengthens the understanding of concepts.
• It helps you think and have better communication skills.
• It shows a self-motivated attitude and interest in the field.
• He will give you honest jobs and interviews. This happened to me!

Your blog is Passive Income generator for your business. The earlier you invest in it, the better the payout.

I recommend that you start blogging here in the direction of data science, because it is very easy to use, has a large scientific community, and already has a built-in audience.

There are other developer-focused platforms, such as Hashnodeor blog on your own website, using platforms like Pressure or Bad wind.

You can have your own blog that you create from scratch using HTML, CSS and JavaScript!

If you want to learn more, I have a whole post about how to start and write a tech blog that you can check out below:

Now that you know the exact projects that turn your portfolio into conversation starters, there's one last piece of the puzzle: How to present it.

Many people simply throw a GitHub link on their resume and hope for the best, but if you do that, you're missing a huge opportunity to highlight the business value of your work.

To learn how to properly showcase your portfolio, check out some of my previous posts below.

I'll see you there!