There are plenty of reasons why am creating an effective machine learning portfolio of projects is important when you’re starting your career
But how do you ensure that the portfolio that you are creating is going to help you get a job? I’m sharing some tips and tricks to get you started.
Let me share an example with you of a project I did and I was super proud of But did nothing for my career.
The project in question classifies birdsong as into two different groups. It required me to learn a variety of different skills and to process audio files.
However, these were not skills that I would need in my future career.
Realistically no one is using this project.
If I want to start over again I would do things a little differently.
You don’t have to every area of math to become a successful accountant.
In a similar way, you don’t have to know all areas of machine learning to be an effective data scientist.
Making a Compelling Data Science Portfolio
Today I wanna talk to you about how to identify projects that will help you to build an effective portfolio that will get you your dream job and machine learning in data science.
The good news is it’s not going to be as difficult as you think.
Shall we get started?
Why is having a machine learning portfolio important?
Having a portfolio of machine learning projects you have been completed will help show an employer your technical skills.
It is not enough to have done a machine learning course, you need practical experience working with real-life data sets to help you take your next steps into your career.
This is what you will show employers through your portfolio.
Step One: Knowing Where to Focus.
When you starting out in a career in machine learning it can be quite daunting. There is so much to learn how do you know where to focus your time?
On top of this, you got into this subject because you’re passionate about machine learning. As a result, you want to learn everything you possibly can about the topic!
You do not need to do this.
Learning everything there is to learn about machine learning is not the only way you get you an amazing job.
Furthermore, it can actually hinder your progress and by making you want to learn everything there is to learn. Trying to learn everything is stopping you from getting practical experience in the workplace.
The best way I found to help you focus your attention is to look at job boards.
Use job boards to help you prioritize
Take a look at a few job boards within the data science space. What are the different roles that are currently available?
Of the roles that are currently available, which are you interested in?
Once you have identified the roles you are interested in, take a look at the requirements for an application to that role.
Which skills does that and potential employer call out as important?
You can use these skills identified, identified by the employer, as the basis for planning out which courses you want to undertake in machine learning.
In addition to this, you can also use the skills to help you identify what projects to do to build up your portfolio.
What can you use from your past to make your machine learning portfolio?
Another thing to consider when building out your machine learning portfolio is your previous experience. If you are transitioning into data science having experience in another field you can use this!
Previous experiences make excellent background research.
Think about:
- What did you enjoy about your previous role?
- What experiences can use in data science? Do you have topical knowledge?
- What are the job requirements? How can you map these to your experience?
Step two: Creating A Machine Learning Portfolio
Once you have identified which skills you need to get your dream job it’s time to start building your portfolio.
The first step you will need to take as identifying a dataset to practice on. There are plenty of different resources you can use to get hold of Free data sets.
Some popular sources of datasets are listed below:
- Kaggle: Kaggle datasets cover a wide range of topics and are free to access. You will find examples of different projects and can take part in competitions when you feel more confident!
- Machine learning repository: A great source of academic data sets
- Imagenet: A source of labeled images for different categories
Once you have chosen your day to say you’re ready to start your project.
I recommend having five strong projects within your machine learning portfolio.
Some of the things that will help your project to stand out versus your peers are listed below:
1. Add Good Comments to Your Code
Make sure you add good commentary to your project.
This will show the different stages that you take that you complete. You are telling the reader how you think about projects while helping them to understand your thought process.
This is important because it will help a potential employer see how you work.
This, in turn, will allow them to understand if that is the is going to be effective within the team.
2. Mix it up
Even though you wanna make sure that you’re focusing in on the skills required for your chosen job area, it is still important to show a variety of different techniques within your profile.
Make sure that you are not just repeating the same processes in each project. Make sure that your potential employer can see the different skills that you are able to bring to the team.
3. In-depth analysis
The final tip that will help your machine learning portfolio stand out, is to do an in-depth analysis.
This may sound obvious but you would be surprised how many people don’t bother.
Don’t just stop at the first reasonably accurate prediction that you are able to make.
Keep going until you have tried a few different options, tested the parameters and refine your model in depth. Show these different skills and get the best result possible.
This is a research approach that will be important in your future career as a data scientist.
Step Three: Share Your Portfolio
Once you have a machine learning portfolio that you are happy with don’t be afraid to share it.
You should definitely make sure that your portfolio is available to view publicly on GitHub. Many potential employers will check your GitHub repository to see examples of your technical work.
Why not add it to your LinkedIn profile for good measure?
Don’t be afraid to share your work!
As you are trying to get into the field of data science it is important to share your machine learning portfolio with your network.
Yes, it can be scary sharing your work as a beginner in the field – but that should never stop you!
I remember the first time I told people about sharing my projects on GitHub. It was terrifying.
I kept thinking that people were going to call me out as a fraud.
But they didn’t.
In fact, everyone was really supportive.
Even if the initial feedback is amazing it is an important learning process and you will get better because of it.
Take your next step and build your strategic machine learning portfolio
Having a portfolio is important not only for getting you a great job, but it will also show you how you have improved over time.
This improvement you say will help you feel more confident as you progress in the world of data science.
I hope you found these three tips useful and that you are ready to move on to creating your own machine learning portfolio.
How to build a machine learning portfolio – top tips!
To summarise on what we have discussed the three takeaway is you should have are:
- Create a targeted portfolio to help you get your dream job
- Use job boards to identify which projects are going to be there Most relevant for your chosen career
- Make sure you show a variety of skills in your project and that they are detailed enough for your employer to understand your skillset
Good luck with everything and creating your dream career.
You can do it!
worldwide.Technology, especially artificial intelligence
has made our lives really easy. From the general apps to
Alexa in our houses technology has seeped in even without
us realizing when this happened. Here is a quick view of
future where AI will have significant role in each phase
of our life.
Android is a mobile operating system based on a modified
version of the Linux kernel and other open source software,
designed primarily for touchscreen mobile devices such as
smartphones and tablets. … These apps are licensed by
manufacturers of Android devices certified under
imposed by Google