TOP 7 HELPFUL TIPS FOR CREATING MACHINE LEARNING PROJECTS

Before you even begin to think about machine learning projects, it is vital that you have a solid foundation of knowledge in order to be successful.

You need to make sure you fully understand the basic concepts of machine learning and then move on to creating your project.

Broadly, there are three different types of machine learning; supervised learning, unsupervised learning and reinforcement learning.

Another common mistake for beginners when starting machine learning projects is underestimating data cleaning and processing. Make sure your data is easy to understand and input missing data, this will ensure that your models are as accurate as possible.

All that being said let's get into it, here are my top seven helpful tips for creating machine learning projects...

1. ALWAYS FOCUS ON SOLVING REAL-WORLD PROBLEMS!

It's easy for machine learning projects to get lost along the way. It is vital to always remember at every point in the process that your model needs to solve a pain point for a business. By researching real-world issues, you can make your project stand out as one that the world wants and needs.

2. PLAY TO YOUR STRENGTHS

This may be difficult, especially for beginners. Try to lean on your background and previous knowledge about different industries to create unique machine learning projects that many other people may not even think about. Don't try to pass yourself off as a genius! You are only beginning your machine learning journey, employers know this.

3. GET EXCITED ABOUT YOUR TOPIC

Time to get those creative juices flowing. Think about something that interests YOU but one that also adds real-world value. Create high-level concepts around those interests, then pick the most viable idea and run with it. Next, create a written proposal. This will act as a blueprint to check throughout the project.

4. FOCUS ON SIMPLE MACHINE LEARNING PROJECTS

Do the simple things really well. By focusing on a small problem and researching a large, relevant data set. Your project is more likely to generate a positive return on your investment. Don't try to run before you can walk.

4. GENERATE INSIGHTS

The main thing to think about is generating actionable insights from your project. Don’t worry about acting on those insights yet. Model your hypothesis and test it. Python is the easiest language for beginners and I advise you to use it to conduct your testing.

5. IMPLEMENT THE RESULTS

Once you’ve reached all the desired outcomes, you can look to implement your project. There are a few steps to this stage:

• Create an API (application programming interface) that allows you to integrate your machine learning insights into the product.
• Record results on a single database by collating everything together. This makes it easier to build upon the results.
• Embed the code. When you’re short on time, embedding the code is faster than an API.

6. WHAT DID YOU LEARN?

When its all wrapped up, it is vital that you evaluate your findings. What happened? Why? Could you have done anything differently? Then as you progress through your career, you will be able to learn more and more from your mistakes.

7. Share your learns.

Take a few moments, to document your key learns and share them within the group.

I know you’ve heard these seven lessons before. They are not new ideas that will significantly alter the way you manage your projects. The challenge is to consistently apply these lessons when spinning multiple plates, attending too many meetings and forced to trade-off processes.

Successful ML projects is all in the fundamentals. Committing to them and being consistent is the best advice I can share.

Let me know your thoughts.

For the latest roles in Data Science, Data Engineering and AI head over to www.alldus.com