Before you even begin to think about getting your hands dirty in Machine Learning projects, it is vital that you have a solid foundation of knowledge in order to be successful.
First and foremost, you need to make sure you fully understand the basic concepts of Machine Learning and then move on to creating your project. There are three different types of Machine Learning to explore; Supervised Learning, Unsupervised Learning and Reinforcement Learning.
One thing to be aware of when starting Machine Learning projects is to not underestimate 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.
Nevertheless, here are six helpful tips for creating Machine Learning projects:
1. FOCUS ON SOLVING REAL-WORLD PROBLEMS!
It is very easy for Machine Learning projects to get lost along the way. Therefore, 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 to solve real-world issues.
2. PLAY TO YOUR STRENGTHS
When it comes to the busy job market, you need to stand out from the crowd. 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. If you're a beginner, don't try to pass yourself off as a genius! You are only beginning your ML journey, so let employers know this.
3. GET EXCITED ABOUT YOUR TOPIC
Be creative and think about something that interests YOU but nevertheless, choose something 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. Focus on a small problem rather than taking on too much that you may not be able to deliver. This way, 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, but do show the potential impact of your work. Model your hypothesis and test it. Python is the easiest language for beginners to conduct your testing.
5. IMPLEMENT THE RESULTS
Once you’ve reached all your desired outcomes, you can look to implement your project. There are a few steps to this stage:
- Create an API 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.