The proliferation of artificial intelligence (AI) has made mastery over machine learning (ML) an imperative for anyone in the field of software development. While theoretical knowledge will help, it is not possible to master ML without a strong foundation in practical experience.
With hands-on experience in these projects, aspiring developers, data scientists and other professionals will not only understand how to apply their knowledge to solve real-world problems, but by undertaking different kinds of projects, they will also continue honing their skills, understand their strengths and weaknesses better, and also add valuable experience to their overall portfolio, making it easier for them to land a job.
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However, it can be tough to know where to begin. In the following paragraphs, we have tried to provide some guidance to newcomers on how they can apply ML to real-world problems through projects:
Get Familiar With Common ML Applications
This could be termed step 0 in an ML project lifecycle. Before you get started, invest time in developing a better understanding of ML. There are three basic types – supervised learning, unsupervised learning, and reinforcement learning. Learn what the applications of each of these could be, and once that is done, you will have a better idea of how to apply ML to your problem.
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Select A Project
As mentioned in the introductory paragraphs, there are many ML projects found online that use publicly available real-world datasets. Understand whether or not it covers core aspects of ML. Also, check if it aims to solve a pressing problem and provide real value to stakeholders – this is relevant, especially if you are doing projects with an eye on landing jobs.
While choosing a project from an industry, you are familiar with will help you play to your strengths, selecting one that you may not be well informed about will give you the chance to explore an interesting topic.
Understand The Problem
Once you have selected the project, proceed to define the problem you are trying to solve, and its end objectives. Although this may seem like an easy step, it will give you a window into the criticality of the problem you are trying to solve. During this step, ensure that the objective of the project is measurable. It will also allow you to revisit your choice of the project early on if the results are not desirable.
Outline Limitations – If Any
As an extension to the previous point, this is another step that evaluates the choice of your project and allows you to revisit it if the results are not desirable. The limitations you should consider before proceeding are the following:
- Resources (lack of time)
- Infrastructure (lack of computing power)
- Data (Unstructured and uninterpretable)
Cleaning The Data
If the above points check out, and you have finalised a project to work on, the next step would be to clean the data. If you have curated and collected them from numerous sources, merge them into a single table. After this, wrangle the data, and then conduct exploratory data analysis (EDA).
Selecting, Training & Evaluating Your Model
Once you have cleaned your dataset, you can begin by training your model based on the algorithms. There are several tasks involved in this step, the first of which is selecting the model, which varies based on the problem chosen. A different modeling approach could be warrant based on whether it is a regression problem or a classification one. Following this, you need to train your model and then evaluate it using testing data based on the success metrics decided by you.
In a previous article, we have illustrated these steps with an example. You can read it here.
While there are fun applications of machine learning as well, choosing problems of real import will have a higher chance of getting noticed on your professional portfolio. It might make it a tough playing field, especially if you are a newcomer, but the learning curve with such a project will be great.