The growth of artificial intelligence (AI) has inspired more software engineers, data scientists, and other professionals to explore the possibility of a career in machine learning. However, some newcomers tend to focus too much on theory and not enough on practical application. If you’re going to succeed, you need to start building machine learning projects sooner rather than later.

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It can be tough to know where to begin, so it’s always a good idea to seek guidance and inspiration from others. In this post, we’ll share real-world examples of machine learning projects that will help you understand what a completed project should look like. We’ll also provide actionable tips for creating your own attention-grabbing machine learning projects.

Machine Learning Projects

1. Identifying Twits on Twitter (Beginner)

Social media hate speech and fake news have become worldwide phenomena in the digital age. While offensive posts are a problem, it’s even worse when they are inaccurate or wrongly attributed to people through false profiles.

machine learning project: tweets

(Source: Towards Data Science)

A popular application of natural language processing (NLP) is sentiment analysis. This allows thousands of text documents to be scanned for certain filters within seconds. For example, Twitter can process posts for racist or sexist remarks and separate these tweets from others.

Eugene Aiken undertook a project to analyze the posts of two people and determine the probability that a specific tweet came from one particular user. To do this, he used the tweets of two well-known political rivals: Donald Trump and Hillary Clinton.

This involved several stages:

  • Scrape their tweets
  • Run them through a natural language processor
  • Classify them with a machine learning algorithm
  • Use the predict-proba method to determine probability

With the results, Eugene was able to identify which tweets were most and least likely of being from Donald Trump. This same process can be used to analyze tweets from anyone, including your friends or family.

You can learn more about this project here, and download the data set here.

2. Finding the Frauds (Intermediate)

As the world moves toward a cashless, cloud-based reality, the banking sector is under greater threat than ever. The global cost of credit card fraud is expected to soar above $32 billion by 2020.

While it’s a major problem, fraud only accounts for a minute fraction of the total number of transactions happening every day. This gives rise to another problem: imbalanced data.

In machine learning, fraud is viewed as a classification problem, and when you’re dealing with imbalanced data, it means the issue to be predicted is in the minority. As a result, the predictive model will often struggle to produce real business value from the data, and it can sometimes get it wrong.

machine learning project: banking fraud

(Source: Towards Data Science)

Rafael Pierre explains how the Towards Data Science team conducted a project to tackle this issue. Working with a highly imbalanced data set that had 492 frauds out of 284,807 transactions, they implemented three different strategies:

  1. Oversampling
  2. Undersampling
  3. A combined approach

While each technique has its virtues, the combination approach struck a sweet spot between precision and recall, effectively offering a high level of precision when dealing with imbalanced data sets.

You can learn more about this project here.

3. Catching Crooks on the Hook (Advanced)

Vulnerable marine life is under immense threat from illegal poachers around the world. For many years, it was practically impossible to keep tabs on the activities of every boat at sea. These days, advancements in AI, geo-mapping, and cloud computing have combined to realize a brilliant machine learning project idea: Global Fishing Watch.

machine learning projects: global fishing watch

(Source: Unsplash)

So, how exactly is machine learning helping Global Fishing Watch identify illegal fishing activity in our oceans? This ongoing project involves three main stages:

  1. Harvesting data – Most large ships use a GPS-like device known as the automatic identification system (AIS), which broadcasts their position. Although many fishing boats don’t have AIS, those that do account for about 80 percent of global fishing in the high seas. By tracking AIS devices with satellites, it’s possible to monitor ship movements, even in remote areas.
  2. Processing – Global Fishing Watch uses neural networks to process the information and find patterns in large data sets. This comprises some 60 million data points from over 300,000 vesselsdaily! With the help of fishery experts, the algorithm has learned how to classify these vessels by a number of factors, such as: 
    • Type – sail, cargo, fishing
    • Fishing gear – grawl, longline, purse seine
    • Fishing behaviors – where it is, when it’s active
  3. Sharing the results – This information on vessel tracking is publicly available. Anybody can visit the website to track the movements of commercial fishing boats in real time, follow them on the interactive map, or download the data. People can even create heat maps to check for patterns of fishing activity or view the tracks of specific vessels in marine-protected areas.

You can learn more about this project here.

4. Uber Helpful Customer Support (Advanced)

As one of the prime examples of technological disruption, Uber intends to stick around. With billions of rides to handle each year, the ride-sharing app needs a fantastic support system to resolve customer issues as quickly as possible.

machine learning projects: uber

(Source: Uber)

Uber set out to improve the effectiveness of its customer support representatives by creating a “human-in-the-loop” model architecture, which is called Customer Obsession Ticket Assistant, or COTA.

By split-testing two versions of COTA, the Uber team used deep learning to discover the impact on ticket handling time, customer satisfaction, and revenue.

You can learn more about this project here.

5. Barbie With Brains (Advanced)

Modern dolls that can “speak” play an important role in shaping the young minds of children. However, standard dolls typically have a limited set of phrases that have no correlation to what the child is saying.

But what if the doll could understand questions? What if the doll could give logical answers?

machine learning projects: hello barbie

(Source: ToyTalk)

Hello Barbie is an exciting demonstration of the power of machine learning and artificial intelligence. Through NLP and some advanced audio analytics, Barbie can interact in logical conversation. The microphone on her necklace records whatever is said and then transmits it to the ToyTalk servers, where it is analyzed.

There are over 8,000 lines of dialogue available, and the servers will transmit the most appropriate response back within a second so that Barbie can respond.

You can learn more about this project here.

6. Netflix ‘n’ Change (Advanced)

Netflix is the dominant force in entertainment now, and the company understands that different people have different tastes. Sometimes, people are guilty of judging shows or movies by their images and so they might never check out certain programs. Not to be defeated, Netflix aims to persuade more people to watch their shows.

machine learning projects: netflix

(Source: Unsplash)

When you visit Netflix, sometimes you’ll see different artwork for the same shows. This is machine learning at work. Netflix uses a convolutional neural network that analyzes visual imagery. The company explains that they also rely on “contextual bandits,” which continually work to determine which artwork gets better engagement.

Over time, as you use Netflix more, it begins to understand not only what programs you like, but also what type of artwork! For example, if you’ve watched several movies starring Uma Thurman, you’d be likely to see Pulp Fiction art featuring the actress instead of co-stars John Travolta or Samuel L. Jackson.

You can learn more about this here.

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How to Generate Your Own Machine Learning Project Ideas

If you’re already learning to become a machine learning engineer, you may be ready to get stuck in. If not, here’s some steps to get things moving.

Pick an Idea That Excites You

To kick things off, you need to brainstorm some machine learning project ideas. Think about your interests and look to create high-level concepts around those. Choose the most viable idea, and then solidify it with a written proposal, which acts as a blueprint to check throughout the project.

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Avoid Going Out of Scope

If it’s your first project, you should fight the urge to go beyond the scope of the project. Focus on simple machine learning projects. 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.

Test Your Hypothesis

Especially when talking about easy machine learning projects for beginners, the main thing to think about is generating 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 we advise you to use it to conduct your testing.

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) – This allows you to integrate your machine learning insights into the product.
  • Record results on a single database – By collating everything together, you make it easier to build upon the results.
  • Embed the code – When you’re short on time, embedding the code is faster than an API.

Revise and Learn

When you’ve finished the project, evaluate the findings. Think about what happened, and why. What could you have done differently? Over time, as you gain experience you will be able to learn from your own mistakes.

Tips for Machine Learning Projects for Beginners

Even simple machine learning projects need to be built on a solid foundation of knowledge to have any real chance of success. Furthermore, the competitive playing field makes it tough for newcomers to stand out.

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Here are a few tips to make your machine learning project shine.

Get Familiar With the Common Applications of Machine Learning

Broadly, there are three basic types of machine learning:

  • Supervised learning analyzes historical data to predict new outcomes. For example, predicting property prices.
  • Unsupervised learning looks for data patterns by using statistical analysis. For example, identifying customer segments within your company sales data.
  • Reinforcement learning operates with a dynamic model that uses trial and error to constantly improve performance. For example, stock trading.

When you develop a better understanding of these applications, you will know how to apply machine learning to your problem.

Don’t Underestimate Data Preprocessing and Cleaning

Noisy data can skew your results. Therefore, you should look to use data preprocessing and data cleaning regularly. Put simply, this is about taking your data and making it easier to understand. By tidying things up and inputting missing data, you ensure that your models are as accurate as possible.

Machine Learning Is a Team Game

Even Neo needed friends. When you’re developing machine learning projects, you’ll need to work with other people, many of whom won’t have the same understanding of AI and software as you.

You must trust other people, and also be honest about your model. Ultimately, when you’re working on machine learning projects, aim for transparency and open communication so your project can run smoothly.

Focus on Solving Real-World Problems

It’s all well and good to use machine learning for fun applications, but if you have your eye on landing a job as a machine learning engineer, you should focus on relieving a pain point felt by a lot of people. Think about how your project will offer value to customers. By researching real-world issues, you can make your project stand out as one that the world wants and needs.

Play to Your Strengths

If you’re new to machine learning and don’t have a lot of experience, it can be a little daunting going up against veteran coders and software engineers. In this case, your perceived weakness can be a strength. You can 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.

Machine Learning Can Make the World More Human

The machine learning industry will continue to grow for years to come. While some people see the so-called “rise of the robots” as the end of the personal touch in business, the reality is quite the opposite. There are so many great machine learning project ideas that actually help companies offer a better service, effectively humanizing brands by making them more in tune with the interests of their target audience.

It’s not easy to develop your first machine learning project ideas. By learning from others, you can create something great.

Springboard’s AI / Machine Learning Career Track, the first of its kind to come with a job guarantee, focuses on project-based learning. Find out more.