Machine learning is one of the most popular tech fields today. The field contains both a high demand for skilled data scientists and an increased supply of interested individuals who wish to pursue a machine learning career. Due to this surge in people interested in the field, applying for and landing job roles has become very competitive.
One of the deciding factors of any data scientist in landing the position they applied for is the strength of their portfolio. For most tech fields, your portfolio is an overview of every project that you have built, the tools you used to build it, and occasionally your motivation for building it. The more projects you have in the portfolio, the more versatile your knowledge base is, and the more experience you can demonstrate to a prospective employer in that specific field.
As a machine learning enthusiast you need to master many skills to be prepared for the workforce, and the best way to master any skill is by doing it. However, one of the confusing decisions any of us should make regardless of our experience level is to build various projects. Choosing the right projects that will strengthen your portfolio and help you hone your skills is not an easy task.
So, how do you choose a project to work on and add to your portfolio? This article will go through 12 machine learning projects for data scientists of all levels, from beginners to advanced practitioners. Having one or more of these projects in your portfolio can help make your resume stand out, and hopefully get you one step closer to landing the position.
What do you do when you are missing critical information or need to know more about a specific topic? Today’s most straightforward approach to that question is, “Google it!”. Millions of people use Google every day in billions of searches to find information about a wide variety of topics.
One exciting project idea is to use Google’s Pytrends API to analyze what people around the world are searching. Pytrends can help you obtain different information about what people use Google for. For example, you can find search statistics about a specific topic, trending searches, and categorize those pieces of information by time, region and keywords.
Machine learning is an umbrella term covering multiple subfields, including computer vision. Computer vision has quite a bit of active research as the potential for the automatic interpretation of visual inputs is massive. Because of that potential and research, it attracts a lot of attention from the users. Suppose you’re new to machine learning in general or to computer vision. In that case, an excellent place to start is using the MNIST dataset to build a digit recognizer. Building this project will help you get familiar with the basics of computer vision and neural networks.
Categorizing machine learning projects into simple or complex ones is a challenging task. The project’s complexity often depends on how you choose to implement it rather than the project itself. One great example of that is recommendation systems. At first, you would assume that building a recommendation system is an intermediate or advanced project. But with experience you can create simple and straightforward code to implement your recommendation engine. For example, you can use the rich, rare dataset to implement a simple recommendation system using a user-user similarity matrix that recommends items that similar users like.
Suppose you are into being physically active and sporty. In that case, one project that might interest you is the recognition of different human activities using the smartphone dataset. This dataset contains the fitness activity recordings of 30 people captured through smartphone-enabled inertial sensors. This project aims to use machine learning algorithms to accurately classify the different fitness activities. Mainly, you will need to implement a multiclass classification algorithm and work on your data visualization and analysis skills.
One of the important application areas for machine learning is natural language processing. The next two projects will be related to this area, in the form of text summarization and text mining.
Summarizing a text shortens its body while maintaining its message and meaning. You can build an abstractive text summarizer that uses advanced natural language processing techniques to generate a new, shorter version that conveys the same information. You can build this project using Pandas, Numpy, and NTLK in addition to an unsupervised learning algorithm for word representation.
Text mining is the process of structuring and extracting useful information from unstructured data, which is 80% of all raw text data. When we mine text, we effectively transform it into a structured format, facilitating the identification of key patterns and relationships within datasets. If you want to dip your toes into some natural language processing, you can use these datasets to implement multi-level classification or to evaluate the performance of multi-label algorithms.
Music is a big part of everyone’s daily life. Often, people have different tastes in the music they listen to while they work, exercise, or just relax. One exciting project that you can build is a music genre classifier. This project’s idea is to automatically use one or more machine learning algorithms (such as multiclass support vector machine, K-means clustering, or convolutional neural networks) to automatically classify different musical genres from audio. Often this classification is done through the filtering of audio files using their low-level frequency and time-domain features.
Back to another natural language processing project. An idea that has long intrigued researchers and companies is the automatic recognition of handwritten characters. The idea behind this project is to model a neural network to detect & recognize handwritten characters. You can use the A-Z handwritten alphabet dataset along with Keras, TensorFlow, and Pandas to implement this project.
The Myers Briggs Type Indicator is a famous personality test that divides people into 16 different personality types. You will need to answer various questions, which the system then evaluates to determine your personality type. This dataset contains different information about the test that you can then use to evaluate the validity of the test design, analyze its results, and make predictions about the different personality types or categorizations of human behavior.
In most projects, the first step is often obtaining some data to analyze and apply algorithms. For example, you can use the Youtube-Comment-Scraper-Python library to fetch YouTube video comments and then use those to implement various sentiment analyses, hate-speech flaggers, and bot-detection projects. Using this library, you will learn how to implement an automated scraper which will help you focus on exploratory data analysis and feature engineering.
Mental health is an incredibly important topic of discussion. The ability to detect and recognize people’s mental health state can help save lives or vastly improve quality of life. If you want to build a project that feels important or if you have struggled with mental health issues before, you can use the Twitter dataset (or scrape recent Twitter data) to build a sentiment analysis that recognizes depression cues.
Last on today’s projects list is another one for the music lovers. This time we are not categorizing the music; we are going to generate it. Many songs today contain elements generated by computers. One approach to generating music is through the usage of deep learning or neural networks. If you want to try generating your own music, you can try MuseNet, or WaveNet, or use a dataset like the Maestro to classify and generate your own music.
Building a solid machine learning projects portfolio can make or break your chances of getting the role you’re applying for. Luckily, because machine learning is a practical field, there are a wide number of projects from which you can build your skills during your machine learning journey. For example, if you want to learn a new algorithm or concept, the best way is to build a project where you apply the rules and concepts of that algorithm. . Today, we went through 12 machine learning projects that you can build and add to your portfolio to make it stand out among the crowd and help you get your desired role. These project levels vary from beginner to advanced, so you are sure to find one that matches your current skills level, with additional ideas that will challenge you to grow.
You can also check out these helpful resources from Interview Query:
Classification Machine Learning Projects and Datasets - Great ideas for doing classification machine learning projects.
Machine Learning Course - See our new machine learning course, which includes sections on modeling, Python, and ML system design.
Interview Questions - 500+ real data science interview questions in product sense, data analytics, machine learning, and more.
Top 10 Regression Projects & Datasets - Ideas for more machine learning projects, using regression analysis.