As Artificial Intelligence (AI) continues to progress rapidly, it’s becoming increasingly important to achieve mastery over machine learning for a career path that shows a lot of promise for many. This is largely because both Artificial Intelligence and machine learning complement each other. Which is why, as a beginner in this field, it’s always best to work on different machine learning projects to up your skills.
Thanks to rapid technological advancements, machine learning is no longer a futuristic concept or something still in the works. It’s already here with us today, now. The most common concepts lie in speech recognition and virtual assistants like Alexa and Siri. These machines use learning programs to recite reminders, follow commands, and answer specific questions.
As a beginner, jumping into a machine learning concept can be quite overwhelming, especially if you don’t have the technical experience or expertise for the job. The whole process starts with picking a set data, studying the collected data, and using the collected data to determine which machine learning algorithm will best fit your dataset.
To that regard, here’s a comprehensive list of the top 10 easy machine learning projects to consider trying this year:
1. Human Activity Recognition with Smartphones
Most of today’s phones are designed to automatically detect activity around them when they’re about to be engaged in specific activities like cycling or running. This is machine learning at its best. To practice with this type of project, machine learning enthusiasts should use smartphones that can collect fitness activities like jogging or sprinting. These machine learning smartphones are equipped with inertial sensors that detect whenever its user is engaging in exercise activity so they can collect and record data collected.
Learners can then use this data to build classification models that can accurately predict future activities. Using these smartphones can also help you to understand how to solve complex classification issues.
2. Machine Recommendations with Movielens Dataset
Thanks to the internet and super-fast internet speeds, almost everyone today uses smart devices to stream movies and other television shows online. While all this is great, sometimes figuring out which movie or TV show to watch next can be a daunting task. But that’s about to come to an end with the recommendations machine learning process. Machine recommendations now make everything easier based entirely on your previous history and preferences.
The whole process is really simple and easy to understand. This is one machine learning process that’s really fun and easy to understand, especially for beginners. For new programmers, you can practice a little coding using either Python or R languages to form a recommendation pattern. This, plus data from Movielens Dataset, help make the whole process straightforward.
Currently, Movielens has over one million movie ratings from over 3,900 films. This makes it one of the best sources to use for such projects.
3. Music Recommendation System Project
As with movies, music also ranks as one of the best machine learning projects to consider undertaking across different domains. It’s very likely you are already familiar with one or two recommendation systems like Movie/Movie websites. This is usually common in major streaming services like Netflix or Spotify. Such platforms base their recommendations on movies or music you enjoy listening to and like. These are perfect examples of how machine learning is applied.
Starting a music recommendation project for a streaming site should also help you determine which new song or artist the target audience might like listening to. And the platform does this by using your previous choices to predict a pattern of your favorite song niche.
Your primary task should be to try and predict the app user’s chances of listening to your song repetitively within a given timeframe. Most mobile phone users are accustomed to repeating similar songs, especially ones from their favorite artists. This makes music recommendation system project one of the easiest machine learning projects for beginners.
4. Stock Price Predictions with Quandl
Yes! There’s such a thing as a stock price predictor. And you can actually make some invaluable stock investments if you find one that’s effective and easy to use. Stock prices predictor is a machine learning system set on learning about a company’s stock performance and uses the data it collects to predict the company’s future stock prices.
While this may be a tricky project to take on, it’s one of the most interactive and hands-on projects to try. However, the main challenge of taking this machine learning project lies more in the types of data sources you can use. These may include:
• Volatility indices
• Fundamental analysis
• Historical prices
• Technical analysis using indicators
• Global macroeconomic indicators
The benefit of analyzing and researching the stock market is that its feedback cycles are often much shorter. This makes it easier for you to validate your predictions with the data you have. Understanding how the typical cycle looks like always seems like the next best option if you already know market predictions.
5. MNIST Handwritten Digit Classification
Another upcoming deep learning process that might pique your interest is the MNIST dataset. This project demands the use of image recognition and automatic text generation. To start working in these areas, you must start with a simple and easy to use management program like the MNIST dataset. While it can be daunting to work with image data as opposed to flat relational data, picking up and solving the MNIST Handwritten Digit challenge should help you convert handwritten information into a digital sheet.
6. Sales Forecasting with Walmart Dataset
As one of the leading multinational retail corporation hypermarkets, Walmart dataset has sales information for 98 products across over 45 outlets. Its dataset contains sales per department, per store, and on a weekly basis, making it one of the most detailed datasets you can find. This machine learning project’s primary objective is to help Walmart forecast sales for every department on every outlet to help the hypermarket make better data-driven decisions. This helps ensure channel optimization and other inventory planning options for the most effective marketing processes.
7. Sentiment Analysis with Twitter
One of the most interesting applications to use for your machine learning projects is sentiment analysis. You can use many platforms with sentiment analysis like Facebook, Reddit, LinkedIn, since these platforms all offer easy-to-use APIs that are excellent for receiving data. And due to the consistent data collection format on the Twitter platform, sentiment analysis is the most preferred data machine platform. It’s the perfect project that runs the preferred data for your machine learning projects. It’s also always much easier to pre-process all tweets to your account, which mainly consisting of text, hashtags, and URLs.
The Twitter Application-Programming Interface (API) has several API libraries that you can use to integrate into your project. You can install the wrapper for Python using pip to install Python-twitter. However, you must watch out when using this API since excessive usage also gets you blacklisted on the social media platform. To that regard, Twitter provides appropriate guidelines on how you can avoid being rate limited. However, the Twitter streaming API can actually save you, especially if you need real-time data content.
8. Supervised Machine Learning using Iris Flowers Classification
This dataset is usually seen as the “Hello World” of machine learning and as the classic example of classification. The Iris Flowers dataset offers beginners an excellent introduction since it allows its user to learn to explore data and load it.
This project usually involves the identification of up to four different species of the Iris Flowers. The dataset also allows you to use a supervised learning algorithm since this data is usually labeled. However, there’s also unsupervised data, which means that you’ll be looking for hidden structures within the data since it will be unlabeled.
9. Predicting Wine Quality with Wine Quality Dataset
As you already know, the older your wine, the better it will taste. However, several other factors also go into wine quality certification other than age, which includes physiochemical tests like fixed acidity, volatile acidity, alcohol quality, pH, determination of density, and more. The primary goal of this machine learning project is to build an ML model that can predict the quality of wines. The wine quality dataset consists of over 4898 observations with over eleven independent and one dependent variable.
10. Breast Cancer Prediction
Another top machine learning project that uses a simple and easy to use dataset is for determining the likelihood of breast cancer. This machine learning project helps medical experts to determine whether a breast tumor is benign or malignant. As with other machine learning projects, several factors are taken into consideration here as well. These may include mitosis, the number of bare nuclei, and the lump’s thickness. It’s also an excellent way for beginner machine learning professionals to practice R programming.
Get Certified in Machine Learning
Arguably, there’s no better time to train in one of the most exciting fields doing rounds today; machine learning. Thankfully, you can now apply for comprehensive Machine Learning Certification training that covers all fundamentals to advanced techniques. Machine learning certification training provides an array of ML projects that include over 25 machine learning exercises. This machine learning course will also include over 44-hours of instructor-led training and other mentoring sessions from machine learning experts. Apply for your certification today and boost your career.
The Bottom Line
From the data collected above, it’s evident that machine learning is the new career path that shows a lot of promise in the technical world we live in today. And these ten machine learning projects should be perfect for beginners seeking a perfect blend of challenges that are perfectly set for engineers and data scientists.