Machine learning is broad and applicable in many fields. So you might get lost trying to find a foothold as a beginner. Nonetheless, taking up projects while learning helps you decipher your interests and focus on a specific path.
Additionally, it lets you familiarize yourself with the typical machine learning workflow.
Here, we’ll show you some of the best beginner project ideas that’ll help you dive deeper into the nitty-gritty of machine learning.
1. Loan Prediction
Many lending and banking apps now incorporate loan eligibility models. So this is an inspiring angle to start with if you’re interested in applying machine learning to your existing fintech knowledge.
You’re not likely to scale this up for app incorporation, though. But you’ll learn how most enterprise apps decide whether someone is eligible for a loan or not.
To start, you need a dataset containing some financial information. Leveraging the earning and spending trends in this data, you’ll then train your model to learn specific patterns and predict loan eligibility when it receives new information.
2. Sentiment Analysis
Playing around with sentiment analysis is a perfect idea, especially if you have a knack for written words.
If you’re confused, sentiment analysis involves text classification or clustering by a machine, usually into positive and negative perceptions.
As with many natural language projects, feature selection might be a bit challenging here as well. But analyzing sentiments in text often starts with text mining to study the patterns of the texts in question. This lets you figure the main features across your dataset that you can use as training criteria.
You can then use appropriate classification algorithms like the Naive Bayes or the decision tree to train your model. Ultimately, this project exposes you to the basic concepts of text manipulation and how spam detection works.
Python offers a ton of flexible algorithms and logic around sentiment analysis. So if you’re comfortable with Python, which is relatively easy to grasp, you can take a look at how to use the natural language processing toolkit.
3. Code a Logistic Regression Model
Logistic regression is a straightforward classification model perfect for beginners. As you may already know, it finds the probability of occurrence of discrete events.
You can start by working with datasets containing discrete values like “Yes” and “No,” or “Good” and “Bad.” Like other classification algorithms, logistic regression helps your machine encode these into readable values so it can predict appropriately.
And if you want to predict more than two possible outcomes, you can delve further into multinomial logistic regression. That said, Python’s scikit-learn might be a pretty handy tool for writing your model.
4. Image Recognition
Technologies like facial recognition and fake image detection might seem like magic. But when you get yourself engrossed in a DIY image recognition project, you’ll soon realize that creating one is easier than you might’ve thought.
Moreover, you have a pretty large handful of image processing machine learning libraries out there at your disposal. TensorFlow, for instance, offers versatile resources for image modeling.
And if TensorFlow is complex to narrow down, Keras, a part of the TensorFlow platform, is also a valuable tool you can leverage. Ultimately, a basic understanding of Artificial Neural Networks (ANN) is helpful for this project.
Your image recognition project, however, may range from fake image detection to image recognition algorithms.
Although it looks tedious at first, it becomes easier as you dive deeper. Plus, it gives you a solid understanding of deep learning concepts.
5. Cancer Classification and Prediction
Cancer classification is an interesting angle to look into, especially if you’re interested in applying your machine learning knowledge in medical fields like bioinformatics.
Your data would typically contain standard metrics for deciding whether a tumor is benign or malignant. You’ll then use this information to create a model that classifies new cancer cases into the appropriate category using the same metrics. Depending on how you intend to approach this, you can use a classification algorithm like the decision tree to inform the machine’s decision.
And if you want to add to the existing knowledge, you can even take your project deeper by delving into cancer prediction. Here, you can use algorithms like Support Vector Machines (SVMs) and Artificial Neural Networks (ANN) to achieve your goal.
6. Stock Price Prediction (Clustering)
The stock market is volatile and prices are based on a plethora of different factors. So, determining a profitable stock can sometimes be an uphill battle for investors.
Because you’re solving a financial-related problem and at the same time learning the basic concepts of machine learning, this project is well worth your time.
Your dataset should contain various stock information and how they’ve changed over time. Because it’s more efficient learning patterns, your model will use this information to predict whether a stock will fall or rise at a point in time. So this is also related to a time series analysis as your model will forecast future outcomes.
And thankfully, many tools are also at your disposal for this project. Facebook’s Prophet, for instance, is an open-source forecasting tool. You can use this with Python. But if you’re more comfortable with R, Prophet also supports R in a massive way.
7. Website Niche Prediction
This isn’t a very popular project for beginners. But you can take it if you like challenges and want to uncover the tools you can use to achieve your aim.
One of the setbacks that you might encounter with this project is where to get datasets. But once you figure out the information you need to solve the problem, you can scrape the data using this BeautifulSoup tutorial.
To make this work, you need to consider metrics like the headers on a webpage. Additionally, look out for frequently used sentences and keywords, as these are at least pointers to what a webpage is all about. So it means you must select your features carefully for reasonable accuracy.
8. Build a Recommendation System Using Python
You must have come across a recommendation system while browsing the internet or using apps like YouTube and Netflix. Most advertising systems on the internet also use it to filter ads that you see—and sometimes, it feels like the internet knows what you’re thinking.
In some cases, based on what you frequently search on the internet, a recommender might learn about your content preferences. It then uses this to recommend related content that it feels may interest you.
Yours may not be as complex. But you can build something pretty basic to get started. A product recommender, for instance, is an excellent starting spot.
To build a product recommender, for example, you need to gather data about products and people’s perceptions about them. These, of course, might include the number of positive and negative reviews, the product niche, the number of buys, and more.
9. Wine Quality Prediction
Wine quality prediction is one of the few beginner-centric projects. This is a classification problem involving categorizing wine into high and low-quality types.
For this, you can use classification algorithms like logistic regression or a decision tree to train your model. You can even use an ANN if you’re more interested in connecting every dot for decision-making.
Like every other machine learning project, this one exposes you to the core concepts of feature selection, correlation, label encoding, and more. Plus, it gives you a leveled playground with your data.
10. Build a Simple Machine Learning Algorithm
While, until this point, we recommended projects that use other algorithms, you can hard code a DIY algorithm from scratch using ML-friendly languages like C, C++, R, or Python.
Although this might sound a bit more challenging than the other tasks on the list, it’s a perfect project idea, especially if you want to know how built-in algorithms work and iterate through your data.
Of course, this doesn’t have to be a complex algorithm. You can look up the mathematical concept behind a simple linear regression, for instance, and use this to create an applicable, reusable, and installable algorithm.
11. Fake News Detection
It’s no news that fake and authentic news flies around the web. But both have unique pointers and attributes that put them in either category.
Because you’re dealing with plain texts, finding a unique descriptive pattern for both news types might give you headway into achieving your goal. You should select your feature carefully to avoid overfitting or underfitting your model.