Machine learning is broad and applicable in many fields. So you might get lost trying to find a place as a beginner. Still, taking on projects while learning helps you decipher your interests and focus on a specific path.
Plus, it lets you familiarize yourself with the typical machine learning workflow.
Here, we’re going to show you some of the best beginner project ideas that will help you dive deeper into the details of machine learning.
1. Loan forecast
Many lending and banking applications now incorporate loan eligibility models. So this is an inspiring angle to start with if you want to apply machine learning to your existing fintech knowledge.
However, you are not likely to increase this for the incorporation of apps. But you will learn how most business applications decide whether or not someone is eligible for a loan.
To get started, you need a dataset that contains financial information. By taking advantage of the income and expense trends in this data, you will 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 great idea, especially if you have a knack for written words.
If you are confused, sentiment analysis involves classification of text or grouping by machine, usually into positive and negative perceptions.
As with many natural language projects, selecting features can also be a bit tricky here. But the analysis of sentiment in the text often begins with text exploration to study the patterns of the texts in question. This allows you to determine the main characteristics of your data set that you can use as training criteria.
You can then use appropriate classification algorithms like Naive Bayes or 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 are comfortable with Python which is relatively easy to understand, you can take a look at how to use the natural language processing toolkit.
3. Code a logistic regression model
Logistic Regression is a simple 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 that contain discrete values such as “Yes” and “No” or “Good” and “Bad”. Like other classification algorithms, logistic regression helps your machine code them into readable values so that it can predict appropriately.
And if you want to predict more than two possible outcomes, you can dig deeper into multinomial logistic regression. That said, Scikit-learn from Python could be a pretty handy tool for writing your model.
4. Image recognition
Technologies like facial recognition and fake image detection can seem magical. But when you immerse yourself in a DIY image recognition project, you’ll quickly find that creating one is easier than you might think.
In addition, you have a fairly large amount of machine learning libraries for image processing. TensorFlow, for example, offers versatile resources for image modeling.
And if TensorFlow is complex to refine, Keras, which is part of the TensorFlow platform, is also a valuable tool that you can take advantage of. Ultimately, a basic understanding of artificial neural networks (ANNs) is useful for this project.
However, your image recognition project can range from false image detection to image recognition algorithms.
Although it seems tedious at first, it gets easier the deeper you dive. Plus, it gives you a solid understanding of deep learning concepts.
5. Classification and prediction of cancer
Cancer classification is an interesting angle to consider, especially if you want to apply your machine learning knowledge to medical fields like bioinformatics.
Your data usually contains standard measurements for deciding whether a tumor is benign or malignant. You will then use this information to create a model that categorizes new cancer cases into the appropriate category using the same metrics. Depending on how you plan to approach this, you can use a classification algorithm like the decision tree to inform the machine’s decision.
And if you want to enrich the existing knowledge, you can even deepen your project by immersing yourself in cancer prediction. Here you can use algorithms like Support Vector Machines (SVM) and Artificial Neural Networks (ANN) to achieve your goal.
6. Share price prediction (cluster)
The stock market is volatile and prices are based on a multitude of different factors. So, determining a profitable stock can sometimes be an uphill battle for investors.
Because you are solving a financial problem while learning basic machine learning concepts, this project is well worth your time.
Your dataset should contain a variety of information about stocks and how they have changed over time. Because these are more efficient learning models, your model will use this information to predict whether a stock will fall or rise at any given time. So this also relates to a time series analysis, as your model will predict future outcomes.
And luckily, there are plenty of tools available for this project as well. The Facebook Prophet, for example, is an open source forecasting tool. You can use it with Python. But if you are more comfortable with R, Prophet also has massive support for R.
7. Website niche prediction
It is not a very popular project for beginners. But you can take it if you like a challenge and want to discover the tools you can use to achieve your goal.
One of the setbacks you might encounter with this project is knowing where to get the datasets. But once you find the information you need to fix the problem, you can recover the data using this BeautifulSoup tutorial.
For this to work, you need to consider metrics like the headers of a web page. Also, pay attention to frequently used phrases and keywords, as these are at least pointers to what a web page is. So this means that you need to select your characteristics 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 internet advertising systems also use it to filter the ads you see – and sometimes it feels like the internet knows what you are thinking.
In some cases, based on your frequent Internet searches, an advisor may know your content preferences. It then uses it to recommend related content that might be of interest to you.
Yours may not be as complex. But you can create something pretty basic to start with. A product advisor, for example, is a great place to start.
To create a product recommendation tool, for example, you need to collect data on products and people’s perceptions of them. These, of course, can include the number of positive and negative reviews, the niche of the product, the number of purchases, etc.
9. Prediction of wine quality
Predicting wine quality is one of the few beginner-centric projects. This is a classification problem involving the categorization of wine into high and low quality types.
You can do this by using classification algorithms like logistic regression or a decision tree to train your model. You can even use an ANN if you are more interested in connecting each point for decision making.
Like any other machine learning project, this one exposes you to the basic concepts of feature selection, correlation, label encoding, and more. Plus, it gives you a level playing field with your data.
10. Build a simple machine learning algorithm
While so far we’ve recommended projects that use other algorithms, you can hard code a DIY algorithm from scratch using ML compatible languages like C, C ++, R, or Python.
While it might seem a bit more difficult than the other tasks on the list, it’s a perfect project idea, especially if you want to know how the built-in algorithms work and navigate your data.
Of course, it doesn’t have to be a complex algorithm. You can research the mathematical concept behind a simple linear regression, for example, and use it to create an applicable, reusable, and installable algorithm.
11. Detection of fake news
It is not news that fake and genuine news is circulating the web. But both have unique pointers and attributes that put them in one or the other category.
Since you are dealing with simple texts, finding a unique descriptive template for both types of news can help you achieve your goal. You must select your feature carefully to avoid overfitting or underfitting your model.
For this one, you can start by looking at the Natural Language Toolkit Documentation, which has many resources you can use for word processing.
Learn by doing and become an expert in machine learning
Whether you’re self-taught, taking classes, or learning in school, you might lose your grip on the basics if you don’t apply what you learn.
Although machine learning seems complex, with personal projects you will gain a better understanding of the life cycle of real projects and the challenges involved. So, it is easier to navigate your way when you encounter real world issues.
You know the basics and are now ready to apply them. Get started with these Python projects!
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