Machine learning algorithm

If you’re getting into machine learning and data science, then you’ve probably already looked at online machine learning courses to build your theoretical knowledge. But machine learning is also about practical skills through relevant machine learning projects. That’s why we’ve put together these 11 machine learning project ideas for beginners to get you thinking and working like a machine learning expert.

What skills do you need for a machine learning project?

At its core, machine learning is a branch of data science, which means that anyone who wants to do a machine learning project should be familiar with the most popular data science programming languages.


Anyone in data science and machine learning will need to work with databases, and learning Structured Query Language (SQL) will give you access to databases all over the internet.


Python is one of the most popular programming languages for machine learning experts. Once you know Python, you’ll be able to work with endless libraries, tools, and datasets for your machine learning project.

JavaScript and Java ML

Many machine learning professionals are certified in JavaScript because of its flexibility and object-oriented nature. And Java ML is Java’s machine learning library, which is why you should understand the fundamentals of Java ML before starting on your machine learning project.


TensorFlow is a software library created specifically for machine learning projects. It uses both Python and C++ to combine ease of use with high performance and is especially useful for deep learning projects.

As you learn TensorFlow, you’ll find that there are plenty of ways for machine learning beginners to improve their skills through libraries, applications, and Java projects within TensorFlow.

Machine Learning, Data Science and Deep Learning with Python

Last Updated September 2022

  • 116 lectures
  • Beginner Level
4.6 (28,449)

Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks | By Sundog Education by Frank Kane, Frank Kane, Sundog Education Team

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11 Machine Learning Projects for Beginners

1. Activity detection through email and social media

One way to detect your daily activity is by looking at your email and social media activity. Use a machine learning algorithm to determine your daily routine — for example, when you wake up, when you exercise, when you eat, and when you go to sleep — based only on the times you use social media and send emails. When does activity stop?

If you want to challenge yourself more with this project, use your machine learning algorithm to put together a timeline of major life events. Can your algorithm detect when you changed jobs based on your email activity? When you went on vacation? When you traveled to another time zone?

2. Stock price predictions

Stock prices predictions make an ideal machine learning project because there’s so much publicly available data on past stock prices.

Develop a machine learning algorithm that predicts stock prices for a single company or a stock market index such as the S&P 500 or Nasdaq. While we don’t endorse or recommend using your algorithm for investing in the real world, many stock trading platforms offer virtual trading accounts where you can invest virtual money into real stocks and see how much money you would have made (or lost).

3. Wine quality prediction

If you like red wine, then this is the machine learning project for you. Given a variety of individual wine attributes (acidity levels, sugar content, alcohol content, density, and so on), predict the quality of the wine on a scale between 1 and 10. This machine learning project can be based on this open-source wine dataset provided by the University of California Irvine.

If you’re particularly into wine and want to give yourself a bigger challenge, try sampling some of the wines in the dataset to understand how different quality wines taste to you. Then try a new wine that’s not in the dataset. How would you rate the quality yourself, and how does your machine learning algorithm rate the wine? How do these compare to the wine quality as rated by professionals?

4. Iris flower classification

Many iris species can be distinguished by a combination of petal size and sepal size.

Using this open-source iris dataset, develop a machine learning algorithm to cluster iris specimens into one of three species based on petal length, petal width, and sepal length.

For an additional challenge, try working with just a portion of the dataset to train your machine learning algorithm. Then, use the rest of the data as input into your algorithm. How often does your machine learning algorithm correctly identify a new iris specimen?

5. Twitter sorting

One way to gauge public sentiment is to figure out what everyone is Tweeting about. Using natural language processing, create a machine learning algorithm that scrapes Twitter posts and determines which ones are more likely to talk about specific people, match particular themes, and so on.

If you or your friends use Twitter, you can give yourself a new challenge by using your algorithm on your own accounts. What does your machine learning algorithm pick up about your Tweet history? Are there times when you were into a new product? Can your algorithm detect when you picked up a new hobby or traveled somewhere based on your Tweets? And can your algorithm whether you enjoyed the new hobby or the place you visited?

6. Movie recommendations

Have you ever wondered how streaming services know which movies or shows you’re most likely to enjoy? A lot of it is based on movie ratings. Using this open-source dataset on movie ratings provided by GroupLens, develop a recommendation system to recommend movies to viewers.

For an added challenge, put in a list of your own favorite movies and ratings. Which movies are suggested by your recommender to watch next? Are they movies that you think you’d enjoy? Next, try this with your friends’ favorite movies. 

7. Sales forecasting

Retailers would give just about anything to see into the future, and while machine learning can’t predict future sales with 100% accuracy, it can do a pretty good job. In fact, one way Walmart recruits data scientists is by providing them with historical sales data to develop prediction models. And since the Walmart sales data is publicly available, you can do the same thing, whether or not you plan to apply to Walmart.

8. Titanic survival prediction

While we’ve all heard of the “women and children first” rule made famous after the sinking of the Titanic, the truth is that one’s chance of surviving the sinking depended on much more than gender or age.

Using real data about Titanic survivors, create a machine learning algorithm to predict what kinds of people would have survived on the Titanic.

For an added challenge, try testing your algorithm on yourself. What are the odds that you would have survived on the Titanic? What about your family and friends? Your favorite celebrities?

9. Loan Prediction

If you’ve ever applied for a mortgage or auto loan, it’s not always clear how they calculate your maximum loan amount. Use this dataset to build a machine learning model that predicts an applicant’s maximum loan amount.

If you’re looking for an added challenge, try applying for a loan based on your own attributes and characteristics. How big of a loan can you take out based on the results of your machine learning algorithm? How do the results compare with loans you’ve successfully applied for in the past?

10. Recognizing fitness activity

How does your phone or smartwatch know when you’re exercising? The answer, of course, is through machine learning. With this open-source dataset on fitness activity, create a machine learning model to recognize whether someone is sitting, lying down, or moving.

One way to give yourself more of a challenge is to develop a machine learning algorithm to detect activity based on combined actions. For example, can your machine learning algorithm tell when someone was walking around at home — which might involve a lot of stopping and sitting — versus when they were at a store — which might involve a lot of walking and standing activity.

11. Digitizing handwriting

If you use a tablet or other touchscreen device with a stylus, you may wonder how your device can recognize your handwriting as you take notes. The answer is with deep learning. Part of “training” the deep learning algorithm is to provide handwriting samples for given sentences or characters.

Now you can do the same thing. Use deep learning and artificial neural networks to convert a scanned, handwritten document into a digital text output. Your training dataset might include samples of the handwritten digits and letters as well as individual sentences (“The quick brown fox jumps over the lazy dog.”).

Once your algorithm is ready, try it with your own handwriting. Write a few words, then a sentence, then a paragraph. Does your algorithm recognize everything?

For an additional challenge, try setting up your deep learning algorithm to recognize connected handwriting, that is, cursive handwriting.

How do I start a machine learning project?

If you want to make the most of your machine learning project, it’s important to set yourself up for success by:

Understand the problem

The most important part of any project or experiment is to understand the problem that you’re trying to solve or the question that you’re trying to answer. From there, it’s possible to define a project goal. For example, suppose that you want to understand how customers feel about a product or service based on online reviews and social media posts. The problem is that it’s impossible for a human to go through every single relevant social media post in real time. That helps you define a goal for your machine learning project: to create a machine learning algorithm that does sentiment analysis (positive or negative) in real time and predicts trends for future product sentiment.

And as you work through your machine learning project, you need to always ask yourself, “How can this task or process help solve the problem and get me closer toward the project goal?”

Gather the right data

Once you understand the problem and have defined your machine learning project goal, the next step is to find the right data that will help you achieve your goal. If you don’t have access to proprietary data, then you have two alternatives. The easiest option is to find open-source data on the internet, though you’ll need to pay close attention to limitations on data use. Some open-source datasets can only be accessed for personal use or research purposes only.

The second option is to gather raw data directly by web crawling or web scraping. For example, if you want to use machine learning to predict the results of an election, then you may want to gather information from social media posts as well as news sites. Keep in mind that web scraping is its own skillset. You’ll need to take into account bias when assembling and labeling your data. If you’re not yet familiar with web scraping, there are plenty of web scraping courses online that guide you through the fundamentals.

Prepare your data

Once you have all the raw data you think you need, it’s time to sort, filter, and clean your data. This could involve data cleaning, which is eliminating data features that you don’t need, removing data points that don’t have enough information, eliminating duplicate or redundant values, or formatting text data for consistent capitalization and punctuation.

Data preparation can also involve data transformation, which is converting data from one form (for example, from a spreadsheet or database) to another form (for example, into a DataFrame in R or object in Python). Data transformation is particularly important with unstructured datasets such as image collections and free text.

What makes a good machine learning project?

Machine learning projects, like any data science project, take a lot of time and effort to do well. Most of the time is spent simply collecting and preparing data, and a lot of thought goes into developing machine learning algorithms that work for a particular dataset and for a particular purpose. The best machine learning projects help solve problems and answer questions that are worth all that time and effort.

As you think about problems that could be solved with data science and machine learning, the most important thing to ask yourself is, “Is this problem too difficult, too time consuming, or otherwise impossible for human beings to solve on their own?” If the answer is yes, then you have a potential machine learning project.

The next question to ask yourself is, “Do I have access to the data I need to explore the problem further?” If you’re working on your own, then you probably don’t have access to proprietary or sensitive data. That’s why it’s important to frame your machine learning problem in a way that could use open-sourced data or data that you could collect yourself with web crawling or web scraping methods.

What is deep learning?

Deep learning is a type of machine learning that involves artificial neural networks, or collections of functions and algorithms, that communicate with other inputs and outputs — the output of one artificial neuron is used as the input of another neuron. Deep learning networks use training data to adjust and fine-tune themselves to return outputs that match the training data.

If you’re not yet familiar with deep learning and artificial neural networks, there are many online deep learning courses to help you understand the fundamentals.

What is a good deep learning project?

The best deep learning projects include a good training dataset to help the deep learning algorithm learn and adjust the artificial neural network. What’s important in the training dataset is that there’s a roughly equal portion of different types of data. For example, a deep learning project that detects spam email would make a good deep learning project because it’s easy to assemble a training dataset that includes equal amounts of spam emails and non-spam emails. On the other hand, a machine project that involves detecting a rare disease based on symptoms and patient history would not make a good deep learning project because there would be too few instances of patients with the disease compared to patients who don’t have the disease.

How do I choose a deep learning project?

As you choose a deep learning project, it’s important to consider your training dataset. If you can put together a training dataset that includes equal proportions of the different outputs (for example, spam and non-spam emails), then your machine learning project is a good candidate for deep learning.

What field should I pick for my machine learning project?

Machine learning projects take a lot of time and work, which is why they should involve something that you enjoy. If you like sports, then it makes sense to work on a machine learning algorithm involving sport team performance predictions. If you’re more into business intelligence or marketing, then consider a machine learning project focused on predicting market trends or product demand.

Remember that machine learning experts and other data scientists are most successful when they bring domain expertise along with machine learning skills. Use your machine learning project to advance both your data science skills and domain knowledge and give yourself a double boost in your machine learning career. 

Ready to start your first machine learning project?

We know you’ve mastered the basics of machine learning, so the next step is to put your knowledge to practical use. We hope you enjoy these machine learning projects that test your skills, build your machine learning project portfolio, and help you advance in your machine learning career!

Page Last Updated: April 2022

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