7 Machine Learning Projects to Build Your Skills

Written by Coursera • Updated on

Machine learning projects are a great way to practice your skills and develop your portfolio. Test yourself and prepare for a future career as a machine learning expert with these engaging projects.

[Featured Image]:   A man wearing a blue plaid shirt and glasses, is standing in front a whiteboard, going over a machine learning project.

So, you’ve been developing your machine learning skills, diving into the finer points of data points, and practicing programming languages. What’s more, you know what a machine learning model is and want to get your hands dirty actually making one rather than just reading about them. 

Machine learning (ML) projects allow you to practice the skills you’ve developed so far while giving you something to showcase in your portfolio. As a result, they not only help you better understand data science and machine learning but also can demonstrate to potential employers what you can really do when given the chance. 

To help you get started, here are seven machine learning project ideas suitable for both beginners and more advanced ML students. 

1. Identify irises

Irises influenced the design of the French fleur-de-lis, are commonly used in the Japanese art of flower arrangement known as Ikebana, and underlie the floral scents of the “essence of violet” perfume [1]. They’re also the subject of this well-known machine learning project, in which you must create an ML model capable of sorting irises based on five factors into one of three classes, Iris Setosa, Iris Versicolour, and Iris Virginica.

To help you get started, the data set below includes 50 instances of each of the three iris classes for a total of 150 instances. While one of the classes is linearly separable, the other two are not. Your task is to create a model capable of classifying each iris instance into the appropriate class based on four attributes: sepal length, sepal width, petal length, and petal width. 

UCI data set: UCI Machine Learning Repository Iris Data Set

2. Forecast sales

How will the changing seasons, shifting demographics, or government regulations impact a business’s future sales? 

Questions like this undergird the common business practice of sales forecasting, in which a business estimates the number of products or services it will sell in the future based on relevant historic data. Unsurprisingly, then, businesses have increasingly turned to machine learning to build models capable of forecasting sales with greater and greater accuracy than the less technologically advanced approaches of the past. 

In this machine learning project, you will gain experience with sales forecasting using real-world sales data provided by Walmart. Your task is to predict the department-wide sales for 45 Walmart stores located in different regions, while also taking into account important seasonal markdown periods such as Labor Day, Thanksgiving, and Christmas. 

Kaggle data set: Walmart Recruiting – Store Sales Forecasting

3. Predict stock prices 

A common piece of investing advice intones that the key to beating the market is to buy stocks when they’re at their lowest price and to sell them at their highest. In other words: buy low, sell high. But, how do you know when a stock is at a low point and when it’s reached its peak? 

While there is no fool-proof way to answer this question, one approach is to develop a machine learning model that can try to predict stock price fluctuations using historical data. That’s exactly what you will try to do in this machine learning project. 

The data set below includes high-quality data for US-based stocks and exchange-traded funds (ETFs) on the NASDAQ, NYSE, and NYSE MKT. How might you try to crack the ever-elusive question of predicting stock prices with machine learning? 

Kaggle data set: Huge Stock Market Data Set

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Conversation with Andrew Ng: Tensor flow sequences.

4. Design a recommendation system

We’ve all been there: you’re on a streaming platform with a seemingly endless collection of videos and are unsure of what to watch. Do you try that anime series set in the not-so-distant future or that cheesy romantic comedy clearly from the early aughts? Or, should you finally get around to that atmospheric noir from the 1940s? 

Online platforms are aware of the decision fatigue that can result from an overwhelming amount of options, so many of them employ complex machine learning models to make bespoke recommendations for users. In fact, recommendation systems underlie many of the most popular services today – from Google to Netflix to Xbox’s Gamepass service. 

In this project, you’ll create your very own recommendation system using data collected from the movie-recommendation service MovieLens. Created by 138,493 users, the Movielens data set includes over 20 million ratings and 460,000+ tags for 27,278 movies. See what you can do with this important data. 

Kaggle data set: MovieLens 20M Dataset

5. Predict house prices 

Buying a home is often one of the most important – and expensive – milestones in a person’s life. As a result, the real estate and housing market are some of the most significant within the U.S. (and global) economy. 

While financial value certainly isn’t everything when purchasing a home, many people want to know if a specific home will be a good investment in the long term. For example, how much might your home be worth if you sell it today or after you renovate it? 

With so much publicly available real estate data out there, predicting housing prices is a natural fit for a machine learning project. In this self-guided lab by Google Cloud Training, you will learn how to use machine learning to predict housing prices by building an end-to-end machine learning solution using Tensorflow 1.x and AI Platform. You’ll also learn how to leverage the cloud for distributed training and online prediction, in general, so you can use your skills on future projects. 

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Predict Housing Prices with Tensorflow and AI Platform

This is a self-paced lab that takes place in the Google Cloud console. In this lab you will build an end to end machine learning solution using Tensorflow + ...

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Tensorflow, Machine Learning, Google Cloud Platform

6. Identify damaged car parts 

During the COVID-19 pandemic, supply chains across the world came to a halt as countries and workplaces shut down in an attempt to stop the spread of the virus. As a result, the automotive industry struggled to manufacture new cars, which led to a 45-percent price increase in demand for used cars between 2020 and 2021 [2]. 

As a potential car buyer during that period, then, you’d likely be concerned about the condition of a potential car purchase as you scrolled through used car listings. Wouldn’t it be great if you could use machine learning to identify the damage to different car parts, so you could know if the purchase would be with the investment for you? 

In this interactive project by Google Cloud Training, you will do just that as you use machine learning vision to identify damaged car parts. Designed for intermediate machine learning practitioners, this quick project will walk you through the process of uploading a data set to cloud storage, inspecting uploaded images to ensure there are no errors, training an ML model, and evaluating your model for accuracy. 

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Identify Damaged Car Parts with Vertex AutoML Vision

This is a self-paced lab that takes place in the Google Cloud console. Vertex AI brings together the Google Cloud services for building ML under one, ...

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Average time: 1 month(s)

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Skills you'll build:

Machine Learning, Google Cloud Platform, Cloud API, Vertex AI

7. Identify emotions

As painters, sculptors, and actors have known for millennia, the face is a wellspring of emotion. While actors in traditional Japanese Noh theater use light and shadow to convey smiles and frowns on otherwise unchanging masks, the ancient sculptor who created the famous statue Laoocon and his Sons used contorted expressions on his subjects’ faces to convey their suffering as they’re attacked by snakes. 

The face and its expressions, then, is yet another source of data – often intuitively understood by many humans but not so with machines. Nonetheless, the key points on faces that alter as expressions change provide the possibility for machine learning models to identify at least some emotions.

In this Coursera project, you will use Artificial Intelligence (AI) to predict emotions based on different facial expressions. In this three-hour guided project, you will build and train a deep learning model based on Convolutional Neural Network and Residual blocks using Keras with Tensorflow 2.0 as a backend.

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Emotion AI: Facial Key-points Detection

In this 1-hour long project-based course, you will be able to: - Understand the theory and intuition behind Deep Learning, Convolutional Neural Networks ...

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Deep Learning, Machine Learning, Python Programming, Artificial Intelligence(AI), Computer Vision

Reinforce your learning (and gain new skills)

Machine learning is a growing field with a wide range of applications. Whether you are just starting out or are already well acquainted with the field, Coursera has a course for you. 

Stanford University and Deeplearning.AI’s joint Machine Learning Specialization can help you master fundamental AI concepts and develop practical machine learning skills in a beginner-friendly, three course program by AI visionary Andrew Ng. DeepLearning.AI’s Deep Learning Specialization, meanwhile, teaches intermediate-level course takers how to build neural networks, CNNs, and RNNs. 

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Machine Learning

#BreakIntoAI with Machine Learning Specialization. Master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, 3-course program by AI visionary Andrew Ng

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Average time: 3 month(s)

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Skills you'll build:

Decision Trees, Artificial Neural Network, Logistic Regression, Recommender Systems, Linear Regression, Regularization to Avoid Overfitting, Gradient Descent, Supervised Learning, Logistic Regression for Classification, Xgboost, Tensorflow, Tree Ensembles, Advice for Model Development, Collaborative Filtering, Unsupervised Learning, Reinforcement Learning, Anomaly Detection

Related articles 

Article sources

1. Britannica. “Iris, https://www.britannica.com/plant/Iris-plant-genus.” Accessed June 23, 2022. 

2. The New York Times. “‘The Market is Insane’: Cars Are Sold Even Before They Hit the Lot, https://www.nytimes.com/2021/07/15/business/car-sales-chip-shortage.html.” Accessed June 23, 2022. 

Written by Coursera • Updated on

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