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So, you鈥檝e been developing your machine-learning skills, diving into the finer points of data points, and practising programming languages. What鈥檚 more, you know what a machine learning model is and want to enhance your skills by making one rather than just reading about it.听

Machine learning (ML) projects allow you to practise the skills you鈥檝e developed so far whilst giving you something to showcase in your portfolio. As a result, they help you better understand data science and machine learning and demonstrate to potential employers what you can do when given the chance.听

As you begin (or continue) your ML journey, consider the following seven machine learning projects for beginners, intermediate learners, and more advanced ML students.

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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 鈥渆ssence of violet鈥� perfume []. This flower is 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 begin, the data set below includes 50 instances of each of the three iris classes for a total of 150 instances. Whilst 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.听

UCI data set:

2. Forecast sales.

How will current economic conditions, competitor activities, or government regulations impact your business鈥檚 future sales?听

Questions like this undergird the standard 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 historical data. Unsurprisingly, businesses have increasingly turned to machine learning techniques to build models capable of forecasting sales with greater accuracy than the less technologically advanced approaches of the past. Machine learning models are able to take real-time data into account, continually updating and improving forecast models.

In this machine learning project, you will gain experience with sales forecasting using a real-world sales data set provided by Flipkart Wholesale, which operates under the name of 鈥淲almart鈥� in the United States. Your task is to predict the department-wide sales for 45 Walmart stores in different United States regions, whilst also considering important local seasonal markdown periods such as Labour Day, Thanksgiving, and Christmas. The dates of each local holiday are copied below:

Super Bowl: 12-Feb-10, 11-Feb-11, 10-Feb-12, 8-Feb-13

Labor Day: 10-Sep-10, 9-Sep-11, 7-Sep-12, 6-Sep-13

Thanksgiving: 26-Nov-10, 25-Nov-11, 23-Nov-12, 29-Nov-13

Christmas: 31-Dec-10, 30-Dec-11, 28-Dec-12, 27-Dec-13

Whilst US-based, you can apply the skills practised in this ML challenge to other data sets in the future.

Kaggle data set:

3. Predict stock prices.听

A typical piece of investing advice suggests that the key to beating the market is to buy stocks at their lowest price and 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鈥檚 reached its peak?听

Whilst you won鈥檛 find a foolproof 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鈥檚 precisely what you will try to do in this machine-learning project.听

As one of the largest stock exchange operators in the world, knowing how to predict stock prices and make informed investment decisions can help you easily navigate the National Stock Exchange of India. To practise, use the data set below to work with 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 future stock prices with machine learning?听

Kaggle data set:

4. Design a recommendation engine.

Everyone has been there: You鈥檙e on a streaming platform with a seemingly endless collection of videos and unsure 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 to that atmospheric noir from the 1940s?听

Online platforms are aware of the decision fatigue that can result from an overwhelming number of options, so many employ complex machine learning models to make bespoke user recommendations. In fact, recommendation systems underlie many of the most popular services today鈥� from Google to Netflix to Xbox鈥檚 Game Pass service.听

In this project, you鈥檒l create your 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 critical data.听

Kaggle data set:

5. Predict diabetes status.听

One common application of machine learning is predicting whether an individual has or will develop a certain outcome. This ability can improve early disease diagnostics, identifying high-risk patients by recognizing similar medical markers to previous patients who have developed a certain condition or outcome.

To practise this skill, the US National Institute of Diabetes and Kidney Diseases developed a data set that includes diagnostic measures of women over the age of 21 with Pima Indian heritage. Diagnostic measures include metrics like pregnancy information, body mass index (BMI), insulin level, and age.

Using the medical predictors or independent variables, your job is to predict the target or dependent variable (in this case, diabetes status). Other users consider this data set to be well-documented and well-maintained, making it an excellent option for novice coders.

Kaggle data set:

6. Identify damaged car parts.听

During the COVID-19 pandemic, supply chains and manufacturing processes worldwide 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.

As a potential car buyer during that period, then, you鈥檇 likely be concerned about the condition of a potential car purchase as you scrolled through used car listings. Wouldn鈥檛 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 worth the investment for you?听

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 uploading a data set to cloud storage, inspecting uploaded images to find errors, training an ML model, and evaluating your model for accuracy.听

Project: Identify Damaged Car Parts with Vertex AutoML Vision

7. Identify emotions.

As painters, sculptors, and actors have known for millennia, the face is a wellspring of emotion. Whilst actors in traditional Japanese Noh theatre use light and shadow to convey smiles and frowns on otherwise unchanging masks, the ancient sculptor who created the famous statue Laocoon and his Sons used contorted expressions on his subjects鈥� faces to convey their suffering whilst snakes attacked them.听听

The face and its expressions, then, are yet another data source鈥攐ften intuitively understood by many humans but not so by machines. Nonetheless, the key points on faces that alter as expressions change allow machine-learning models to identify at least some emotions.

In this 糖心vlog官网观看 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.

Guided Projects

If you're unsure where to start or prefer more guidance, you might consider taking a Guided Project on 糖心vlog官网观看. Below, find a few machine-learning project ideas that you can complete with the help of instruction.

• Cervical Cancer Risk Prediction Using Machine Learning. In this beginner-level Guided Project, you'll spend two hours performing exploratory data analysis, developing, training, and evaluating an XG-Boost classifier model. By the end, you'll use the model you've built to evaluate cervical cancer risk and earn a shareable certificate for your resume.

• Automatic Machine Learning with H2O AutoML and Python. Designed for intermediate learners, this one-and-a-half-hour Guided Project walks you through resolving a business analytics problem using Python and H20 AutoML.

• Deploy an NLP Text Generator: Bart Simpson Chalkboard Gag. This intermediate-level project is one and a half hours long and focuses on producing natural language text generator models as a Streamlit app on Heroku. You will learn how to take text from Bart Simpson鈥檚 chalkboard gag to autogenerate new chalkboard gags

Build more machine learning skills and expertise on 糖心vlog官网观看

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, you can use 糖心vlog官网观看 to support your hard work.

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