Machine learning is a subset of AI (artificial intelligence) that allows a system to analyze a specified set of data and to learn from that data on its own without any instructions from the user. As you can imagine, machine learning can be very useful in business applications. Through the use of a machine learning project, businesses can use existing data to identify patterns that help them discover new trends or to predict future events. A machine learning project is different from a standard algorithm in that a standard algorithm is a set of instructions on what to do with the data provided. A machine learning project contains no such instructions — only the set of data that needs to be analyzed. With that in mind, the following are 12 different machine learning projects that you can use as teaching tools and that you can emulate for your business:
Sentiment analysis is a machine learning project that uses customer data to determine what the opinions and reactions of your brand are. It analyzes what people think of you (it’s why this machine learning project is sometimes referred to as emotional artificial intelligence). It’s capable of doing this via an automated process of natural language processing and text analysis.
Datasets can include online forums, news articles, comment sections on articles, customer reviews, surveys, and more, but sentiment analysis is most commonly used to comb through social media data on platforms such as Facebook and Twitter. Social media channels such as these contain massive amounts of data and are used by customers to express their opinions in the form of comments, likes, shares, and reviews, making them a goldmine for sentiment analysis.
Using historical data, you can identify patterns that can help you predict future sales. There are many companies that use machine learning in an attempt to forecast sales, so there are plenty of different machine learning projects available to look at.
A good machine learning project to check out as an example is the BigMart sales prediction ML project. This machine learning project made use of an unsupervised learning model to predict the sales of 1,559 products in ten different outlets for the following year. The dataset that was used contained sales data for those products in those outlets for the year 2013. The dataset also contained a number of different attributes for each product and store, which allowed BigMart to identify how those attributes played a part in generating sales. The solution to the machine learning project is available to the public.
Another dataset you may want to look at is the Walmart dataset. This dataset consists of 98 products from 45 outlets. Some of the data available in this machine learning project includes sales per store and sales per department on a weekly basis.
Stock market prediction won’t ever be 100 percent accurate — if it were, everyone would be rich. However, machine learning can be used to learn about how a company is performing and to predict future stock prices with some level of success. The challenge in predicting the stock market is that data is very granular. Many types of data factor into the price of stocks, including prices, fundamental indicators, volatility indices, global macroeconomic indicators, and more. While this can make forecasting quite difficult, one advantage is that financial markets have shorter feedback cycles. You’ll be able to validate predictions on new data relatively quickly.
Keeping all of this in mind, there are several machine learning projects to consider implementing. Quantitative value investing allows you to predict six-month price movements using fundamental indicators from your company’s quarterly reports. Forecasting allows you to build time series models on the delta between actual and implied volatility. Finally, statistical arbitrage allows you to find similar stocks based on a variety of factors (such as price movements) and look for periods when their prices diverge. There are several stock market datasets available to the public at Quantopian.com and Quandl.com.
Learning how to solve multi-classification problems is an important part of understanding how to use machine learning. The human activity recognition machine learning project consists of a smartphone dataset full of fitness activity recordings. These activities were recorded by strapping smartphones onto the waists of 30 subjects. It was through the inertial sensors of the smartphones that the data was captured. The idea behind the project is to build a classification model that will allow you to identify human fitness activities. There are six classifications — walking, walking upstairs, walking downstairs, sitting, standing, and laying down.
As much as you want to have complete trust in your employees, there’s always the possibility that there may be a bad apple or two among your ranks. Employees who are committing financial fraud can do some serious damage to your company. Unfortunately, fraud can be difficult to detect, even if the employees are being sloppy as they commit fraud. Fortunately, you can use machine learning to identify employees who may be committing fraud through the use of financial and email data by using the Enron dataset.
Enron is, of course, the primary example of a company that committed widespread corporate fraud. The Enron dataset contains over 500,000 emails generated by their ex-employees. The dataset contains not just the emails themselves, but also metadata about the emails (including the number of emails received and sent by each individual) along with financial information (such as salaries and stock options). Using the Enron dataset, you can train a machine learning algorithm as well as optimize it and create new features that will predict potential fraud or unsafe business practices using your own dataset of employee emails.
Using the Enron dataset, you can implement several different machine learning projects, including anomaly detection, which allows you to map the number of emails sent and received by the hour to detect irregular behavior leading up to a public scandal, and natural language processing, which allows you to analyze messages along with metadata to classify emails according to their purpose.
User-generated content accounts for a huge amount of potentially useful data. This data can be used to identify public opinion about your brand, discover new trends, identify customer opinions in general, and more. This data is mostly found on social media platforms, including Facebook, Twitter, WhatsApp, WeChat, YouTube, and Reddit.
One of the best ways to get started on using social media sentiment machine learning is by focusing on Twitter since Twitter provides not just data (the content of the tweets posted by customers), it also contains a lot of useful meta-data (such as locations, re-tweets, hashtags, and more). Using the Twitter dataset should give you an idea of some of the challenges associated with social media data mining as well as give you insights into classifiers. Building a model that classifies tweets as positive or negative is a good place to start when developing a new machine learning model.
Predicting quality is an important aspect of quality control and assurance. This is yet another facet of your business that can be improved through the use of machine learning. An excellent example of how machine learning can predict quality is the wine quality prediction machine learning project. The machine learning model in this project predicts the quality of wines by exploring their chemical properties, which include factors such as alcohol quantity, pH, volatile acidity, fixed acidity, determination of density, and more. The wine quality dataset includes 4898 observations along with 11 dependent variables and one independent variable.
Deep learning has allowed machine learning projects to identify actual speech. This is done by loading sound recordings into a neural network and training it to produce text. This can be incredibly useful for a number of reasons: the use of speech recognition can be employed to write emails quickly, monitor and transcribe phone calls, and perform automatic transcription of audio files. However, existing machine learning projects are only so accurate when it comes to speech recognition. Since speech can vary in speed, speech recognition is currently around 95 percent accurate, which is high but certainly not perfect. There are several machine learning projects you might want to explore for speech recognition, including Project DeepSpeech, which is an open-source project by Mozilla.
If you have an image you want to use for content or branding purposes, but you want to apply the style of a separate image, you can use machine learning to achieve this. For example, say you have a photograph of the NYC skyline but you want it to look like an Edward Hopper painting. Adding the style of an Edward Hopper painting to that photograph is known as a neural style transfer. Three images are required to perform a style transfer: a content image, a style reference image, and the input image. The VGG19 model is an example of a pre-trained image classification network that can be used for this project.
If your company has been around for several years, there’s a good chance you have physical documents that were filled out by hand. While digitizing these documents isn’t that difficult with today’s technology, searching through handwritten documents and extracting information from them is much more of a challenge. Machine learning can help make this possible. Businesses using machine learning to search through and extract handwritten data commonly use the MNIST (Modified National Institute of Standards and Technology) handwritten digit database, which contains 70,000 labeled images of handwritten digits.
One really exciting machine learning application is that of image recognition. Image recognition can be useful in countless ways. For example, businesses can implement face ID identification to identify and authorize employees. This can be used to help limit access to certain facilities, meeting rooms, and document storage areas. Image recognition models can also generate descriptions of images, which is particularly beneficial to blind users.
In order for a machine learning model to identify images, a very large amount of data is required for the model to learn from. It will need to learn from certain relationships and common features related to the objects in the images contained within its dataset. There are many datasets available online that can be used to train your model.
Automated translation isn’t just useful to the translation industry. The ability to instantly translate complex sentences from one language to another can also be incredibly useful for businesses who are dealing with foreign customers or business partners to greatly improve their customer experience. Automated text translation using neural machine translation is still not perfect, which isn’t surprising considering that human language is naturally ambiguous and flexible. This means that there is no single best translation of one language to another.
Older translation methods required the expertise of linguists along with a wide range of rules and exceptions. Statistical machine translation was an alternative method that used statistical models to learn how to translate text into different languages. However, statistical machine translation requires a pipeline of specialized systems. With neural machine translation, this isn’t necessary. Neural network models are capable of learning a statistical model that can be directly trained on source and target text.
It doesn’t matter what industry your company works in, there’s a very good chance that machine learning could benefit your organization. We’ve compiled some examples of different industries that commonly implement machine learning and how it benefits them:
The healthcare industry is adopting machine learning probably more quickly than any other industry. This is in part because of the heavy reliance on data. The healthcare industry collects massive amounts of patient data, especially now that wearable tech devices that contain built-in sensors are being used more commonly. Sifting through this data in search of different signs of disease and conditions is incredibly time-consuming. Machine learning makes this much more feasible and accurate. For example, Google has a machine learning algorithm that helps healthcare professionals identify cancerous tumors on mammograms and to diagnose Alzheimer’s disease a full decade earlier than doctors were traditionally able to.
Machine learning isn’t just limited to the stock market within the financial services industry. Lenders are using machine learning to improve their ability to assess the risk of borrowers. Insurance companies are doing the same to assess the risk factors of a person signing up for insurance.
Government agencies on every level (local, state, and federal) are turning to machine learning to improve their efficiency. For example, local governments are using machine learning to predict when potholes will form on their city streets so they can properly budget for repairs. The U.S. military is using machine learning to predict component failure on their tanks in much the same way.
The automotive industry is leveraging machine learning to improve operations and customer service in numerous ways. For example, a car manufacturer uses machine learning to forecast potential vehicle or part failures and reduce customer maintenance costs. Dealerships can also use machine learning to identify trends and patterns from datasets containing information about car ownership, allowing them to optimize their parts inventory. Some car manufacturers, such as Tesla, are also implementing autopilot features, which depend on machine learning to function. Its autopilot feature consists of eight cameras and 12 sensors to detect environmental conditions and to react accordingly. This is possible as a result of machine learning.
With the volume of customer data being collected by businesses in all industries, machine learning can use that data to greatly improve the customer experience. Machine learning can help to make sense of customer behaviors and to personalize their experience even further. For example, virtual voice assistants are growing in popularity. These assistants, which include Alexa and Siri, use machine learning to provide the best responses to queries from users based on their location as well as their past search history.
The retail industry is making great use of machine learning to forecast sales based on past sales. This not only gives them an idea of what kind of profits to expect but also how to manage their inventory and hiring practices more effectively. For example, by forecasting sales during the holiday season at different locations, they can stock up on the necessary inventory to meet the predicted demand as well as make seasonal hirings where appropriate.
The IT service industry is using machine learning in many different ways, most importantly in the area of cybersecurity. Hackers are getting more and more advanced every year, so machine learning is being implemented by IT security companies to detect anomalies in networks. For example, machine learning-based malware scanning programs are being used to identify characteristics of malware instead of searching for specific malware. This makes it more difficult for hackers to infiltrate a system since developing a brand new malware doesn’t mean that it will go undetected.
Machine learning can be instrumental in improving your company’s efficiency and effectiveness in a number of different areas if properly implemented. These 12 machine learning projects are great examples of how you can identify data trends and patterns and to automate and analyze transactions that were traditionally manually performed.
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