Machine Learning: Top 10 Machine Learning Projects And Their Applications

Introduction 

You are welcome to blueguard.ng lecture series on machine learning, in today's post, we will be discussing the top 10 machine learning projects that are being widely used in the industries and which can help machine learning enthusiasts get knowledge and experience in it. 

So if you haven't subscribed to this blog yet, please make sure to do it now to stay connected with all the latest updates from Blueguard. Without any further delay, let's begin.

10. Sales Forecasting 

At number 10, we have sales forecasting. Sales forecasting is a common task performed by many organizations. It is commonly used in retail and e-commerce sectors. Earlier, it used to involve a manually intensive process of using spreadsheets that require inputs from various levels of an organization.

This approach introduced bias and was generally not accurate, especially during the initial few weeks of a quarter. But machine learning can help you discover the factors that influence sales in an organization and estimate the amount of sales that it is going to have in the near future. 

It allows you to estimate future sales volumes. In particular, it identifies how much of a product will sell during a certain period, in what market, and at what price. 

It promotes and facilitates the making of informed business decisions by predicting short-term and long-term performances. Apart from predicting sales, this method of forecasting is also valuable in deriving insights into how workforces, resources, and cash flows should be managed by an organization. 

You can use linear regression, classification and regression trees, Bayesian models, as well as time series analysis for forecasting sales. Retail giant Walmart uses machine learning for predicting its sales for different products across various regions. 

9. Cancer Tumour Detection 

Machine learning is widely used in the healthcare industry for predicting diseases before they occur. With machine learning, healthcare service providers can make better decisions on patients' diagnosis and treatment options, which leads to the overall improvement of healthcare services. 

It aids analysts to process huge and complex medical datasets and then helps for getting clinical insights. Machine learning and deep learning models are being developed to detect cancer tumor cells in the human body.

You can find out if the tumor is malignant or benign. Diabetic retinopathy is another disease that can be cured using machine learning. Medical imaging is being popularly used for discovering patterns in diseases. 

Machine learning is also playing a huge role in discovering and developing new drugs. ARIMA model, convolutional neural networks, k-nearest neighbors and k-means clustering are majorly used for disease detection.

Healthcare companies such as Lunit and QuantGene use AI and machine learning to detect and diagnose diseases in patients. 

8. Fraud Analysis 

At 8, we have fraud analysis. Fraud detection is another popular application of machine learning used in the banking and finance sector. Fraud management has been painful for the banking and finance industry. 

The number of transactions has increased due to a plethora of payment channels such as credit or debit cards, net banking, payments from mobile apps, etc. At the same time, criminals have become adept at finding loopholes. As a result, it is getting tough for businesses to authenticate transactions. 

Data scientists have been successful in solving this problem using machine learning and predictive analytics. Automated fraud screening systems powered by machine learning can help businesses in reducing fraud. 

Fraud analysts use anomaly detection models, logistic regression, k-nearest neighbors and deep learning to study the identity of the person, orders, payment methods, locations and network that was used. The famous banking and finance organization J. P. Morgan uses machine learning techniques to detect fraudulent transactions.

7. Machine Language Translation 

Next, at  number 7, we have machine translation. We all know about Google Translate and love to use it to ease our tasks. The technology behind Google Translate is machine translation that can instantly translate between 100 different human languages as if by magic.

It is even available on smartphones and smart watches. Machine translation uses sequence-to-sequence learning and is a subfield of computational linguistics that is focused on translating texts from one language to another.

In a machine translation task, the input already consists of a sequence of symbols in to another language. Mostly recurrent neural networks such as long short-term memory networks or LSTMs are used for creating applications that support machine translation. 

6. Recommender Systems

At number 6, we have recommender systems. A recommender system or recommendation system is a type of information filtering system that is used to predict the rating or priority a user would give to something. 

E-commerce, retail and entertainment companies are leveraging the power of data and boosting sales by implementing recommender systems on their websites and mobile applications. 

Recommender systems aim to predict users' interests and recommend product items that might be interesting and exciting for them. Companies using recommender systems to focus on increasing sales as a result of very personalized offers and enhanced customer experience.

Recommendations typically speed up searches and make it easier for users to access the content they are interested in and surprise them with offers they would have never searched for.

Companies such as Amazon, Netflix, eBay, LinkedIn and Pandora leverage recommended systems to help users discover new and relevant items like products, videos, jobs, movies and music and also create a delightful user experience while driving revenue. 

Now before moving ahead, let's take a pause and recap on the top projects that we have covered so far. 

So at number 10 we had sales forecasting, at number 9 we had cancer tumor detection, at number 8 we discussed fraud analysis followed by machine translation at number 7 and at 6 we looked at recommended systems. If you have enjoyed reading this post so far, please subscribe to this blog  to get all the latest updates on top trending technologies. 

5. Sentiment Analysis 

Now moving ahead, at number 5 we have sentiment analysis. Sentiment analysis is the process of using machine learning and natural language processing techniques to analyze customer sentiments based on their emotions. 

It uses computational linguistics and text analysis to systematically identify, extract, quantify and study effective states and subjective information. 

Sentiment analysis allows businesses to identify customer sentiment towards products, brands or services in online conversations and feedback. 

Sentiment analysis models focus not only on polarity that is positive, negative or neutral but also on feelings and emotions such as angry, happy, sad etc and even on intentions for example interested versus not interested.

Sentiment analysis can also be carried out from tweets and Instagram posts to understand the mood of the public towards a brand or foreign event like general elections in a country. Intel, IBM and Twitter are few companies using sentiment analysis for their business. 

4. Caption Bot

Up next we have CaptionBot. Machine learning and deep neural networks can help you generate textual descriptions for an image or a video. It is an easy problem for a human but very challenging for a machine as it involves both understanding the content of an image and how to translate this understanding into natural language. 

Deep learning methods have replaced classical methods and are achieving state of the art results for the problem of automatically generating descriptions called captions for images and videos. 

Microsoft has created its own CaptionBot where you can upload an image or the URL of an image and it will give you the textual description. CaptionAI is another such application that suggests a perfect caption and best hashtags for a picture. 

Automatic image caption generation software can be built using recurrent neural networks and long short-term memory networks. 

3. Music Generation 

Music is the composer's language of communication. Many amazing musicians throughout history have composed pieces that were both creative and deliberate. 

Now it is possible for a machine to learn the notes, structures and patterns of music and start producing music on its own. 

Music21 is a python toolkit used for computer aided musicology. It allows you to teach the fundamentals of music theory, generate music examples and study music. The toolkit provides a simple interface to acquire the musical notation of MIDI files which stands for Musical Instrument Digital Interface. 

Additionally, it allows us to create note and chord objects so that we can make our own MIDI files easily. Using WaveNet architecture and long short-term memory networks you can generate music automatically. 

 Companies like Amper Music, MuBot and Juke Deck produce smart music powered by AI-driven algorithms that use both machine learning and deep learning. 

2. Image Colouring 

At number two, we have image coloring. Automated colorization of black and white images has been subject to much research within the computer vision and machine learning communities. 

Image colorization is the process of taking an input grayscale of a black and white image and then producing an output colorized image that represents the semantic colors and tones of input. For example, an ocean on a clear sunny day must be plausibly blue. It can't be colored pink or brown by the model. 

Colorizing black and white images with deep learning has become an impressive showcase for the real world application of neural networks in our lives. Auto encoders and convolutional neural networks are mostly used for automated image colorization. 

1. Object Detection 

Finally, on the top of the list we have object detection. Object detection is a computer vision technique that aims to detect objects such as cars, buildings and human beings to name a few. The objects can generally be identified from either pictures or video feeds. 

Object detection has been applied widely in video surveillance and self-driving cars. Object detection locates the presence of an object in an image and draws a bounding box around that object. 

First, we take an image as input then, we divide the image into various regions. We will then consider each region as a separate image and pass all the region's images to the convolutional neural network and classify them into various classes. 

Google's TensorFlow library provides its own object detection API to identify various objects in an image. Detectron is Facebook's AI research software system that implements state-of-the-art object detection algorithms. 

In the United States, the FBI uses facial recognition and object detection algorithms to investigate criminal cases. Google uses object detection for building its self-driving vehicles. Regions with convolutional neural network features or RCNN and mask RCNN models are mainly used for object detection. 

Conclusion 

With that, we have covered the top 10 machine learning projects and their applications. I hope it was interesting and informative. If you liked it, please let us know in the comments section. Also, do don't forget to comment all your doubt, we will answer you as soon as possible.

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