Top 10 the most interesting projects of Machine Learning

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10. Sales forecasting

It a common task performed by many organizations, mostly used in retail and e-commerce sectors. Earlier it used to be involved in a manual intense process of using spreadsheets that require an inputs from various levels of an organization. This approach introduced Bias and was generally not accurate especially during first week of a quarter. But machine learning involvement helped to discover the factors that influence sales in an organization and estimate the amount of sales that it is going to have in the nearest future. It allows you to estimate future sales volume and in particular it tells you how much will be selled in the future period in what market and what price. It promotes facilities to make informed business decisions by predicting short term and long term predictions. Apart from predicting sales this method is valuable to get more insights of how to relocate and manage work forces, recourse and cash flows. You can use Linear Regression, Classification and Regression Trees . The biggest retail companies, Walmart already implemented machine learning to manage the market sales and predict what will bring the biggest income.

9. Cancer Tumour Detection

Machine learning is widely used in the health care system for detecting the disease that might occur. Medical imaging is used in healthcare to diagnose patients with cancer diseases. ML can provide more information that leads for a better decision on patient diagnoses and improvement to overload in health care services. ML can process huge datasets that can give clinical insights as does the body have cancer cells and in which stage they are, we can define the stage of tumor and predict with which speed it will develop. Machine learning also play a big role in discovering new drugs and improve old one.in such sector mostly used Convolution Neuron models, k-nearest neighbors and k-means clustering. Healthcare companies such as LEONI and Cigna corp. So these area especial getting popular during the COVID-19 when scientist trying to figure out how to prevent in future such massive viruses.

8. Fraud Analysis

Fraud detection is mostly used in the banking and financing sector. The number of transactions increasing rapidly at the same time criminals are getting more tricky to break secure systems or trick authentication processes. Data scientists have been successful in solving these problems with the help of Machine Learning and predictive analytics. Automatic secure scripts help to prevent the fraud situations. Fraud analysis uses anomaly detection, logistic regression, k-nearest neighbors and deep learning to study payment methods, authentification, and common transactions that users make. One of the first pioneers of using ML in finance is J.P.Morgan.

7. Machine Translations

We all know about Google Translate allowing us to use it to easy our tasks. The technology behind that machine translation. Machine translation uses sequence to sequence learning, input most of the time represents the sequence of characters of one language which is translated to a sequence of characters of another language.Mostly recurrent neural network used for hosting such applications for constant learning and impowing translation.

6. Recommender Systems

Recommendation system is a type of information filtering system that is used to predict reting or priority that users will give to something (e-commerce, retail and entertainment companies). Recommender systems use machine learning algorithms to suggest products, tv series, movies based on your interest.mst of the time companies search the way how to improve the sales and fulfill the needs of their customers. Such companies as Amazon, Ebay, Netflix help users to discover new items and create the delightful user experience.

5. Sentiment Analysis

Sentiment Analysis is the process of using machine learning and natural processing techniques, so it would be easier to understand the sentiments and emotions of people though their social media posts. It uses computational linguistics and text analysis to systematically identify, extract, quantify and study effective subjective information. Sentiment analysis helps companies to identify customer emotions and attitude to product.Sentiment analysis not just focus on polarity such as positive, negative or neutral but also on feelings and emotions such as feelings, anger, happiness, sadness, interest, not interest. Intel, IBM, Twitter are the companies that most commonly use sentiment analysis.

4. Captionbot

Machine learning can help you to generate the textual description for an image or a video. It is an easy task for a human but quite challenging for the machine how to translate and explain images for instance. Deep learning models were replaced by more simple solutions for generating the most common captions for the images and videos.Microsoft has created it is own caption bot, so you can upload an image or the url of the image and it will give you a textual description.Automatic image text-caption generation software can be build using recurrent neural networks and long-short term memory networks.

3. Music Generation

Music is the composed language of communication, many brilliant musicians composed musical pieces that are both creative and deliberate. Now it is possible for the machine to learn the notes, structure and patterns of music and start to produce the music on its own. Music 21 is a python toolkit used for computed musicology, it allows to teach the fundamentals of music theory, generate music examples and study music. The toolkit provides a simple interface to acquire a musical notation of MIDI (musical instrument digital interface). Additionally it allows us create note and call objects so we can make our own MIDI files easily. Mostly it uses long-short time memory, deep learning helps to create your own musical masterpieces. Companies that use such techniques for producing AI driven music are: Amper Music,Weav and so on.

2. Image Coloring

Automatic colorization of back and white images has been subject to much researches within the computer vision and machine learning communities. Image coloring is a process of taking an input of gray scale of black and white image and then producing an output of colorized image that represents the tons and semantic colors of the initial image. Colorizing back imagies with deep learning has become an impressive showcase for the real world application of neural networks in our lives. Autoencoders and computational neural networks are mostly used for coloring.

1. Object Detection

Object detection is the computer vision technique that helps to detect objects such as cars, buildings, numbers etc.. Objects detection cases used in video recognition, self-driving cars and criminal cases, mostly objects recognizable in images or videos. Object detection has been applied widely in video surveillance and self-driving cars. First we are getting an image as an input and dividing it into few parts that will further be classified as separate images. Mostly in such cases used convolution neural networks . Google Tensorflow library provides its own API for image detection.

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• ai ml projects