#Startups #MachineLearning #AI #ML #Business
Today, many people may have heard about Machine Learning (ML), but they do not know what excatly it is, how is it working for a business or which type of business-related problem it can solve.
Machine learning in business helps in ehnacing business scalability and improving business operations for companies across the globe. Artificial intelligence tools and numerous ML algorithms have gained tremendous popularity in the business analytics communities. Factors such as growing volumes, easy availability of data, cheaper and faster computational processing, and affordable data storage have lead to a massive Machine learning boom!
What is Machine Learning?
Simply put, ML is all about understanding, mostly hidden, data and statistics and then mining meaningful insights from this raw dataset. The analytical method that uses algorithms can help solve intricate data-rich business problems. Machine Learning is a branch of Artificial Intelligence (AI) that is revolutionizing the workplace. It refers to the process of developing self-learning algorithms by way of simple data input. The goal with Machine Learning is to increase accuracy. It does this by optimizing the performance of the task from experience gained with running through related datasets.
Major benifits of Machine Learning for Business
1. Predict customer behavior
Machine Learning is being used by companies all over the world to predict customer behavior and convert the predictive insights into prescriptive insights to increase customer base or offer them better services. By looking at purchasing patterns and browsing through purchase histories, retail companies can offer the best-customized product or service to individual customers and improve demand forecasts.
2. Detecting spam Detecting spam
Machine Learning in detecting spam has been in use for quite sometime. Previously, email service providers made use of pre-existing, rule-based techniques to filter out spam. Howerver, spam filters are now creating new rules by using neural networks to detect spam and phishing messages.
3. Eliminates manual data entry
Plagiarized and incorrect data are one of the biggest issues faced by the organizations today. By incorporating Machine Learning businesses can considerably steer clear of any errors cuased by manual data entry. Machine Learning technology make these processes by using the discovered data. Therefore, the organizations can save time and manpower and utilize it to improve the business.
4. Image recognition
Image recognition is also know as the computer vision and it has the capability for producig symbolic and numeric information from images and other high-dimensional data. This involves Machine Learning, Data Mining, Pattern Recognition and Database knowledge discovery. Here Machine Learning plays a very crucial role and is used by numerous comapnies from varioys industries including automobiles, healthcare and others.
5. Improved customer satisfaction
Machine Learning algorithms can easily collate all the information related to different customer pain-poins with product or service, and analyze it for any correlations of interest. In fact, it can even be programmed to forward customer queries to the correct departments, This ML-driven automation significantly reduces customer problem-solving wait times and provides faster solutions. Both of these aspects lead to improved customer satisfation, which help build a loyal client base as well as strengthen referrals.
6. Enhacing cyber security
One of the most difficult problems, cyper-security can be solved using Machine Learning algorithms. Organizations can use Machine Learning to increase their cyber-security and detech newer threats easily and quickly.
7. Product recommendations
In e-commerce, ML algorithms can be used to motivate product purchase. Maching with a large product inventory, ML can be used to identify hidden patterns and group similar things together. These products can then be suggested to customers.
8. Financial analysis
Machine Learning now plays an essential part in many phases of the financial eco-system like portfolio management, algorithmic trading, loan endorsing, and fraud indentification. Future applications of ML in finance will include Chatbots and other interactive interfaces for customer services, security and sentiment analysis.
9. Medical prediction & treatment
The healthcare sector is like goldmine of data and more the data better the Machine Learning model. If applied well in the pharma and medicine sector ML could lead to better diagnosis of diseases, personalized treatment, improved efficiency of research and clinical trials, smart health records, outbreak prediction and better control measures.
10. Detect network intrusions
Besides predicting complex customer behaviors, data mining can also be used to predict patterns in network intrusions and accordingly eliminating them. An intrusion detection systems screens the network traffic while looing for any malicious activity in the form of an attack or unauthorized access. Analysis of this traffic can bring out patterns to be better equipped in the future to catch hold of the intrusions. Since they will be based on analysis, these detections will be more accurate and speedy.
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