More and more products are now being developed using artificial intelligence. To avoid being left on the sidelines of progress, managers must understand how the robot’s “brains” work
Artificial intelligence (AI) and machine learning technologies have been used for many years, but now the intensity of their use has increased significantly. For example, machine learning is being actively implemented in telecommunications, retail, marketing and e-commerce. But many still do not fully understand what it is.
Machine learning involves the system processing a large number of examples, during which it identifies patterns and uses them to predict the characteristics of new data. In other words, this is the process of giving AI ml courses “consciousness”, the ability to remember and analyze.
Machine learning use cases
The use of machine learning has touched many areas in our lives. Let's look at the most striking examples of the use of computer intelligence:
Voice assistant Alisa. This system, without human help, will always find the answer to any question of interest, turn on music, answer the call and much more.
Selecting relevant advertising in browsers is not an easy process for a person, but it is quick for a computer. The system will analyze the viewing data and select what a particular user will like. The same system is used on various marketplaces - products that a person viewed or purchased are offered.
Chatbots in various dialogues. For example, many banks use bots to solve customer problems. It's convenient and fast.
Data security: Machine learning models can identify data security vulnerabilities before they become breaches. By analyzing past experiences, machine learning models can predict future high-risk actions so that risk can be mitigated proactively.
Finance: Banks, trading brokers and fintech firms use machine learning algorithms to automate trading and provide financial advisory services to investors. Bank of America uses chatbot Erica to automate customer support.
Healthcare: Machine learning is used to analyze massive medical data sets to speed up the discovery of treatments and cures, improve patient outcomes, and automate routine processes to prevent human errors. For example, IBM Watson uses data mining to provide doctors with data they can use to personalize patient care.
Fraud detection: AI is used in the finance and banking sector to autonomously analyze large numbers of transactions to identify fraudulent activities in real time. Technology services company Capgemini says fraud detection systems using machine learning and analytics reduce fraud investigation time by 70% and increase detection accuracy by 90% .
Retail: AI researchers and developers are using machine learning algorithms to develop AI recommendation engines that make relevant product suggestions based on shoppers' past choices, as well as historical, geographic and demographic data.
Facial recognition in the subway will help identify violators or criminals in a huge mass of people. Ordinary observers cannot cope with this task. But a fast-learning machine will do this job without any problems.
What do you need for machine learning (ML)?
For those interested in training, there are several requirements to be met in order to be successful in this field. So, there are the main points you need to know about the machine learning course. These requirements include:
Basic knowledge of programming languages such as Python, R, Java, JavaScript, etc.
Average knowledge of statistics and probability.
Basic knowledge of linear algebra in the ml course. In a linear regression model, a line is drawn through all the data points, and that line is used to calculate new values.
Understanding Calculus.
Knowledge of how to clean and structure raw data into the desired format to reduce the time required for decision making.
Machine learning courses from AI Eeducation are the best choice!
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