Machine learning is becoming more and more mainstream today. Until a few years ago, self-learning programs were exclusively a topic for universities, research institutions, and some technology companies, but today they are increasingly being used in normal products and solutions. Our everyday and business life is increasingly determined by intelligent programs that learn from data and generalize what has been learned.
Speech recognition on cell phones such as the iPhone or Google cell phones, for example, is largely controlled by machine learning algorithms - just like spam filters in PCs and notebooks or facial recognition when managing photos. We are often in contact with learning systems without knowing it - for example with personalized online advertising. And more and more companies are recognizing the value of machine learning when it comes to optimizing their business and saving costs.
To put it casually, machine learning is the art of making a computer do useful things without specifically programming it to do so. To put it more precisely, machine learning is the acquisition of new knowledge through an artificial system. Like a human being, the computer independently generates knowledge from experience and can independently find solutions to new and unknown problems.
To do this, a computer program analyzes examples and uses self-learning algorithms to try to identify certain patterns and regularities in the data. The goal of machine learning is to intelligently link data with one another, recognize relationships, draw conclusions, and make predictions.
In principle machine learning is similar to human learning. In the same way as, for example, a child learns that certain objects can be seen in pictures, a computer can also "learn" to identify objects or differentiate between people. To do this, the learning software is first fed with data and trained. For example, the programmers tell the system that one particular object is "a dog" and another is "not a dog".
The learning software constantly receives feedback from the programmer, which the algorithm uses to adapt and optimize the model: With each new data set, the model becomes better and can ultimately clearly distinguish dogs from non-dogs.
Machine learning helps people work more efficiently and creatively. For example, they can use machine learning to organize and manipulate their images faster. With machine learning, you can also leave boring or time-consuming work on the computer. Learning software can independently scan, save, and file paper documents such as invoices.
Above all, self-learning machines can take on very complex tasks for humans - such as the detection of error patterns or possible damage in production (predictive maintenance). Even with the detection of cancerous tumors in medicine and with therapy recommendations, self-learning programs now help - and often outperform the best human experts. This ability to process complex relationships between the input and output of large amounts of data is one of the main advantages of machine learning.
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