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What is Machine Learning ML, and How Does It Work?

From public safety, website ad recommendation to fraud detection, machine learning powers computers to engage in activities that were in the past, only done by people. Ten most popular machine learning languages are Python, C++, Java, C#, JavaScript, Julia, Shell, R, TypeScript, and Scala. Python has become more popular compared to the other options because of its accessibility, diverse libraries such as PyTorch and TensorFlow, and ease of learning with tons of free online resources. Java is also a good option, especially due to the strong community around this language. R and C++ are the last spread options, though still attention-worthy programming languages.

How Machine Learning works?

The typical machine learning process involves three steps: Training, Validation, and Testing. The first step is to learn from the training set provided, the second step is to measure error, the third step involves managing noise and testing all the parameters. These are the basic steps followed and a very broad description on how machine learning works.

For a refresh on the above-mentioned prerequisites, the Simplilearn YouTube channel provides succinct and detailed overviews. The rapid evolution in Machine Learning has caused a subsequent rise in the use cases, demands, and the sheer importance of ML in modern life. Big Data has also become a well-used buzzword in the last few years. This is, in part, due to the increased sophistication of Machine Learning, which enables the analysis of large chunks of Big Data. Machine Learning has also changed the way data extraction and interpretation are done by automating generic methods/algorithms, thereby replacing traditional statistical techniques.

Machine Learning at present:

Based on its accuracy, the ML algorithm is either deployed or trained repeatedly with an augmented training dataset until the desired accuracy is achieved. Siri was created by Apple and makes use of voice technology to perform certain actions. Siri also makes use of machine learning and deep learning to function. A technology that enables a machine to stimulate human behavior to help in solving complex problems is known as Artificial Intelligence. Machine Learning is a subset of AI and allows machines to learn from past data and provide an accurate output.

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It can automate many tasks, especially the ones that only humans can perform with their innate intelligence. Replicating this intelligence to machines can be achieved only with the help of machine learning. You can also take the AI and Machine Learning Course in partnership with Purdue University.

What are the advantages and disadvantages of machine learning?

Today, wearable medical devices are already a part of our daily lives. These devices measure health data, including heart rate, glucose levels, salt levels, etc. However, with the widespread implementation of machine learning and AI, such devices will have much more data to offer to users in the future. With personalization taking center stage, smart assistants are ready to offer all-inclusive assistance by performing tasks on our behalf, such as driving, cooking, and even buying groceries. These will include advanced services that we generally avail through human agents, such as making travel arrangements or meeting a doctor when unwell. Blockchain is expected to merge with machine learning and AI, as certain features complement each other in both techs.

Using ML can help people discover the shows, music and platforms best suited to their unique preferences. For the consumer, picking up medication at the pharmacy often feels like a simple transaction, however, the situation behind the pharmacy counter is a different story. Pharmacists have to use information from doctors, patients, insurance companies and drug manufacturers in order to How does ML work prescribe medication effectively. Historically, this process involved many data silos and made it difficult for pharmacists to get a complete picture regarding patient information. Walgreens worked with Microsoft Azure to implement a machine-learning-powered back end system to improve their quality of care. Machine learning is a natural match for data-driven fields like healthcare.

Self-Supervised machine learning

This is especially important because systems can be fooled and undermined, or just fail on certain tasks, even those humans can perform easily. For example, adjusting the metadata in images can confuse computers — with a few adjustments, a machine identifies a picture of a dog as an ostrich. Machine learning is the core of some companies’ business models, like in the case of Netflix’s suggestions algorithm or Google’s search engine. Other companies are engaging deeply with machine learning, though it’s not their main business proposition. A 12-month program focused on applying the tools of modern data science, optimization and machine learning to solve real-world business problems.

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