Machine learning refers to the field of studying computer algorithms that improve by themselves through the use of data and experience. It is an application of artificial intelligence (AI) where a system learns from its experience and improves its performance without requiring much human intervention.
Over the years, machine learning has become one of the most popular sectors. Taking an applied machine learning course or certification can help you capitalize on this popularity and demand. It has tons of applications in various industries, including healthcare, finance, research, manufacturing, marketing, etc. Depending on its usage and study, we can divide machine learning into two fields:
- Theoretical machine learning
- Applied machine learning
Theoretical machine learning is the study and research of machine learning concepts and algorithms to understand their fundamentals and potential. On the other hand, applied machine learning refers to the study of machine learning applications and understanding their usage.
In the following article, we'll look at applied machine learning and explore its functions. You'll also learn how you can get familiar with this subject effectively and learn its various concepts quickly. Let's get started.
What is Applied Machine Learning?
Applied machine learning refers to using machine learning algorithms for solving data-related problems. This can include supervised models as well as unsupervised models. A supervised model is an algorithm that improves automatically according to the labeled training data and an unsupervised model is one that draws analyses and inferences from unlabeled data.
Generally, applied machine learning uses statistical techniques and algorithms to understand, manipulate, and categorize data. You can use machine learning in any problem that has nondeterministic elements, especially where it's necessary to manipulate and analyze large quantities of statistically generated data.
Deterministic problems are the ones where there's exactly one correct solution for a posed issue. An excellent example is the fizzbuzz interview question. Suppose the programmer has to create a program that prints the string 'fizz' for a positive integer divisible by 2, 'buzz' if the integer is divisible by 3, and 'fizzbuzz' if the integer is divisible by 6.
You don't need statistical algorithms to solve such problems, which is why you don't use applied machine learning in such cases. A lot of what you use in applied machine learning is based on the concepts that theoretical machine learning discovers.
Some of the important subjects that you'll study in applied machine learning are mathematics, statistics, probability, computer science, and programming.
Where Applied Machine Learning Works?
As we discussed previously, we use applied machine learning in non-deterministic problems. However, we can also use it in areas that don't have completely deterministic issues. There are a ton of areas where applied machine learning works. They include the following:
- Search engines
Our smartphones are heavily reliant on machine learning. From the face-lock, which identifies your face to unlock the one, to the phone's AI assistant, all of these technologies use machine learning algorithms to operate.
The most sophisticated use of ML technology is in search engines. Google, Bing, and other search engines use their Machine Learning algorithms to generate the list of the most relevant and useful web pages for a search query.
There are many applications of machine learning in the transport industry. For example, manufacturers use ML algorithms to find the most optimal routes for transporting their goods and supplies. Cab hailing services such as Ola and Uber also utilize them to determine optimal fare prices and distance estimation.
Finance companies and institutions are some of the biggest users of ML technology. They use machine learning algorithms to analyze investments, their past performance, and relevant factors to determine if they are worth investing in. They also use ML in risk analysis and fraud detection.
The marketing sector uses machine learning algorithms in various ways. The most popular ML application in the sales and marketing sector is chatbot technology. Chatbots are software solutions that imitate human conversation. They help companies in automating a large chunk of their customer service activities.
Prominent E-commerce websites such as Amazon and Flipkart use machine learning algorithms to create recommender systems. These recommender systems analyze a user's buying history, clicks, and other relevant data to generate a list of product recommendations for them. This helps them provide personalized recommendations to their users.
How to learn applied machine learning?
As you can see, the field of applied machine learning is quite vast with various subjects and applications. The optimal way to study applied machine learning is by taking a course in this field. That's because a course would provide you with a structured curriculum and guidance from experts. Both of these benefits make it easier for you to study and comprehend the various concepts present in this field.
Taking a course would also help you manage your time properly. You should pick a machine learning course that suits your skill levels and requirements. In the current pandemic-ridden world, it would be best to take an online course in this subject as it would let you study without interruptions and at your own pace.
The average pay of a machine learning engineer in India comes out to be ₹7,01,354 per year. That's not all. Your pay in this field can go up to ₹20,00,000 per year. Companies offer such lucrative salaries to machine learning professionals because there's a shortage of skilled talent in the industry. (Source)
That's why now is the perfect time to pursue a career in applied machine learning. You should definitely start your pursuit in this field by taking a course in applied machine learning and getting certified. It would boost your chances of advancing your career tremendously.
Applied machine learning is the field of studying and utilizing the applications of machine learning. Recently, it has become one of the most popular sectors due to its versatility. Applied machine learning focuses on solving non-deterministic problems and problems that aren't completely deterministic.
To learn applied machine learning, you should focus on taking an applied machine learning course. It would help you learn and understand the different concepts of this field properly.