Machine learning meaning, job salary, methods and application examples
How machine learning works?
Artificial intelligence is a buzzword nowadays, as most of the biggest companies use AI to bring innovation and assure profits. But how does it work actually? In fact, a concept called machine learning (ML) is what runs most of the tasks for artificial intelligence applications to operate precisely.
With machine learning, programmers don’t need to teach computers what it needs to do to finish tasks. Instead, computers can learn themselves using significant amounts of data. With machine learning, the software can behave like people or animals – learn from experience. To conclude, machine learning meaning is straightforward – machines can learn to do various tasks just like us.
Machine learning jobs
The simplest machine learning example is a basic linear regression. It predicts how variable behaves for different values of the argument. Important to realize, linear regression can give you a lot of insightful information for bivariate processes. You can calculate the linear equation and r-squared values even with your calculator if there are few data points. In contrast, if there are countless data points and you’ll have further use of the regression model, then Python or R-statistics come in use.
Looking at the following picture, we see how machine learning answers the trivial question: “Does longer experience means bigger salary?”.
In this case, we can use a machine learning model that predicts employees salary knowing how many years of experience they have.
Machine Learning is one of the essential tools in Data Science. By the same token, data scientists need to have all the corresponding skills to use and develop machine learning algorithms. Overall, machine learning jobs can be divided into those that need to create or use the ML algorithms.
Machine learning engineer usually needs to develop and deploy ML algorithms. The most used tools for this job are Python or R; Big data technologies; SQL databases.
A data scientist’s work specter is broader than pure ML engineer’s. In reality, it is often a universal employee that completes most of the data related tasks that a company needs. Besides creating machine learning models, this person also analyzes data and visualizes its insights. This type of employee usually is the only one working with data in a small or average company.
ML related job ads always have some job titles to it. Still, never judge those jobs only by the title name. Most HRs in big companies don’t know the difference between data science terms, so it is better to read all of the job ads that could be related to your skill-set. In some cases, a data analyst would be required to use or develop machine learning algorithms. Likewise, there can be times when the job description in the ad might be totally misleading, and the complete picture of what the employer wants from you can only be known in the job interview.
Salary - One of the best in USA
The average machine learning engineer salary in the USA is around 110k USD per year, according to payscale.com.
As it is one of the newest jobs in the market, most of the ML engineers are in the early stage of career (1-4 years of experience).
Yet, even for entry-level engineers, the average salary is 94k USD/year. Given that requirements for junior ML engineers or data scientists are very high, it can often mean that those entry-level engineers might have master’s or Ph.D. diplomas or great job experience as pure data analysts or mathematicians.
Types of machine learning
There are these three types of machine learning:
- Reinforcement learning.
In a supervised learning model, the algorithm learns on a labeled dataset, to generate reasonable predictions for the response to new data. Supervised learning classifies into regression and classification.
It is one of the main concepts in statistics and finance. Regression determines whether the relationship between dependent (Y) and independent variables (X) is strong enough, so it could prove to us that some insight is valid.
- Logistic regression;
- Decision trees;
- Random forest;
- Neural networks;
- K-nearest neighbors.
This type of machine learning group and interpret data based only on input data. Samples don’t have any labels prior.
The most common unsupervised learning type is clustering. It finds hidden patterns or groupings in data. Clustering has many techniques, including:
- fuzzy c-means;
- hidden Markov models;
- subtractive clustering.
The unsupervised learning techniques detect anomalies, find associations, and extract features with autoencoders.
The third paradigm of machine learning deals with how software agents ought to take actions in an environment to maximize some results. Unlike other supervised and unsupervised learning, it does not have any labelled input/output. In application fields, reinforcement learning may help us to explain how equilibrium may arise under bounded rationality.
Surprisingly, all of the taxi-booking, travel advisor, and route planning apps run on machine learning. Taxi-booking apps learn from customer experience ratings, supply-demand data to save your time, and give the best experience. Likewise, using prediction models and live data, apps like google can give you an approximate travel time for your trip taking usual peak hour traffic, other users GPS data into account. Similarly, Vacation planning software uses the same prediction system to recommend the cheapest flight tickets, hotel bookings, museums and more.
Money laundering detection
Countless bank officials let money laundering to strive for years as often it was not possible to check the significant amounts of transactions. At the same time, machine learning techniques of supervised and unsupervised learning became useful in identifying bank accounts that are possible money launders.
Now banks have data science specialists that improve machine learning models to find the most suspicious bank transfers using the least amount of employees for checking. For this reason, banks can use computers to find “a needle in a haystack” easily.
Social media solutions
Machine learning is a critical component of the popular social networks you use every day. Facebook uses advanced machine learning to do everything from content selection to face recognition in photos to user targeting in advertising. Instagram uses artificial intelligence to identify visual objects.
Similarly, LinkedIn uses machine learning to offer job recommendations, suggest people you might want to connect with, and serving you specific content in your feed.
Snapchat leverages the power of computer vision to track your features and overlay filters that move with your face in real-time.
These are just a few examples of how AI works behind the scenes to power features of the world’s most popular social networks.
Google Translate uses Natural Language Processing tools to learn itself from numerous languages big data. There are many techniques to teach translating model, including:
Agreement regulation: ML algorithms read text from left to right and then from right to left again to create a match. The end result is a consensus from both directions to eradicate errors.
Dual learning: Texts are translated from one language to another repeatedly until a natural and predict translation is delivered.
Deliberation Networks: Similar to dual learning, this method involves translating the same text over and over again to improve the final results.
Surveillance cameras recognition
Video files contain more information compared to texts and other media files. Because of this, extracting useful information from the automated video surveillance system has become a hot research issue. With this regard, video surveillance is one of the advanced application of an artificial intelligence approach.
Security companies need to identify humans from the videos saved. The facial pattern is the most widely applied parameter to recognize a person.
A system with the ability to gather information about the presence of the same person in a different frame of a video is highly demanding. There are several techniques of artificial learning algorithms to track the movement of a person and identifying them.
Mastering machine learning with Kaggle
Kaggle is the community-based website that could be called home to data science. It has courses to learn machine learning in general, various python libraries, deep learning, SQL, NLP, and so on.
With Kaggle, you can test your data science skills without a need to get a job. Kaggle users provide access to various exciting databases that can be analyzed using multiple machine learning methods. For example, we can use supervised or unsupervised learning to examine the following data:
- Titanic passengers death/survive data;
- House prices;
- CO2 emissions;
- Google labeling;
- Democratic debate transcripts;
- Trending youtube video statistics;
- Multidimensional taxi data in various cities;
- Crime rates;
- Cats classification;
- Russian troll tweets;
- Formula 1 races, …, and many more.
For every dataset page in Kaggle, there are many research versions with code from different users. Users post Python or R code to show their insights for the dataset. Also, there are many data science competitions in which you can compare your machine learning skills among fellow users of Kaggle. Winners of competitions are decided by other members that evaluate their peers. The best kagglers in competitions win as many as 10 thousand dollars that go to 1st place winners.
Machine learning with Knime Analytics
Knime Analytics is free software that lets you apply machine learning techniques on your datasets. Simple and intuitive design helps you not only learn machine learning and data science; it also carries the most demanding real-world tasks for problem-solving. It uses an object-node structure in which you can see a whole tree of used operations and inputs/outputs.
There are informative descriptions for most of the available nodes and commands what help you gain deeper understanding instantly.