What is Machine Learning?

Nowadays, it is difficult to find a person who has never heard of machine learning. However, laypeople’s vague idea of what this term implies usually involves particularly advanced walking robots from science fiction films learning to do complicated tasks. In reality, it is not that scary; indeed, anyone can learn machine learning, and this article will break down the fundamentals of this intriguing field. 

What is Machine Learning?

Let's begin by answering the main question of this article: what is the definition of machine learning?

Machine learning (ML) is a branch of artificial intelligence (AI) that provides a set of algorithms enabling machines to learn patterns and trends from available historical data to predict already-known outcomes on the same data, and then to generalize their inferences beyond the training data set, improving the accuracy of their predictions without being explicitly programmed.

In other words, we divide the dataset of historical data into training and test sets, feed the training set into a selected machine learning algorithm, set algorithm parameters (called hyperparameters), if necessary, and build a model to predict the already-known values. Then, we run the same model on a test set, for which the outcomes are known to us but not to the model so that we can estimate the accuracy of the model predictability on the new data. This enables us to decide if we can rely on this model for making predictions on truly unknown data, or if we have to adjust it first or even change the algorithm.

Machine Learning vs AI vs Deep Learning

Machine learning is often confused with artificial intelligence or deep learning. Let's take a look at how these terms differ from one another.

While machine learning is a subfield of artificial intelligence, AI is a vast field of computer science concerned with enabling machines to simulate the process of human thinking, understanding, reasoning, planning, learning from mistakes, expanding knowledge, gaining experience, communicating, performing various tasks, and even demonstrating creativity. Thanks to AI technologies, computers have become intelligent mechanisms capable of learning without human assistance.  They are then able to make data-driven decisions and act accordingly. Some examples of AI applications are self-driving cars, robot surgeons, and digital assistants.

Deep learning, on the other hand, is a subfield of machine learning dealing with algorithms based essentially on multi-layered artificial neural networks (ANN) that are inspired by the structure of the human brain. Unlike conventional machine learning algorithms, deep learning algorithms are less linear, more complex and hierarchical, capable of learning from enormous amounts of data, and able to produce highly accurate results. Language translation, image recognition, and personalized medicines are some examples of deep learning applications.

The Most Common Use Cases of Machine Learning in the Industry

Machine learning has entered literally every branch of industry and changed its overall flow. We can witness numerous applications of machine learning in various spheres such as banking, marketing, sales, healthcare, logistics, linguistics, education, insurance, and manufacturing. Some popular real-world use cases of machine learning from these spheres include:

• Credit card fraud detection
• Customer segmentation
• Chatbots
• Spam filters
• Customer sentiment analysis
• Disease prediction
• Customer churn rate prediction
• Recommendation systems
• Investment risk analysis
• Search engine optimization (SEO)
• Social media marketing (SMM)
• Price optimization
• Taxi route optimization
• Market basket analysis
• Supply chain visibility
• Medical image analysis

The list above is by no means exhaustive. It comes as no surprise that machine learning algorithms are broadly applied by both giant companies (such as Google, Facebook, Amazon, Netflix, and Uber) and small businesses.

Types of Machine Learning

There are three main types of machine learning algorithms, each represented by various techniques applicable for different use cases: supervised learning, unsupervised learning, and reinforcement learning.

Supervised learning involves teaching a model on a labeled training set of historical data from which it learns the relationships between input and output data. It then estimates the accuracy of predictions on a test set with the output values known in advance, so that the model can be used later to make predictions on unknown data. Some of the more commonly used supervised learning algorithms include linear and logistic regressions, support-vector machines (SVM), decision tree, and Naive Bayes classifiers. Examples of typical use cases are spam filters and price prediction. To learn more about supervised learning, take the Supervised Learning with scikit-learn course of DataCamp.

Unsupervised learning deals with identifying the intrinsic structure of the data without being given a dependent variable, detecting common patterns in it, classifying the data points based on their attributes, and then, based on this information, making predictions on new data. The most popular algorithm of unsupervised learning is k-means. Examples of common real-world applications are customer segmentation and anomaly detection. If you want to expand your knowledge in unsupervised learning, consider the Unsupervised Learning in Python course at DataCamp.

Reinforcement learning implies an algorithm progressively learning by interacting with an environment, deciding which actions can draw it nearer to the solution, identifying which ones can drift it away based on its past experience, and then performing the best action for that particular step. The principle here is that the algorithm receives penalties for wrong actions and rewards for correct ones so that it can figure out the optimal strategy for its performance. Reinforcement learning is neither supervised nor unsupervised. It is used, for example, for game-playing engines (video games, chess, etc.). For more information, you can check out this Introduction to Reinforcement Learning tutorial.

Machine Learning Methods

Let's now take a look at the methods most commonly used in machine learning:

• Regression is a supervised learning method where an algorithm predicts continuous numerical labels based on the provided input variables. It works in the following way: the algorithm learns the relationships between one or more independent features and the dependent variable, establishes a function between them, and then uses it to make predictions on unseen data. Typical examples of regression problems are price and revenue predictions. The machine learning algorithms used for solving regression problems include linear regression, logistic regression, ridge regression, lasso regression, decision tree, and support-vector machines (SVM).

• Classification is a supervised learning method concerned with predicting categorical labels based on the provided input variables. A classifier takes each data observation, identifies to which of the predefined categories it belongs (based on its likeness with the rest of the data points), and labels it accordingly. Some common examples of classification problems are customer churn rate prediction, fraud detection, and cancer prediction. The algorithms used for classification problems include k-nearest neighbors (KNN), decision tree, random forest, and Naive Bayes.

• Clustering is an unsupervised learning method where the observations of a dataset are grouped into clusters according to their resemblance to some common characteristics. Unlike what happens in classification problems where the groups are predetermined by humans, these clusters are defined by the machine while learning the input data. In clustering problems, the data points in each cluster are similar among themselves and different from the observations in the other clusters. Typical clustering problems are grouping households or determining the optimal positioning of taxi vehicles. The most popular clustering algorithm is k-means; other algorithms include hierarchical clustering, spectral clustering, and agglomerative clustering.

• Natural Language Processing (NLP) method represents an overlap between machine learning and deep learning by making computer programs understand and analyze written or spoken natural human language. An NLP algorithm: 
  • Takes unstructured text data
  • Converts it to a structured form
  • Searches for linguistic and contextual patterns inside it 
  • Categorizes those patterns 
  • Performs other cognitive language-based tasks
  • Extracts meaningful insights from the input data
    
    NLP algorithms are used, for example, to create chatbots, automatic translators, speech-to-text converters, and to conduct sentiment analysis. The algorithms used in natural language processing include support-vector machines (SVM), Bayesian networks, Markov chains, and neural networks.

• Artificial Neural Networks (ANN) are the basis of deep learning (a subset of machine learning) and consist of up to hundreds of layers of interconnected elements called neurons largely inspired by the structure of the human brain. The layers of an artificial neural network filter the data through them, apply an activation function to it, and make predictions at the output. The advantages of ANN are their flexibility when dealing with advanced machine learning tasks and the capability to work on huge bodies of data producing highly-accurate results. ANN can be used, for example, for language translation, voice recognition, or object detection.

Other machine learning methods include anomaly detection, transfer learning, active learning, and ensemble learning.

Primary Machine Learning Tools

There is an impressive spectrum of machine learning tools available. Without getting too technical, let's roughly divide them into three major categories and discuss each of them briefly:

• Machine learning as an online service. Such cloud services offer a full set of operations that cover the entire machine learning workflow, including fast building, training, deploying, scaling, comparing, and securely maintaining high-quality models on a unified AI interface. They are usually very flexible and adaptable for all levels of technical expertise and for all business needs. Thanks to an extensive toolkit of these platforms, we can use them to train models with minimum to no code or create more advanced models applying custom tools. The most notable and worth-knowing industry-leading giants among such online services are Microsoft Azure Machine Learning, Google Cloud AI Platform, Cloudera, IBM Watson, and Amazon Machine Learning.

• Open-source environments for performing machine learning tasks. These environments are free and accessible to anyone, they are constantly being developed, improved, and are well-supported by their communities. In these interfaces, we can work on machine learning projects using one or more programming languages. A certain level of proficiency is required. The most popular of such integrated software is RStudio for modeling in the R programming language and Jupyter Notebook, which supports many languages including Python and R.

• Specialized machine learning libraries. In any data science-oriented programming language, we can find a few libraries or packages specifically adapted to solve machine learning problems. Such libraries can differ greatly as they were developed by different companies, offer different approaches to the same tasks, or have varying degrees of user-friendliness  They all, however, have the same purpose: to provide users with the necessary integrated toolkit tailored for building, deploying, and managing machine learning models using a wide range of methods. These libraries come with detailed and exhaustive documentation explaining the implementation of each machine learning method, the commands and syntax to be used, mandatory and optional parameters, illustrative examples, and more. The most common machine learning libraries are scikit-learn, Keras, Theano (Python), TensorFlow, PyTorch (Python and C++), caret, mlr3, kernlab (R), mlpack, OpenNN (C++).

What is a Machine Learning Engineer and What Do They Do?

A machine learning engineer is a professional that is familiar with both software engineering and machine learning and combines these skills and knowledge to create and design programs that enable computers to self-learn and build predictive models.

While the exact responsibilities of a machine learning engineer depend on the company size and the project type, there are still some general tasks that are required for this role, including: 

• Designing and developing pipelines to automate the machine learning workflow
• Sourcing and extracting relevant datasets 
• Performing data quality check  
• Conducting statistical analysis
• Building machine learning frameworks
• Deploying machine learning models
• Monitoring machine learning models in production 

What are the Benefits of a Career in Machine Learning?

You are already on your way if, after reading up to this point, you are considering a career in machine learning. To prove that it is an excellent choice, here are two primary advantages of entering this sphere:

• Machine learning engineer is a very well-paid, privileged, and highly in-demand profession with an ever-increasing number of job postings all over the world. You can see it for yourself by looking at specialized career websites such as Indeed, SalaryExpert, or DataCamp Jobs. For example, here you can find various statistics, including the average base salary, for this role in the USA.
• It gives you endless opportunities to learn, grow, solve a great variety of tasks, and broaden your professional horizon in the most innovative, cutting-edge technology.

How to Get Started in Machine Learning

At this point, you may be curious as to what steps a beginner should take to start a lucrative career in machine learning. In a nutshell, here are the main landmarks that you will encounter on your journey "from zero to hero" in attaining your goal:

• Learn machine learning
• Practice your skills and build your portfolio of projects
• Create a professional resume
• Maintain your online visibility and network with other machine learning enthusiasts and specialists
• Start applying for machine learning engineering jobs

While each of the above steps deserves an entire article alone to go into the details, it is important to begin with an overview. Each journey starts with the first step, which can often appear more daunting than the rest of the journey.  

In order to find the right learning program from a vast sea of options, you should consider those that require minimum effort, time, and money, especially if you are changing your career and not a fresh college graduate. While earning a master's or bachelor's degree in machine learning might seem alluring, keep in mind that it would take 2-4 years to complete the entire program–a substantial financial investment that would still not guarantee you a job upon completion. Instead, a faster, more efficient, and economic way to get there is to opt for an online boot camp which offers:  

• a well-balanced machine learning engineer curriculum with the essential theory explained at the beginner level
• a lot of practical exercises to master your skills
• various portfolio-ready projects based on real-world data
• the ability to learn whenever you want from wherever you are. 

If this sounds interesting to you, consider the comprehensive online career tracks, Machine Learning Scientist with Python and Machine Learning Scientist with R at DataCamp, where you will learn with real-world data and acquire all the necessary skills to land your first job in machine learning.

If you want to learn more about the typical duties of a machine learning engineer and how to get started in this sphere, you may find this article useful: How to Become a Machine Learning Engineer.

Conclusion

This article has covered many aspects of machine learning. We started with the definition of what it is and how it is different from artificial intelligence and deep learning and looked at the most common applications of machine learning in the modern industry. We then familiarized ourselves with some basic technical details related to machine learning: the various types, the most commonly used methods, and major tools. Finally, we considered different career aspects of machine learning, including the main responsibilities of a machine learning engineer, why this profession is so attractive nowadays, how to get started in this sphere, and how to select the best program to become a highly-skilled and job-ready machine learning professional.

You are now ready to take the first step on your machine learning journey – start learning.