What Is Machine Learning and How Does It Work?

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What is Machine Learning?

For starters, machine learning is a core sub-area of Artificial Intelligence (AI). ML applications learn from experience (or to be accurate, data) like humans do without direct programming. When exposed to new data, these applications learn, grow, change, and develop by themselves. In other words, machine learning involves computers finding insightful information without being told where to look. Instead, they do this by leveraging algorithms that learn from data in an iterative process.

The concept of machine learning has been around for a long time (think of the World War II Enigma Machine, for example). However, the idea of automating the application of complex mathematical calculations to big data has only been around for several years, though it’s now gaining more momentum.

At a high level, machine learning is the ability to adapt to new data independently and through iterations.  Applications learn from previous computations and transactions and use “pattern recognition” to produce reliable and informed results.

How Does Machine Learning Work?

Machine Learning is, undoubtedly, one of the most exciting subsets of Artificial Intelligence. It completes the task of learning from data with specific inputs to the machine. It’s important to understand what makes Machine Learning work and, thus, how it can be used in the future. 

The Machine Learning process starts with inputting training data into the selected algorithm. Training data being known or unknown data to develop the final Machine Learning algorithm. The type of training data input does impact the algorithm, and that concept will be covered further momentarily. 

New input data is fed into the machine learning algorithm to test whether the algorithm works correctly. The prediction and results are then checked against each other.

If the prediction and results don’t match, the algorithm is re-trained multiple times until the data scientist gets the desired outcome. This enables the machine learning algorithm to continually learn on its own and produce the optimal answer, gradually increasing in accuracy over time.

The next section discusses the three types of and use of machine learning.

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What are the Different Types of Machine Learning?

Machine Learning is complex, which is why it has been divided into two primary areas, supervised learning and unsupervised learning. Each one has a specific purpose and action, yielding results and utilizing various forms of data. Approximately 70 percent of machine learning is supervised learning, while unsupervised learning accounts for anywhere from 10 to 20 percent. The remainder is taken up by reinforcement learning.

1. Supervised Learning

In supervised learning, we use known or labeled data for the training data. Since the data is known, the learning is, therefore, supervised, i.e., directed into successful execution. The input data goes through the Machine Learning algorithm and is used to train the model. Once the model is trained based on the known data, you can use unknown data into the model and get a new response.

In this case, the model tries to figure out whether the data is an apple or another fruit. Once the model has been trained well, it will identify that the data is an apple and give the desired response.

Here is the list of top algorithms currently being used for supervised learning are:

• Polynomial regression
• Random forest
• Linear regression
• Logistic regression
• Decision trees
• K-nearest neighbors
• Naive Bayes

Now let’s learn about unsupervised learning

The following part of the What is Machine Learning article focuses on unsupervised learning.

2. Unsupervised Learning

In unsupervised learning, the training data is unknown and unlabeled - meaning that no one has looked at the data before. Without the aspect of known data, the input cannot be guided to the algorithm, which is where the unsupervised term originates from. This data is fed to the Machine Learning algorithm and is used to train the model. The trained model tries to search for a pattern and give the desired response. In this case, it is often like the algorithm is trying to break code like the Enigma machine but without the human mind directly involved but rather a machine.

In this case, the unknown data consists of apples and pears which look similar to each other. The trained model tries to put them all together so that you get the same things in similar groups.

The top 7 algorithms currently being used for unsupervised learning are:

• Partial least squares
• Fuzzy means
• Singular value decomposition
• K-means clustering
• Apriori
• Hierarchical clustering
• Principal component analysis

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3. Reinforcement Learning

Like traditional types of data analysis, here, the algorithm discovers data through a process of trial and error and then decides what action results in higher rewards. Three major components make up reinforcement learning: the agent, the environment, and the actions. The agent is the learner or decision-maker, the environment includes everything that the agent interacts with, and the actions are what the agent does.

Reinforcement learning happens when the agent chooses actions that maximize the expected reward over a given time. This is easiest to achieve when the agent is working within a sound policy framework.

Now let’s see why Machine Learning is such a vital concept today.

Why is Machine Learning Important?

To better answer the question :what is machine learning” and understand the uses of Machine Learning, consider some of the applications of Machine Learning: the self-driving Google car, cyber fraud detection, and online recommendation engines from Facebook, Netflix, and Amazon. Machines make all these things possible by filtering useful pieces of information and piecing them together based on patterns to get accurate results.

The process flow depicted here represents how Machine Learning works:

The rapid evolution in Machine Learning (ML) 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.

Now that you know what machine learning is, its types, and its importance, let us move on to the uses of machine learning.

Main Uses of Machine Learning

Typical results from machine learning applications usually include web search results, real-time ads on web pages and mobile devices, email spam filtering, network intrusion detection, and pattern and image recognition. All these are the by-products of using machine learning to analyze massive volumes of data.

Traditionally, data analysis was trial and error-based, an approach that became increasingly impractical thanks to the rise of large, heterogeneous data sets. Machine learning provides smart alternatives for large-scale data analysis. Machine learning can produce accurate results and analysis by developing fast and efficient algorithms and data-driven models for real-time data processing.

Pro Tip: For more on Big Data and how it’s revolutionizing industries globally, check out our “What is Big Data?” article.

According to Marketwatch, the global machine learning market is expected to grow at a healthy rate of over 45.9 percent during the period of 2017-2025. If this trend holds, then we will see a greater use of machine learning across a wide spectrum of industries worldwide. Machine learning is here to stay!

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How Do You Decide Which Machine Learning Algorithm to Use?

There are dozens of different algorithms to choose from, but there’s no best choice or one that suits every situation. In many cases, you must resort to trial and error. But there are some questions you can ask that can help narrow down your choices.

• What’s the size of the data you will be working with?
• What’s the type of data you will be working with?
• What kinds of insights are you looking for from the data?
• How will those insights be used?

What is the Best Programming Language for Machine Learning?

If you’re looking at the choices based on sheer popularity, then Python gets the nod, thanks to the many libraries available as well as the widespread support. Python is ideal for data analysis and data mining and supports many algorithms (for classification, clustering, regression, and dimensionality reduction), and machine learning models.

Enterprise Machine Learning and MLOps

Enterprise machine learning gives businesses important insights into customer loyalty and behavior, as well as the competitive business environment. Machine learning also can be used to forecast sales or real-time demand.

Machine learning operations (MLOps) is the discipline of Artificial Intelligence model delivery. It helps organizations scale production capacity to produce faster results, thereby generating vital business value.

A Look at Some Machine Learning Algorithms and Processes

If you’re studying what is Machine Learning, you should familiarize yourself with standard Machine Learning algorithms and processes. These include neural networks, decision trees, random forests, associations, and sequence discovery, gradient boosting and bagging, support vector machines, self-organizing maps, k-means clustering, Bayesian networks, Gaussian mixture models, and more.

There are other machine learning tools and processes that leverage various algorithms to get the most value out of big data. These include:

• Comprehensive data quality and management
• GUIs for building models and process flows
• Interactive data exploration and visualization of model results
• Comparisons of different Machine Learning models to quickly identify the best one
• Automated ensemble model evaluation to determine the best performers
• Easy model deployment so you can get repeatable, reliable results quickly
• An integrated end-to-end platform for the automation of the data-to-decision process

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Prerequisites for Machine Learning (ML)

For those interested in learning beyond what is Machine Learning, a few requirements should be met to be successful in pursual of this field. These requirements include:

1. Basic knowledge of programming languages such as Python, R, Java, JavaScript, etc
2. Intermediate knowledge of statistics and probability
3. Basic knowledge of linear algebra. In the linear regression model, a line is drawn through all the data points, and that line is used to compute new values.
4. Understanding of calculus
5. Knowledge of how to clean and structure raw data to the desired format to reduce the time taken for decision-making.

These prerequisites will improve your chances of successfully pursuing a machine learning career. For a refresh on the above-mentioned prerequisites, the Simplilearn YouTube channel provides succinct and detailed overviews.

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So, What Next?

Wondering how to get ahead after this “What is Machine Learning” tutorial? Consider taking Simplilearn’s Post Graduate Program in AI and Machine Learning which will set you on the path to success in this exciting field. Master Machine Learning concepts, machine learning steps and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer. Enroll today.