Machine learning is the science of developing algorithms and statistical models that computer systems use to perform tasks without explicit instructions, relying instead on patterns and inferences. Computer systems use machine learning algorithms to process large amounts of statistical data and identify data patterns. In this way, systems can more accurately predict outcomes based on a given set of inputs. For example, data scientists can train a medical application to diagnose cancer from x-ray images, storing millions of scanned images and the corresponding diagnoses.
Why is machine learning so important?
Machine learning helps companies drive growth, discover new revenue streams, and solve complex problems. Data is an important driver of business decision making, but traditionally companies have used data from a variety of sources such as customer, employee, and financial feedback. Machine learning research automates and streamlines this process. By using software that analyzes very large amounts of data at high speed, companies can achieve results faster.
Where is machine learning used?
See below for key uses for machine learning.
Machine learning can support preventive maintenance, quality control, and innovative research in the manufacturing sector. Machine learning technology is also helping companies improve logistics solutions, including assets, supply chains, and inventory management. For example, 3M, a large-scale manufacturing company, uses AWS Machine Learning to innovate sanding paper. Machine learning algorithms allow 3M researchers to analyze how subtle changes in shape, size and orientation improve abrasion and durability. These proposals affect the production process.
Healthcare and life sciences
The proliferation of wearable sensors and devices has generated a significant amount of health data. Machine learning programs can analyze this information and help doctors diagnose and treat in real time. Machine learning researchers are developing solutions that detect cancerous tumors and diagnose eye diseases, which significantly affect human health outcomes. For example, Cambia Health Solutions has used AWS Machine Learning to support healthcare startups to automate and personalize care for pregnant women.
Financial machine learning projects improve risk analytics and regulation. Machine learning technology can allow investors to identify new opportunities by analyzing stock market movements, valuing hedge funds, or calibrating financial portfolios. In addition, it can help identify high-risk credit customers and mitigate signs of fraud. Leading financial software provider Intuit uses the AWS Machine Learning engine, Amazon Textract, to provide more personalized financial management and help end users improve their finances.
In retail, machine learning can be used to improve customer service, inventory management, upsell, and multi-channel marketing. For example, Amazon Fulfillment (AFT) has been able to reduce infrastructure costs by 40% by using a machine learning model to identify misplaced inventory. This delivers on Amazon’s promise to provide customers with easy access to and on-time delivery of the item, despite processing millions of shipments each year.
Multimedia and entertainment
Companies in the entertainment industry are turning to machine learning to better understand their target audience and deliver immersive, personalized content on demand. Machine learning algorithms are being used to design trailers and other advertisements, provide consumers with personalized content recommendations, and even optimize production.
For example, Disney uses AWS Deep Learning to archive its media library. AWS machine learning tools automatically tag, describe, and sort media content, allowing Disney writers and animators to quickly search for and become familiar with Disney characters.
How does machine learning work?
The central idea of machine learning is that there is a mathematical relationship between any combination of inputs and outputs. The machine learning model does not have knowledge of this relationship in advance, but can generate it if enough datasets are provided. This means that every machine learning algorithm is built around a modifiable mathematical function. See below for a description of the underlying principle.
We “train” the algorithm by giving it the following I/O combinations [input / output (i,o)]: (2.10), (5.19) and (9.31).
The algorithm calculates the ratio between input and output as follows: o = 3 × i + 4.
Then we give input 7 and ask to predict the result. The algorithm can automatically determine the output as 25.
While this is a basic understanding, machine learning focuses on the principle that all complex data points can be mathematically related by computer systems if they have enough data and processing power to process that data. Therefore, the accuracy of the output data is directly proportional to the magnitude of the input data.
What types of machine learning algorithms are there?
Algorithms can be classified into four learning styles based on the expected output and type of input.
Supervised machine learning
Machine Learning Unsupervised
Partially Involved Machine Learning
Reinforcement Machine Learning
1. Supervised machine learning
Data scientists provide the algorithms with labeled and defined training data to evaluate correlations. The sample data defines both the input and output of the algorithm. For example, images of handwritten digits are annotated to indicate which number they correspond to. The supervised learning system can recognize clusters of pixels and shapes associated with each number, given enough examples. Over time, the system recognizes handwritten numbers, consistently distinguishing between 9 and 4 or 6 and 8.
The strengths of supervised machine learning are its simplicity and lightness of structure. Such a system is useful when predicting a possible limited set of results, categorizing data, or combining the results of two other machine learning algorithms. However, labeling millions of unlabeled datasets is challenging. Let’s take a closer look at this.
What is data labeling?
Data labeling is the process of categorizing inputs with their corresponding defined outputs. Labeled training data is required for supervised learning. For example, millions of images of apples and bananas should be labeled with the words “apple” or “banana”. Machine learning applications could then use this training data to guess the name of the fruit from the image of the fruit. However, labeling millions of new data can be time consuming and challenging. Collaboration services such as Amazon Mechanical Turk can overcome this limitation of supervised learning algorithms to some extent. These services provide access to a large number of available work resources around the world, making it easy to collect data.
2. Unsupervised machine learning
Unsupervised learning algorithms are trained on unlabeled data. Such algorithms scan new data, trying to establish meaningful relationships between inputs and predefined outputs. They can identify patterns and classify data. For example, unsupervised algorithms can group news articles from different news websites into general categories such as sports, crime, etc. They can use natural language processing to understand the meaning and emotion in an article. In retail, unsupervised learning will help find patterns in customer purchases and provide data analysis results such as: A customer is more likely to buy bread if they also buy butter.
Unsupervised learning is useful for pattern recognition, anomaly detection, and automatic grouping of data into categories. Since the training data does not require labeling, setup is easy. These algorithms can also be used to automatically clean up and process data for further modeling. The limitation of this method is that it cannot give accurate predictions. Also, it can’t allocate specific output types on its own.
3. Machine learning with partial teacher involvement
As the name suggests, this method combines supervised and unsupervised learning. This method is based on using a small amount of labeled data and a large amount of unlabeled data to train systems. First, the labeled data is used to partially train the machine learning algorithm. After that, the partially trained algorithm itself labels the unlabeled data. This process is called pseudo-labeling. The model is then retrained on the resulting dataset without explicit programming.
The advantage of this method is that you don’t need large amounts of tokenized data. This is useful when dealing with data such as long documents that take too much of a human’s time to read and label.
4. Reinforcement learning
Reinforcement learning is a method in which reward values are tied to different steps that the algorithm must go through. Thus, the goal of the model is to accumulate as many reward points as possible and eventually reach