In the early 1800s, the rise of mechanization made life easy. It continues to develop smarter machines to improve our quality of life. With machine learning (ML), people made everything from the steam engine to the self-driving car. Each era of the modern age has been defined by its technological advancements due to machine learning. Machine learning will not only impact a single aspect of everyday life, but it is behind the most technological innovations from the last five years.
Whether you know it or not, you likely already encounter machine learning daily. Machine learning is behind the shows Netflix suggests to you, how your social media feeds are presented, chatbots and predictive text, and language translation apps. It can diagnose medical conditions based on images and run autonomous vehicles and machines. Today, companies deploy artificial intelligence programs, and most use machine learning. This makes AI and machine learning synonymous.
Arthur Samuel is the pioneer of artificial intelligence (AI). He defines machine learning as "the field of study that gives computers the ability to learn without being explicitly programmed." Even after 70 years, that definition has expanded to include various algorithms and models. Today, all companies use machine learning, from manufacturing to retail, banking, and bakeries, to unlock new value or boost efficiency.
What is Machine Learning?
Machine learning is a subfield of artificial intelligence that focuses on developing computer methods to solve problems like a human. You can perform classification and prediction tasks on documents, images, numbers, and other data types. Machine learning enables computers to learn without instructions, but computers still require human input.
Traditional programming requires detailed instructions for the computer to follow. In some cases, writing a program for the machine can be time-consuming or impossible, such as training a computer to recognize pictures of different people. It is easy for humans but difficult for computers to tell the difference. As in machine learning, only a set of examples (data) and a task are given to the computer without any instructions on how to solve it. The computer will figure out how to complete the task with the help of examples without following any instructions.
It is like you want to train a computer to identify cat images; for this, you only give thousands of cat images to a computer and don't provide it with specific instructions on what a cat looks like. With the help of cat images, machine learning algorithms can figure out the common patterns and features that define a cat. As the algorithm processes more images, it gets better at identifying cats, even when presented with images it has never seen before.
Machine Learning vs. Artificial Intelligence
AI refers to any technology that enables machines to simulate human intelligence. Alexa, chatbots, image generators, robotic vacuum cleaners, and self-driving cars are the best examples of AI. Machine learning is often confused with AI as it is closely related to AI. It has goals similar to those of AI but vary in method. One of the prime goals of every AI application is to train a machine to complete a complex task effectively.
Machine learning is a subset of AI; it achieves this goal by performing specific data analysis tasks using training data. However, the focus of an ML model is narrower since each model is typically dedicated to one specific task. Google search engines, video recommendations, traffic alerts, stock prediction, and news classification are good examples of machine learning.
Machine Learning vs. Deep Learning
Deep learning is a subset of machine learning that uses neural networks inspired by the structure and function of the human brain. It requires much more data and computing power than machine learning. It does not need human input. Deep learning uses artificial neural networks, which enable machines to make decisions. It can analyze enormous amounts of both structured and unstructured data. Deep learning and machine learning differ in how data is presented to the machine.
Machine learning algorithms require structured data, whereas deep learning networks work on multiple layers of artificial neural networks. One must know about ANN or artificial neural networks to understand deep learning. Neural networks consist of thousands (or millions) of simple processing nodes connected in a layered structure. The network has an input layer that accepts inputs from the data and an output layer that provides the expected output. Deep learning is used for image and speech recognition, natural language processing, and autonomous systems.
How does Machine Learning work?
Machine learning processes enormous amounts of data and learns from it to predict. It has many steps; each step is important and cannot be skipped to achieve high accuracy. It includes:
1 - Data collection
2 - Data pre-processing
3 - Choosing the right model
4 - Training the model
5 - Evaluating the model
6 - Tuning the parameters
7 - Predictions and deployment
Data collection: It is the core of the machine learning workflow. The quality of data can affect the model's performance. Data can be collected from various sources such as databases, text files, images, audio files, or websites. The data is then organized in a suitable format.
Data pre-processing: It is the most important step in machine learning. It pre-processes the data to remove duplicate and missing values and standardize the formats. This improves the data quality and accuracy by dealing with possible errors.
Choosing the right model: You need to focus on factors such as the data size, the data type, and the complex nature of the problem when choosing the model. There are many models to choose from, including linear regression, decision trees, and neural networks. The model you choose depends on the nature of the data you have gathered and the problem you plan to solve.
Training the model: Train the chosen model to use the prepared data. It involves feeding the data into the model. It also involves adjusting the internal parameters to make better predictions.
Evaluating the model: After completing the training, you must check the model's performance. Test the model by using new data. You can use certain metrics, such as accuracy and precision, to check its performance.
Tuning the parameters: Proper tuning of parameters is essential when working with machine learning. It is important to make adjustments to the parameters of the model. It can directly impact the function of machine learning. You can use many techniques, such as cross-validation and grid search.
Predictions and deployment: Now that the model has been trained and tuned, it's ready to make predictions on new data. Feed new data into the model to see how well it can predict. Once it gives accurate predictions, add it to the production world.
Types of Machine Learning
Machine learning has four types based on the type of learning system and the available data: supervised, unsupervised, semi-supervised, and reinforcement learning.
Supervised learning
It is the most common type of machine learning. Supervised learning deals with tasks and activities that are straightforward. Data is already labelled in this method, which means you know the target variable. Training requires at least an input and output variable given to the model. It analyzes the relationship between the input (data) and the output (labels) to predict the output for new data. Common examples of supervised learning are linear regression, logistic regression and decision trees.
- Linear regression: It is used to identify the relationship between a dependent variable and one or more independent variables to predict future outcomes. When there is only one independent variable and one dependent variable, it is called linear regression. As the number of independent variables increases, it is called multiple linear regression.
- Logistic regression: It is selected when the dependent variable has binary outputs, such as true and false or yes and no. It is used to solve binary classification problems, such as spam identification.
- Decision tree: The models make predictions and outcomes using branching-linked decisions. It looks like a flowchart, starting at the root node with a specific question of data that leads to branches with potential answers. The branches will continue until the data reaches a terminal (or "leaf") node and ends.
Unsupervised learning
The model is trained on an unlabeled dataset and is left to find patterns and relationships in the data without any guidance. Unsupervised learning tasks are of three types: clustering, association rules, and dimensionality reduction.
Clustering
It works with raw or unlabelled data and sorts into groups based on their similarities or differences. It is useful in various applications such as customer segmentation, fraud detection, and image analysis. Various types of unsupervised learning algorithms are used for clustering, which include exclusive, overlapping, hierarchical, and probabilistic.
- Association rule: It discovers correlations and co-occurrences within the data and the different connections between data objects. It is useful in finding customer buying trends, such as which products are frequently purchased together.
- Dimensionality reduction: It simplifies a dataset by removing redundant features and noisy data. It helps deal with large datasets with raw data to retain meaningful dimensions and reduce various variables. Speech recognition uses this method to only extract useful vocal features.
Semi-supervised learning
As the name suggests, it combines supervised and unsupervised learning. The model uses both labelled and unlabeled data during the training process. Since labelling data can be tedious and costly, semi-supervised learning is often an efficient solution. It is used in three situations:
- Fraud detection: Semi-supervised learning systems can learn from the smaller data set and help financial teams only have a handful of fraudulent examples. Fraud is hard to detect, but this method saves accountants from sorting through thousands of transactions.
- Content classification: Reading annotations in large volumes of content can take humans an incredibly long time. Human annotators can use semi-supervised learning to assemble a small selection of hand-labeled examples. This can apply to everything from classifying web pages for search engines to classifying incoming emails for email clients.
- Speech recognition: Capturing the breadth and range of human speech with accents and vocal variance is hard. Semi-supervised learning uses a small training set of human-annotated audio for self-learning.
Reinforcement learning
Learning is entirely based on the trial and error method, involving an agent interacting with the environment. For example, we make AI play a game, and due to repeated trials, it learns what moves will help it win and improve its working and success rate.
How are businesses using Machine Learning?
In the digital world of work, data has become a company's most valuable asset. Machine learning represents an opportunity for companies to leverage historical data to better strategize for the future. As augmented workforces become the norm, companies that continue to rely on manual processes and fail to fully utilize their data will fall behind.
According to Workday research, 80% of decision-makers believe that AI is necessary to keep their business competitive. Despite this, 76% say their knowledge of AI and ML applications needs improvement. To succeed, business leaders must understand where machine learning can bring the most value to their business.z
Below are a few examples of how Workday customers are already using our embedded machine learning:
- Recruiting the best candidate: Manually evaluating high volumes of job applications can be a mammoth task. With machine learning, recruiters can quickly match job requisitions with potential candidates, clustering them based on the strength of their match. A large multinational automotive manufacturer experienced a 70% increase in candidate screening efficiency by using HiredScore AI for Recruiting.*
- Identifying and tracking skills: Understanding the full breadth and depth of talent in your workforce is no easy feat. Rather than relying on a basic catalog of skills, machine learning enables a multidimensional overview. Whether surfacing insights on skills gaps or clustering skills based on industry, region, and proficiency, ML is critical for developing a skills-based talent strategy.
- Enhancing internal mobility: If your talent doesn't have regular opportunities to develop and grow, they're at risk of attrition. Machine learning can surface tailor-made learning recommendations and job openings based on an employee's skills, role, and tenure. By using our ML-generated role recommendations, a major global real estate company saw a 10% increase in internal mobility engagement.
- Improving process efficiency for managers: People leaders spend a lot of valuable time on manual processes. With ML, it's possible to streamline scheduling, surface insights from employee feedback, and address time anomalies. In fact, a corporate ventures organization was able to achieve a 50% manager self-service rate for HR processes, enabling far greater oversight and accountability.
- Automating finance intelligently: While automation has touched many parts of the finance function, too many processes remain manual. The intelligent automation enabled by machine learning includes supplier invoice scanning, receipt scanning for expenses, and customer payment matching.
- Detecting data anomalies: A business is only as good as the quality of its financial data. Machine learning flags any anomalies in the general ledger early in the cycle, improving forecast accuracy. That way, your financial professionals can focus on more strategic and valuable work.
The Importance of Machine Learning
In the 21st century, data is the new oil, and machine learning is the engine that powers this data-driven world. It is a critical technology in today's digital age, and its importance cannot be overstated. This is reflected in the industry's projected growth, with the US Bureau of Labor Statistics predicting a 21% growth in jobs between 2021 and 2031.
Here are some reasons why it's so essential in the modern world:
- Data processing. One of the primary reasons machine learning is so important is its ability to handle and make sense of large volumes of data. With the explosion of digital data from social media, sensors, and other sources, traditional data analysis methods have become inadequate. Machine learning algorithms can process these vast amounts of data, uncover hidden patterns, and provide valuable insights that can drive decision-making.
- Driving innovation. Machine learning is driving innovation and efficiency across various sectors. Here are a few examples:
- Healthcare. Algorithms are used to predict disease outbreaks, personalize patient treatment plans, and improve medical imaging accuracy.
- Finance. Machine learning is used for credit scoring, algorithmic trading, and fraud detection.
- Retail. Recommendation systems, supply chains, and customer service can all benefit from machine learning.
- The techniques used also find applications in sectors as diverse as agriculture, education, and entertainment.
- Enabling automation. Machine learning is a key enabler of automation. By learning from data and improving over time, machine learning algorithms can perform previously manual tasks, freeing humans to focus on more complex and creative tasks. This not only increases efficiency but also opens up new possibilities for innovation.