What is one way that reinforcement learning is different from the other types of machine learning
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Written by Coursera • Updated on Sep 14, 2022 Show
Machine learning is an exciting field and a subset of artificial intelligence. Use this guide to discover more about real-world applications, and the three types of machine learning you should know. Machine learning is a specialized technology that falls under the umbrella of artificial intelligence (AI). This exciting field is the driving power behind many modern technologies, including image recognition, self-driving cars, and products like Amazon's Alexa. The global machine learning (ML) market is predicted to grow to more than $188 billion by 2029, up from $21 billion in 2022, according to Fortune Business Insights [1]. This rapid growth means there is plenty of opportunity to dive into a career in machine learning. When you decide to start the journey into machine learning, there are three main types of machine learning you should know. Read on to learn more. What is machine learning?This branch of AI focuses on using data and algorithms to mimic human learning, allowing machines to improve over time, becoming increasingly accurate when making predictions or classifications, or uncovering data-driven insights. It works in three basic ways, starting with using a combination of data and algorithms to predict patterns and classify data sets, an error function that helps evaluate the accuracy, and then an optimization process to fit the data points into the model best. Did you know?Arthur Samuel created the term "machine learning" in reference to his research in the early 1960s. That research was based on the checkers game that Robert Nealy played against an IBM 7094 computer and lost. Although this is minor compared to what machines can do today, it was a groundbreaking milestone at the time. Applications of machine learningMachine learning is already used around us and you may not realize how it impacts your life. Here's a few ways it's used that you should know: Social media features: Social media platforms integrate machine learning algorithms to help deliver personalized experiences to you. Facebook notes your activities, including your comments, likes, and the time you spend on different types of content. The algorithm learns from your activity and makes pages and friend suggestions tailored to you. Virtual assistants: Apple's Siri, Amazon's Alexa, and Google Now are all popular options if you're looking for a virtual personal assistant. These voice-activated devices can do everything from search for flights to check your schedule to set alarms and more. Machine learning is a key component of these smart devices and speakers. They collect information and refine it each time you interact with them. The machine can then use that data to give you results that are best matched to your preferences. Product recommendations: Popular among e-commerce websites, product recommendations are a common machine learning application. It lets these sites track your behavior based on your searches, previous purchases, and your shopping cart history to make suggestions and recommendations about products you may be interested in. Image recognition: This complex technology is cropping up in a variety of fields. In your everyday life, you've probably come across this while uploading a photo to your social media platform. When you tag someone in an image, the platform recognizes them. It can also be transformative for identifying potential threats or criminals, unlocking phones and mobile devices, and finding missing persons. 3 types of machine learningMachine learning involves showing a large volume of data to a machine so that it can learn and make predictions, find patterns, or classify data. The three machine learning types are supervised, unsupervised, and reinforcement learning. Supervised learningGartner, a business consulting firm, predicts that supervised learning will remain the most utilized machine learning among enterprise information technology leaders in 2022 [2]. This type of machine learning feeds historical input and output data in machine learning algorithms, with processing in between each input/output pair that allows the algorithm to shift the model to create outputs as closely aligned with the desired result as possible. Common algorithms used during supervised learning include neural networks, decision trees, linear regression, and support vector machines. This machine learning type got its name because the machine is “supervised” while it's learning, which means that you’re feeding the algorithm information to help it learn. The outcome you provide the machine is labeled data, and the rest of the information you give is used as input features. For example, if you were trying to learn about the relationships between loan defaults and borrower information, you might provide the machine with 500 cases of customers who defaulted on their loans and another 500 who didn't. The labeled data “supervises” the machine to figure out the information you're looking for. Supervised learning is effective for a variety of business purposes, including sales forecasting, inventory optimization, and fraud detection. Some examples of use cases include:
Unsupervised learningWhile supervised learning requires users to help the machine learn, unsupervised learning doesn't use the same labeled training sets and data. Instead, the machine looks for less obvious patterns in the data. This machine learning type is very helpful when you need to identify patterns and use data to make decisions. Common algorithms used in unsupervised learning include Hidden Markov models, k-means, hierarchical clustering, and Gaussian mixture models. Using the example from supervised learning, let's say you didn't know which customers did or didn't default on loans. Instead, you'd provide the machine with borrower information and it would look for patterns between borrowers before grouping them into several clusters. This type of machine learning is widely used to create predictive models. Common applications also include clustering, which creates a model that groups objects together based on specific properties, and association, which identifies the rules existing between the clusters. A few example use cases include:
Reinforcement learningReinforcement learning is the closest machine learning type to how humans learn. The algorithm or agent used learns by interacting with its environment and getting a positive or negative reward. Common algorithms include temporal difference, deep adversarial networks, and Q-learning. Going back to the bank loan customer example, you might use a reinforcement learning algorithm to look at customer information. If the algorithm classifies them as high-risk and they default, the algorithm gets a positive reward. If they don't default, the algorithm gets a negative reward. In the end, both instances help the machine learn by understanding both the problem and environment better. Gartner notes that most ML platforms don't have reinforcement learning capabilities because it requires higher computing power than most organizations have [2]. Reinforcement learning is applicable in areas capable of being fully simulated that are either stationary or have large volumes of relevant data. Because this type of machine learning requires less management than supervised learning, it’s viewed as easier to work with dealing with unlabeled data sets. Practical applications for this type of machine learning are still emerging. Some examples of uses include:
Career paths in machine learningThe World Economic Forum's “Future of Jobs Report 2020” predicts that machine learning and all of artificial intelligence will generate 97 million new jobs around the world by 2025 [3]. In 2019, Indeed ranked machine learning engineer number one on its list of the Best Jobs in the United States, noting its 344 percent growth rate [4]. Machine learning is an in-demand field that lends itself to several possible career paths, including: *All salary data sourced from Glassdoor as of September 2022 Machine learning engineer: In this role, you can work on machine learning projects and create and manage platforms.
Data scientist: In this role, you can use a combination of machine learning and predictive analytics to collect, analyze, and interpret data.
Natural language processing (NLP) scientist: In this role, you can work with computers, computer science, and computational language to form connections between the way humans communicate and computers understand and interpret human language.
Business intelligence developer: In this role, you’ll focus on analyzing data to gather insight into business and market trends.
How to get started in machine learningMost employers look for a combination of education and experience. Here are three common ways to set yourself on the path to the job you want: 1. Earn a bachelor's degree.Start your career path with a bachelor's degree in data science, computer programming, computer science, or a related field. Machine learning is an advanced field and employers tend to hire candidates with a bachelor's degree. But some work experience and a touch of grit, those with associate degrees or high school diplomas can also get started in machine learning. 2. Gain work experience.Try to land an internship or entry level position in machine learning-related roles in software development, software engineering, data engineering, or data science. 3. Advance your career.Consider earning a master's degree or brushing up on your skills with a professional certificate. Many employers prefer to hire machine learning professionals with advanced degrees in software engineering, computer science, machine learning, or AI. You are Currently on slide 1 Next stepsLearn the fundamentals of machine learning with Stanford’s popular Machine Learning course, or develop the skills needed to build and train deep neural networks with the Deep Learning Specialization from DeepLearning.AI. If you’re serious about a career in machine learning, explore how a data science degree could unlock new opportunities. course Supervised Machine Learning: Regression and ClassificationIn the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning libraries ... 4.9 (3,260 ratings) 129,209 already enrolled BEGINNER level Average time: 1 month(s) Learn at your own pace Skills you'll build: Regularization to Avoid Overfitting, Gradient Descent, Supervised Learning, Linear Regression, Logistic Regression for Classification Written by Coursera • Updated on Sep 14, 2022 This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals. How does reinforcement learning differ from machine learning?And, unsupervised learning is where the machine is given training based on unlabeled data without any guidance. Whereas reinforcement learning is when a machine or an agent interacts with its environment, performs actions, and learns by a trial-and-error method.
What is reinforcement learning in machine learning?Reinforcement learning is a machine learning training method based on rewarding desired behaviors and/or punishing undesired ones. In general, a reinforcement learning agent is able to perceive and interpret its environment, take actions and learn through trial and error.
What are the main differences between reinforcement learning and other ML techniques like supervised or unsupervised learning?And in Reinforcement Learning, the learning agent works as a reward and action system. Supervised learning maps labelled data to known output. Whereas, Unsupervised Learning explore patterns and predict the output. Reinforcement Learning follows a trial and error method.
What do you mean by reinforcement learning explain the three different ways in which reinforcement can be implemented?Three methods for reinforcement learning are 1) Value-based 2) Policy-based and Model based learning. Agent, State, Reward, Environment, Value function Model of the environment, Model based methods, are some important terms using in RL learning method.
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