What Is Machine Learning
Machine Learning Work
Similar to how the human brain gains knowledge and understanding, machine learning relies on input, such as training data or knowledge graphs, to understand entities, domains, and the connections between them. With entities defined, deep learning can begin. Machine learning begins with observations or data, such as examples, direct experience, or instruction. It looks for patterns in data so it can later make inferences based on the examples provided. The primary aim of ML is to allow computers to learn autonomously without human intervention or assistance and adjust actions accordingly.
Machine Learning Important
Machine learning as a concept has been around for quite some time. The term “machine learning” was coined by Arthur Samuel, a computer scientist at IBM and a pioneer in AI and computer gaming. Samuel designed a computer program for playing checkers. The more the program played, the more it learned from experience, using algorithms to make predictions. As a discipline, machine learning explores the analysis and construction of algorithms that can learn from and make predictions on data.ML has proven valuable because it can solve problems at a speed and scale that cannot be duplicated by the human mind alone. With massive amounts of computational ability behind a single task or multiple specific tasks, machines can be trained to identify patterns in and relationships between input data and automate routine processes.
Data Is Key:
The algorithms that drive machine learning are critical to success. ML algorithms build a mathematical model based on sample data, known as “training data,” to make predictions or decisions without being explicitly programmed to do so. This can reveal trends within data that information businesses can use to improve decision-making, optimize efficiency and capture actionable data at scale.
AI Is the Goal:
ML provides the foundation for AI systems that automate processes and solve data-based business problems autonomously. It enables companies to replace or augment certain human capabilities. Common machine learning applications you may find in the real world include chatbots, self-driving cars, and speech recognition.
- Data security:
- Machine learning models can identify data security vulnerabilities before they can turn into breaches. By looking at past experiences, machine learning models can predict future high-risk activities so the risk can be proactively mitigated.
- Finance:
- Banks, trading brokerages, and fintech firms use machine learning algorithms to automate trading and provide financial advisory services to investors. Bank of America is using a chatbot, Erica, to automate customer support.
- Healthcare:
- ML is used to analyze massive healthcare data sets to accelerate the discovery of treatments and cures, improve patient outcomes, and automate routine processes to prevent human error
- Fraud detection:
- AI is being used in the financial and banking sector to autonomously analyze large numbers of transactions to uncover fraudulent activity in real-time. Technology services firm Capgemini claims that fraud detection systems using machine learning and analytics
- Retail:
- AI researchers and developers are using ML algorithms to develop AI recommendation engines that offer relevant product suggestions based on buyers’ past choices, as well as historical, geographic, and demographic data.
Machine learning offers clear benefits for AI technologies. But which machine learning approach is right for your organization? There are many ML training methods to choose from including:
- supervised learning
- unsupervised learning
- semi-supervised learning
Supervised Learning:
Supervised machine learning algorithms apply what has been learned in the past to new data using labeled examples to predict future events. By analyzing a known training dataset, the learning algorithm produces an inferred function to predict output values. The system can provide targets for any new input after sufficient training. It can also compare its output with the correct, intended output to find errors and modify the model accordingly.
Unsupervised Learning:
Unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. At no point does the system know the correct output with certainty. Instead, it draws inferences from datasets as to what the output should be
Reinforcement Learning:
Reinforcement machine learning algorithms are a learning method that interacts with its environment by producing actions and discovering errors or rewards. The most relevant characteristics of reinforcement learning are trial and error search and delayed reward. This method allows machines and software agents to automatically determine the ideal behavior within a specific context to maximize its performance. Simple reward feedback — known as the reinforcement signal — is required for the agent to learn which action is best.
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