The process by which computers learn to recognize patterns, or the capacity to continuously learn from and make predictions based on data, then make adjustments without being specifically programmed to do so, is known as machine learning (ML), a subcategory of artificial intelligence.
Machine learning is without a doubt the technology that consumers are most interested in today. Applications for it include developing self-driving cars and forecasting diseases like Cancer/Diabetics. The driving force behind this blog is the rising need for Machine Learning expertise.
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Key Takeaways from this Blog
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- Real-Life Benefits
- High-Level Overview
- Types of Machine Learning
- Machine Learning Algorithms
- Machine Learning Process
- Machine Learning Pros / Cons.
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High-Level Overview of Machine Learning :
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Main Types of Machine Learning :
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Supervised Learning
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Un-Supervised Learning
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Reinforcement Learning
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What is Supervised Learning?
The machine learning activity known as supervised learning involves learning a function that translates input to an output using sample input-output pairs. A function is inferred from labeled training data made up of a collection of training instances. Each example in supervised learning consists of two elements: the desired output value (also known as the supervisory signal) and an input object (usually a vector). An inferred function that can be used to map fresh instances is created by a supervised learning algorithm after it examines the training data.
In simple terms process of adopting models trained using a labeled dataset under the supervision of training data.
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What is Un-Supervised Learning?
Unsupervised learning refers to the use of artificial intelligence algorithms to identify patterns in data sets containing data points that are neither classified nor labeled. The algorithms are thus allowed to classify, label, and/or group the data points contained within the data sets without having any external guidance in performing that task.
In other words, unsupervised learning allows the system to identify patterns within data sets on its own.
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What is Reinforcement Learning?
Reinforcement Learning is an important type of Machine Learning in that an agent will learn from the environment by interacting with it and receiving rewards (positive or Negative) for performing actions.
In general, a reinforcement learning agent is able to perceive and interpret its environment, take actions and learn through trial and error
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Machine Learning Algorithms :
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Machine Learning Process :
The below-depicted diagram Highlights the machine learning process flow.
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Supervised Learning – Pros & Cons. :
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- With the help of supervised learning, the model can predict the output on the basis of prior experiences.
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- In supervised learning, we can have an exact idea about the classes of objects.
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- It helps us to solve real-world problems like fraud detection, and spam filtering.
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- Supervised models are not suitable for handling complex tasks.
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- It cannot predict the correct output if the test data is different from the training dataset.
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- Training requires a lot of computation time.
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Unsupervised Learning Pros & Cons. :
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- It is used for more complex tasks.
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- It is preferable -and easy to get unlabeled data.
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- Unsupervised Learning is intrinsically more difficult than supervised learning as it does not have corresponding output.
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- It is less accurate.
- Conclusion
Now, we have understood the high-level concept behind machine learning. To summarize the same, Machine Learning is a technique of training machines to perform the activities a human brain can do. In today’s world, we have seen that machines can beat human champions in games such as Chess, and AlphaGO, which are considered very complex. You have seen that machines can be trained to perform human activities in several areas and can aid humans in living better lives.
