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This video on Machine Learning Full Course in 2022 is curated in collaboration with real-time industry experts to teach the learners about the critical fundamentals of machine learning. In this Machine LearningAlgorithms Full Course, you will learn in detail about the top Machine learning algorithms. You will start by understanding the basics of machine learning and know the essential applications of machine learning. You will know the machine learning concepts and then focus on some important machine learning algorithms using Python.

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What is Machine Learning?
A good start at a Machine Learning definition is that it is a core sub-area of Artificial Intelligence (AI). ML applications learn from experience (data) like humans without direct programming. When exposed to new data, these applications learn, grow, change, and develop by themselves.

What is Supervised Learning?
In supervised learning, we use known or labeled data for training. Since the data is known, the learning is supervised, i.e., directed into successful execution. The input data goes through the Machine Learning algorithm and is used to train the model.

What is Unsupervised Learning?
In unsupervised learning, the training data is unknown and unlabeled - meaning that no one has looked at the data before. Without the aspect of known data, the input cannot be guided to the algorithm, which is where the unsupervised term originates from.

What is Reinforcement Learning?
The algorithm discovers data through a process of trial and error and then decides what action results in higher rewards. Three major components make up reinforcement learning: the agent, the environment, and the actions. The agent is the learner or decision-maker, the environment includes everything that the agent interacts with, and the actions are what the agent does.

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