What is Machine learning?

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Artificial Intelligence-Importance of Machine Learning:

        Machine learning is A branch of artificial intelligence, concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data. As intelligence requires knowledge, it is necessary for the computers to acquire knowledge.

             It is very difficult to categorize to all the decisions based on all possible inputs. So to solve this problem, algorithms are developed. These algorithms are developed based on specific data and  past experience with the principles of statistics and probability theory , logic ,search.

The developed algorithms form the basis of various applications such as:

·         Vision processing

·         Language processing

·         Forecasting (e.g., stock market trends)

·         Pattern recognition

·         Games

·         Data mining

·         Expert systems

·         Robotics

SOME MACHINE LEARNING METHODS

        Machine learning algorithms are often categorized as 

·         Supervised machine learning algorithms

·         unsupervised machine learning algorithms 

·         Semi-supervised machine learning algorithms

·         Reinforcement machine learning algorithms 

Supervised machine learning algorithms:

    Supervised learning deals with learning a function from available training data. A supervised learning algorithm analyzes the training data and produces an inferred function. Some common examples are,

·         classifying e-mails as spam,

·         labeling web pages based on their content, and

·         voice recognition.

      There are some supervised algorithms such as neural networks, Support Vector Machines (SVMs), and Naive Bayes classifiers.

Unsupervised machine learning algorithms:

          Unsupervised learning is an extremely powerful tool for analyzing available data and look for patterns and trends. It is most commonly used for clustering similar input into logical groups. There are some common approaches such as,

• k-means
• self-organizing maps, and
• hierarchical clustering

Semi-supervised machine learning algorithms:

         Semi-supervised is comes between supervised and unsupervised learning, since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiring unlabeled data generally doesn’t require additional resources.

Reinforcement machine learning algorithms:                    

         Reinforcement is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.