With the world moving deeper into the concepts of data mining and artificial intelligence, learning the basics of Machine learning becomes cardinal for every AI aspirant. If you are looking to build your career in the field of data science or Artificial intelligence, then learning the basics of Machine learning becomes extremely essential. Machine learning acts as an integral tool for business and helps them with challenging decisions making. So, in this article, let’s dig a little deeper about the basics of Machine learning-
What is Machine Learning?
Machine learning is basically a part of computer science, more specifically a part of artificial intelligence. That helps in the construction of algorithms and study of data that is needed to build human-like activities, and gradually increasing the accuracy. By definition, Machine learning actually provided a method to system to change or alter their ability to improve or automatically learn from an experience without being explicitly programmed. Machine learning basically is a program that teaches computers to develop programs that can access the data and use that data to learn for them.
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The process begins by analyzing the data; this could be done by either providing examples or instructions or through direct experiences. Further, this data then looked for any sorts of patterns. The identification of patterns is essential in order to make certain improvements in the current methodologies and make better decisions based on the data previously provided. The main objective of machine learning is to allow computers learn automatically without human interference and assistance and adjust its actions accordingly.
Unlike the Classic algorithm based approach that considered text as a sequence of keywords, this works on the approach of semantic analysis, it mimics the human actions. And understand the text in a similar way as the human brain.
Now, that we have an idea of what Machine Learning actually is, let’s get into some of the basics of Machine learning. But before we get deeper into it, let’s see where the Machine learning finds its application in the real world-
Some applications of Machine learning
Here are a few ways in which machine learning finds its way into the mainstream-
- Web search: most of the search engines on the web use machine learning to give you the best possible matches for your search. It does that by ranking the pages based on what you have searched for.
- Computational biology: Machine learning finds its application in the designing of drugs too. They are done based on the experiments that are performed previously.
- Robotics: One of the most wide-spread or rather popular applications of machine learning is in the design of robots. For example, the self driving cars.
- Ecommerce: Machine learning comes extremely handy in detecting fraudulent transactions.
- Space exploration: Machine learning is extremely useful in designing space robots and propagation machines and more.
- Extracting information: You can ask for information from various people on the web by using machine languages.
The applications of Machine learning are infinite. However, these are principle uses of the same. Now, it’s time you scratch your brains and look for more applications of Machine learning.
Components of Machine learning:
Every year, hundreds and thousands of algorithms are developed in Machine learning; thankfully you don’t have to study them all. The basic of Machine learning algorithm involves three components-
These are common to all the algorithms made around the globe.
- Representation: You need to represent the data that you will be using in machine learning. This could be in the form of sets of rules or instructions, decisions trees, examples, graphical models, networks, support vector machines, or much more.
- Evaluation: This is the basis on which your data will be evaluated. It could be the probability, precision, accuracy, predictions, recall, error, cost margin, or entropy KL divergence. It could be anything based on the domain in which machine learning is supposed to be used. This will depend on your hypothesis.
- And lastly, Optimization: It is the way in which the candidate program needs to generate a result. This is known as the search process. These could be combinatorial, convex, or constrained optimizations.
Any machine learning algorithm can be built or constructed with these three steps. These form the basic of the machine learning algorithm.
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Types of Machine learning Algorithms or methods:
There are four categories or types of Machine learning algorithms or methods-
- Supervised Machine learning algorithm: This one is also known as inductive learning. It works best when you have pre-existing data that can be studied thoroughly and decisions need to be made based on them or the output. For example if, the object is spam or not, etc. These could also be used for labeled data to predict the future. So, if you are willing to generate a fixed output, then your machine learning algorithm will fall under this category.
- Unsupervised Machine learning algorithm: This category includes algorithms that do not require a return of a trained result or output. These are basically used to recover hidden information in the data. This mainly operates for unlabeled data. The system does not operate to generate a specific output; rather it explores and draws possible inferences from the data.
- Semi-Supervised Machine learning algorithm: This category falls in between that of Supervised Machine learning algorithm and unsupervised Machine learning algorithm. This derives outputs that are partially desired.
- Reinforcement Machine learning algorithm: This one is the most ambitious type of learning is mainly used in AI; this type decides the output by interacting with its environment. This is done in the form of Rewards and errors. Trial and error search and delayed rewards are some of the characteristics of the Reinforcement Machine learning algorithm.
These were some of the basic concepts or basics of Machine learning. Machine learning is of considerable importance in the mainstream due to its ease of application. If you are willing to learn more about artificial intelligence, then you must start learning the basics of Machine learning.