Artificial intelligence and machine learning
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Artificial Intelligence and Machine Learning

In the rising technology, many people are dreamed of talking robots, flying rockets. This is where artificial intelligence came into life. Artificial intelligence is nothing but, the work done by humans can be done with the help of artificial intelligence. Artificial intelligence can predict earthquakes, tsunamis.

We have also seen virtual predictive assistance like Alexa, Siri. Artificial intelligence technology has evolved rapidly in the technology. Artificial intelligence can cause a machine to work similarly to humans. presently, the AT technology running with many of the new technologies. Many of the gadgets chatbots, virtual assistants are based on artificial intelligence.

Now, we also have self-driving cars that work very efficiently and increasing rapidly. Everything is developed on basis of the artificial intelligence. Let us know the brief definition of artificial intelligence. The artificial represents man-made things or technologies. All the man-made things all come under artificial intelligence. and the term intelligence defines the thinking power of the machine. simply, the man-made thinking power is called artificial intelligence.

We can create many intelligent machines that behave similarly as the human. Artificial intelligence was coined in the year 1956 by John Mccarthy. It can perform the tasks like visual perception, decision making. Artificial intelligence has many subtopics like deep learning, machine learning, natural language processing, expert systems, and computer vision. we require a lot of computational power to implement artificial intelligence in action.

For a better understanding let us discuss an example, we search about different topics and information in our day to day life, we may observe some of the suggestions that we get while we type in the google search bar. These suggestions and recommendations are predicted based on the artificial intelligence technology.

The AI tries to search what we are going to type on the google search and sends recommendations to us. But how does it predicts the data? We use many of the natural language processing units that are involved in the concept of the google search. Machine learning is the subconcept in artificial intelligence technology.

We use deep learning, language processing, and many more concepts involved behind the google search engine. There are many different types the artificial intelligence. Artificial intelligence can be divided into three stages. let us the categories of artificial intelligence in detail. Artificial narrow intelligence, artificial general intelligence, and other one is artificial superintelligence.

We call artificial narrow intelligence the weak intelligence. It functions only to perform or to identify some of the specific tasks. Let us take an example for the understanding of artificial intelligence. Alexa is the best example for a better understanding of artificial narrow intelligence. Alexa can operate only within the limited range, it doesn’t show any genuine intelligence in it. All the self-driving cars also come under narrow intelligence. The narrow intelligence doesn’t show any self-awareness and it equals blind intelligence.

All the currently running technologies are based on this narrow intelligence. Artificial general intelligence is referred to as strong AI. General intelligence can perform any type of the intelligent tasks that provided by the human. We should train the machine in order to perform specific tasks given by the system. We use the different number of languages we use in artificial intelligence.

We can code in python. Python is known as the most effective language and the essay language to get understand easily. We can implement machine learning and artificial intelligence algorithms easily in python. Another is the statistical programming language that works as effectively by perfectly analyzing and, manipulating the data in it.

Apart from python and java we also can have Java. Java can also work effectively. Artificial intelligence must need many of the algorithms, generic programming, and artificial neural networks. And the most important aspect of artificial intelligence is Machine learning. Machine learning is different from artificial intelligence. Machine learning is only the subconcept of AI.

Typically, we inject and train the different algorithms so that the machine can make its own decisions as humans. As the technology is evolving, the data get increased rapidly, so we should build some of the predictive models in order to the learning of the data and get analyzing of the data for accurate decisions and results. Many of the companies are using machine learning Technology.

They are trying to build predictive models that deliver the right results. It consists of a set of rules and many statistical techniques. These Techniques can learn patterns from the data and retrieve the essential information from it.

The main logic behind the concept of machine learning is nothing but the machine learning algorithms themselves. We have different algorithms like linear regression, random forest, decision tree, and many more are come under the machine learning algorithms. We also have some of the machine learning models. Model be the essential component in the machine learning process.

We train these models by using machine learning algorithms. You might get a difference like what actually the difference between the algorithm and the model. The algorithm will map all the decisions, what the models take in order to provide the correct output. So the model get uses different machine learning algorithms that carry the useful information from the given input. We also have the predictive variables in it. The predictive variable acts as the data for the prediction of the correct output. To understand clearly the predictive variables, let us take an example. We want to predict the height of a specific person based on his or her weight. Now the weight becomes the predictive variable to provide the required output.

Another type of variable is called the response variable, the response variable predicts the exact output with the help of the predictive variable. We call the response variable has the target variable or the output variable. Training data is also one of the concepts in machine learning technology, these are the terminologies that we have already known, These we use commonly in the machine learning process. We can create the machine learning models by using the concept called training data. The process of machine learning works as when we want to predict the desired output, it divides the process into two types.

We call it data splicing. The taken input data can be divided into two parts. We call these the training data and the testing data. As we have discussed earlier, the training data is used to create the machine learning model, it will help the model to identify basic patterns and trends that are very essential to predict the right output.After the completion of the training data, we need to test the model to check whether the desired output is generated or not.

Now we have got a brief idea of how the testing and the training data work. The training data can train the models and the testing data will be used to test the efficiency of the model. It can be used to build a specific output for particular problem statements. Every process has some of the instructions and steps involved in it. Firstly we need to define the objective for the specific problem statement, and we should gather all the required data and prepare the data, data exploration, building of the data model, and at last making the final predictions.

To understand the steps in the machine learning process, let us solve a problem, suppose we need to predict the occurrence of the Tsunami. Firstly we need to predict the local weather environment, the possibility of occurrence of Tsunami in that particular area. we also need to predict the occurrence of the target features. we clearly need to define how the problem is going to be done by the process of machine learning.

And the variables that are needed for the prediction of the specified outcome. As we have discussed, the next step is to gather the data, we may get a common doubt like what kind of data should be gathered to solve a specified problem. and where can we get the required data for that output?. It is one of the toughest jobs to search for the right data.

We need to find the humidity level, pressure, and temperature in that particular area. After the data gathering, we need to use data preparation or which can also be called data cleaning. It means we need to remove all the unnecessary data or any redundant variables from it. after the data cleaning process, we need to explore the data analysis. If we need to understand all the trends and patterns in the data, we have to find at what level we are depending on these variables like temperature humidity and pressure.

We just need to find out all the correlations between all of these variables and need to map all these data at the exploratory and data analysis stage and show how can we predict the exact solution to that particular data. And we should build a machine learning model, as we have discussed earlier, ar this stage we use the training and testing model.

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