Many human activities such as developing computer programs, solving mathematics, common sense reasoning, understanding languages and interpreting it, driving an automobile, etc are said to demand “Intelligence”. The actual point of all such systems is that “How to make system think”.

So, AI or we can say Artificial Intelligence is the automation of tasks that we associate with human thinking and the system that thinks rationally. Now you might be wondering what does thinking rationally means. A system is rational if it does the “right thing” given what it knows.

Now there are four approaches that are followed by AI. These four approaches are Acting Humanly, Thinking Humanly, Thinking Rationally and Acting Rationally.

Now there are different viewpoints from which can view or define AI. These viewpoints are based on Philosophy, Mathematics, Economics, Neuroscience and finally Psychology.

Now unlike Machine Learning, AI is a broader concept than the implementation that is ML is a subset of AI that allows machine to learn from data without being programmed explicitly. The Artificial intelligence system does not require to be pre-programmed, instead of that, they use such algorithms which can work with their own intelligence. It can be divided into three types:

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning

Machine Learning works on a certain life cycle which is as followed:

• Gathering Data: we need to identify the different data sources, as data can be collected from various sources such as files, database, internet, or mobile devices. It is one of the most important steps of the life cycle. The quantity and quality of the collected data will determine the efficiency of the output. The more will be the data, the more accurate will be the prediction.
• Data Preparation: Data preparation is a step where we put our data into a suitable place and prepare it to use in our machine learning training.
• Data Wrangling: Data wrangling is the process of cleaning and converting raw data into a useable format. It is the process of cleaning the data, selecting the variable to use, and transforming the data in a proper format to make it more suitable for analysis in the next step. It is one of the most important steps of the complete process. Cleaning of data is required to address the quality issues.
• Analyse Data: This step involves Selection of Analytical Techniques, Building Models and Reviewing the results.
• Train the model: In this step we train our models to improve performance. We use dataset to train the model using various machine learning algorithms.
• Test the model: Testing the model determines the percentage accuracy of the model as per the requirement of project or problem.
• Deployment: The last step of machine learning life cycle is deployment, where we deploy the model in the real-world system.