and carry out human-like work. As AI is continued to grow, they are having a big impact on our worth of life [1].
AI is also defined as follows:
• An intelligent agent shaped by humans.
• Capable to perform tasks intelligently without human interventions.
• Able to think and act sensibly as human.
1.2 Machine Learning
When a machine gains the capability to learn from practices and experience rather than just by preset instructions is called Machine Learning (ML). It is the subset of AI. ML algorithms produce results and improve their own results on the basis of past experiences. It produces the desired output by modifying its own produced output according to available datasets and implicitly comparing the current outcome to the final output [2].
1.2.1 Difference Between Artificial Intelligence and Machine Learning
In a general sense, AI and ML are much the same, but the fact is ML is the subset of AI as depicted in Table 1.1 [3].
1.2.2 Types of Machine Learning
• Supervised learning
• Unsupervised learning
• Semi-supervised learning
• Reinforcement learning
Figure 1.1 depicts the various types of machine learning techniques.
Table 1.1 Difference between AI and machine learning.
| Artificial Intelligence | Machine learning |
| AI enables the machines to behave or simulate like humans. | ML permits a machine to learn from available past data without giving instructions to it explicitly. |
| AI is used to make such systems which can solve complex problems like humans. | ML goal is to make a machine to be trained itself from historical data without any human intervention. |
| AI has ML and DL as subset. | ML has DL as subset. |
| Following three types of AI: general AI, strong AI, and weak AI. | Following four types of ML: semi-supervised, unsupervised, reinforcement, and Supervised learning. |
| AI focuses to maximize the chance of success. | Machine learning focuses on accuracy and patterns. |
| AI uses structured, unstructured data, and semi-structured. | ML uses structured and semistructured data only. |
1.2.2.1 Supervised Learning
In the supervised ML, a machine learns from past data and then produces the desired output [4]. A machine gets its training from already available dataset using appropriate algorithms and inferred function. This inferred function predicts the output and gives an approximate desired result. The used labeled data set helps the algorithm to understand the data and produce the labeled output for more accurate results [5]. Figure 1.2 shows the complete process of supervised learning.
The following are some algorithms which are based on supervised learning:
• Linear Regression
• Naive Bayes
• Nearest Neighbor
• Neural Networks
• Decision Trees
• Support Vector Machines (SVM)
Figure 1.1 Classification of machine learning.
Figure 1.2 Process of supervised learning.
1.2.2.2 Unsupervised Learning
When a machine learns from unlabeled data or it discovers the input pattern itself, it is known as unsupervised learning. It divides the learning data into diverse clusters. Therefore, this learning is known as clustering algorithm. In this learning, the training data will not be labeled and inferences functions create its own inferences by exploring the unlabeled dataset in order to find suitable patterns [6]. Figure 1.3 shows the complete process of unsupervised learning.
Name of common unsupervised algorithms:
• Anomaly detection
• K-means clustering
• Neural networks
• Hierarchal clustering
• Independent component analysis
• Principle component analysis
1.2.2.3 Semi-Supervised Learning
When the machine learns from both labeled and unlabeled data, it is known as semi-supervised learning. When it is not feasible to label the data due to lack of resource to label it or due to the large size of the data, semi-supervised learning is used [7]. It lies among the supervised and unsupervised learning. For the model building, semi-supervised learning is best. Semi-supervised learning makes use of small amount of labeled data but large amount of unlabeled data [8].
Figure 1.3 Process of unsupervised learning.
