What is the difference between Artificial Intelligence and Machine Learning?

Artificial intelligence and machine learning are correlated, and yet they have some differences. Artificial intelligence is an all-encompassing concept that aims to create intelligence that mimics human-level intelligence. Artificial intelligence is a broad concept that deals with creating human-like critical thinking ability and reasoning capabilities for machines. On the other hand, machine learning is a specific subset or application of artificial intelligence that aims to create machines that can learn autonomously from data. Machine learning is specific, not general, which means it allows a machine to make predictions or decisions about a specific problem using data. 

What are the types of machine learning?

Now let’s see the different types of Machine Learning:

1. Supervised Machine Learning

Imagine a teacher supervising a class. The teacher already knows the correct answers, but the learning process only stops when the students also learn the answers. This is the essence of supervised machine learning algorithms. Here the algorithm learns from a set of training data and makes predictions which are compared to the actual output values. If the predictions are not correct, then the algorithm is modified until it is satisfactory. This learning process continues until the algorithm reaches the required level of performance. Then it can provide the desired output values ​​for all new inputs.

2. Unsupervised Machine Learning

In this case, there is no teacher for the class and the students must learn by themselves! Thus, for unsupervised machine learning algorithms, there is no specific answer to learn and there is no teacher. In this way, the algorithm does not determine any output for the input, but it explores the data. The algorithm is left unattended to find the underlying structure in the data in order to learn more and more about the data itself.

3. Semi-supervised machine learning

Students learn both from their teacher and by themselves in semi-supervised machine learning. And you can guess it from the name itself! It is a combination of supervised and unsupervised machine learning that uses a small amount of labelled data like supervised machine learning and a larger amount of unlabeled data like unsupervised machine learning to train the algorithms .

First, the labelled data is used to partially train the machine learning algorithm, then this partially trained model is used to pseudo-label the rest of the unlabeled data. Finally, the machine learning algorithm is fully trained using a combination of labelled and pseudo-labeled data.

4. Reinforcement machine learning

Well, here are the hypothetical students learning from their own mistakes over time (it’s like life!). Thus, reinforcement machine learning algorithms learn optimal actions through trial and error. This means that the algorithm decides the next action by learning behaviours based on its current state that will maximise the reward in the future. This is done using reward feedback which allows the reinforcement algorithm to learn which are the best behaviours that lead to maximum reward. This reward feedback is known as the reinforcement signal.

What are popular machine learning algorithms?

Let’s look at some of the popular machine learning algorithms based on specific types of machine learning.Supervised machine learning

Supervised machine learning includes regression and classification algorithms. Some of the most popular algorithms in these categories are:

1. Linear Regression Algorithm

The linear regression algorithm provides the relationship between an independent variable and a dependent variable. It demonstrates the impact on the dependent variable when the independent variable is changed in any way. Thus, the independent variable is called the explanatory variable and the dependent variable is called the factor of interest. An example of using the linear regression algorithm is to analyse real estate prices in the area based on property size, number of rooms, etc.

2. Logistic regression algorithm

The logistic regression algorithm deals with discrete values ​​while the linear regression algorithm deals with continuous value predictions. This means that logistic regression is a better option for binary classification. An event in logistic regression is ranked as 1 if it occurs and it is ranked as 0 otherwise. Therefore, the probability of occurrence of a particular event is predicted based on the given predictor variables. An example of the use of the logistic regression algorithm is in medicine to predict whether a person has malignant breast cancerous tumours or not based on the size of the tumours.

3. Naive Bayes classifier algorithm

The Naive Bayes Classifier algorithm is used to classify data texts such as a web page, document, email, among others. This algorithm is based on Bayes’ probability theorem and assigns the item’s value to a population of one of the available categories. An example of using the Naive Bayes Classifier algorithm is for spam filtering. Gmail uses this algorithm to classify an email as spam or not spam.

Unsupervised machine learning

Unsupervised machine learning mainly includes clustering algorithms. Some of the most popular algorithms in this category are:

1. K stands for clustering algorithm

Let’s say you want to look up the name “Harry” on Wikipedia. Now, “Harry” can refer to Harry Potter, Prince Harry of England, or any other popular Harry on Wikipedia! Thus, groups web pages that talk about the same ideas using the K Means clustering algorithm (since it is a popular algorithm for cluster analysis). K means that the clustering algorithm in general uses the K number of clusters to operate on a given data set. In this way, the output contains K clusters with the input data partitioned between the clusters.

2. A priori algorithm

The Apriori algorithm uses the if-then format to create association rules. This means that if a certain Event 1 occurs, then there is a high probability that a certain Event 2 will also occur. For example: IF someone is buying a car, THEN chances are they are also buying car insurance. The Apriori algorithm generates this association rule by observing the number of people who have taken out car insurance after buying a car.