Machine Learning

Machine Learning, as the name says, is all about machines learning automatically without being explicitly programmed or learning without any direct human intervention. This machine learning process starts with feeding them good quality data and then training the machines by building various machine learning models using the data and different algorithms. The choice of algorithms depends on what type of data we have and what kind of task we are trying to automate.

As for the formal definition of Machine Learning, we can say that a Machine Learning algorithm learns from experience E with respect to some type of task T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.

For example, If a Machine Learning algorithm is used to play chess. Then the experience E is playing many games of chess, the task T is playing chess with many players, and the performance measure P is the probability that the algorithm will win in the game of chess.

What is the difference between Artificial Intelligence and Machine Learning?

Artificial Intelligence and Machine Learning are correlated with each other, and yet they have some differences. Artificial Intelligence is an overarching concept that aims to create intelligence that mimics human-level intelligence. Artificial Intelligence is a general concept that deals with creating human-like critical thinking capability and reasoning skills for machines. On the other hand, Machine Learning is a subset or specific 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 take some decisions on a specific problem using data. 

What are the types of Machine Learning?

Let’s see the different types of Machine Learning now:

1. Supervised Machine Learning

Imagine a teacher supervising a class. The teacher already knows the correct answers but the learning process doesn’t stop until the students learn the answers as well. This is the essence of Supervised Machine Learning Algorithms. Here, the algorithm learns from a training dataset and makes predictions that are compared with 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 achieves the required level of performance. Then it can provide the desired output values for any new inputs.

2. Unsupervised Machine Learning

In this case, there is no teacher for the class and the students are left to learn for themselves! So for Unsupervised Machine Learning Algorithms, there is no specific answer to be learned and there is no teacher. In this way, the algorithm doesn’t figure out any output for input but it explores the data. The algorithm is left unsupervised to find the underlying structure in the data in order to learn more and more about the data itself.

3. Semi-Supervised Machine Learning

The students learn both from their teacher and by themselves in Semi-Supervised Machine Learning. And you can guess that from the name itself! This is a combination of Supervised and Unsupervised Machine Learning that uses a little amount of labeled data like Supervised Machine Learning and a larger amount of unlabeled data like Unsupervised Machine Learning to train the algorithms. First, the labeled data is used to partially train the Machine Learning Algorithm, and 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 labeled and pseudo-labeled data.

4. Reinforcement Machine Learning

Well, here are the hypothetical students who learn from their own mistakes over time (that’s like life!). So the Reinforcement Machine Learning Algorithms learn optimal actions through trial and error. This means that the algorithm decides the next action by learning behaviors that are based on its current state and that will maximize the reward in the future. This is done using reward feedback that allows the Reinforcement Algorithm to learn which are the best behaviors that lead to maximum reward. This reward feedback is known as a reinforcement signal.

What are some popular Machine Learning algorithms?

Let’s look at some of the popular Machine Learning algorithms that are based on specific types of Machine Learning.

Supervised Machine Learning

Supervised Machine Learning includes Regression and Classification algorithms. Some of the more popular algorithms in these categories are:

1. Linear Regression Algorithm

The Linear Regression Algorithm provides the relation between an independent and a dependent variable. It demonstrates the impact on the dependent variable when the independent variable is changed in any way. So the independent variable is called the explanatory variable and the dependent variable is called the factor of interest. An example of the Linear Regression Algorithm usage is to analyze the property prices in the area according to the size of the property, number of rooms, etc.

2. Logistic Regression Algorithm

The Logistic Regression Algorithm deals in discrete values whereas the Linear Regression Algorithm handles predictions in continuous values. This means that Logistic Regression is a better option for binary classification. An event in Logistic Regression is classified as 1 if it occurs and it is classified as 0 otherwise. Hence, the probability of a particular event occurrence is predicted based on the given predictor variables. An example of the Logistic Regression Algorithm usage is in medicine to predict if a person has malignant breast cancer tumors or not based on the size of the tumors.

3. Naive Bayes Classifier Algorithm

Naive Bayes Classifier Algorithm is used to classify data texts such as a web page, a document, an email, among other things. This algorithm is based on the Bayes Theorem of Probability and it allocates the element value to a population from one of the categories that are available. An example of the Naive Bayes Classifier Algorithm usage is for Email 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 more popular algorithms in this category are:

1. K Means Clustering Algorithm

Let’s imagine that you want to search the name “Harry” on Wikipedia. Now, “Harry” can refer to Harry Potter, Prince Harry of England, or any other popular Harry on Wikipedia! So Wikipedia groups the 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 Clustering Algorithm in general uses K number of clusters to operate on a given data set. In this manner, the output contains K clusters with the input data partitioned among the clusters.

2. Apriori 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 also occurs. For example: IF someone buys a car, THEN there is a high chance they buy car insurance as well. The Apriori Algorithm generates this association rule by observing the number of people who bought car insurance after buying a car. For example, Google auto-complete uses the Apriori Algorithm. When a word is typed in Google, the Apriori Algorithm looks for the associated words that are usually typed after that word and displays the possibilities.