List of Major Machine Learning Algorithms for Beginners

The world is quickly changing due to artificial intelligence, and machine learning, which has an impact on every aspect of our everyday life.

From voice assistants that use NLP and machine learning to book appointments, look up events on our calendar, and play music to devices that are so accurate that they can anticipate our needs before we even consider them.

Computers can play chess, do surgery, and develop into smarter, more human-like machines with the aid of machine learning algorithms.

We are at a time of continual technological advancement, and by seeing how computers have developed through time, we can make predictions about what will happen in the future.

The democratization of computing tools and methods is one of the key aspects of this revolution that stands out. Data scientists have created powerful data-crunching computers during the last five years by effortlessly implementing cutting-edge methodologies. The outcomes are astonishing.

In this post, we’ll look closely at machine learning algorithms and all of their variations.

So, what are Machine Learning algorithms?

The approach used by the AI system to carry out its task—generally, predicting output values from given input data—is known as a machine learning algorithm.

A machine learning algorithm is a process that utilizes data and is used to create machine learning models that are ready for production. If machine learning is the train that carries out a job, then machine learning algorithms are the locomotives that move the work along.

The best machine learning approach to utilize will be determined by the business problem you’re attempting to address, the type of dataset you’re utilizing, and the resources you have available.

Machine learning algorithms are those that turn a data set into a model. Depending on the sort of problem you’re attempting to answer, the processing power available, and the type of data you have, supervised, unsupervised, or reinforcement learning algorithms can perform well.

So, we talked about supervised, unsupervised, and reinforcement learning, but what are they? Let’s explore them. 

Supervised, Unsupervised & Reinforcement Learning

Supervised Learning

In supervised learning, the AI model is developed based on the input that has been provided and the label that represents the predicted outcome. Based on the inputs and outputs, the model develops a mapping equation, and using that mapping equation, it forecasts the label of the inputs in the future.

Let’s say we need to create a model that can distinguish between a dog and a cat. Multiple photos of cats and dogs are fed into the model with labels indicating whether they are cats or dogs in order to train the model.

The model seeks to establish an equation relating the labels on the input photographs to those images. Even if the model has never seen the image before, after training, it can identify whether it is of a cat or a dog.

Unsupervised Learning

Unsupervised learning involves training an AI model only on inputs without labeling them. The model divides the input data into groups with related characteristics.

The future label of the input is then forecast depending on how closely its attributes match one of the classifications. Consider the situation where we must divide a group of red and blue balls into two categories.

Let’s assume that the balls’ other characteristics are identical, with the exception of color. On the basis of how it can divide the balls into two classes, the model looks for the characteristics that are different between the balls.

Two clusters of balls—one blue and one red—are produced when the balls are divided into two groups based on their hue.

Reinforcement Learning

In reinforcement learning, the AI model seeks to maximize overall profit by acting as well as it can in a particular circumstance. Feedback on its prior results helps the model learn.

Think about the scenario when a robot is instructed to select a route between points A and B. The robot first chooses either of the courses because it has no prior experience.

The robot receives input on the route it takes and gains knowledge from it. The robot can utilize input to fix the issue the next time it encounters a similar circumstance.

For instance, if the robot chooses option B and receives a reward, such as positive feedback, it understands this time that it must select way B to increase its reward.

Now finally what you all are waiting for, is the algorithms. 

Major Machine Learning Algorithms

1. Linear Regression

The simplest machine learning approach that deviates from supervised learning is linear regression. With the knowledge from independent variables, it is mostly utilized to resolve regression issues and create predictions on continuous dependent variables.

Finding the line of best fit, which can aid in predicting the outcome for continuous dependent variables, is the aim of linear regression. House prices, age, and wages are some examples of continuous values.

A model known as simple linear regression uses a straight line to calculate the association between one independent variable and one dependent variable. There are more than two independent variables in multiple linear regression.

A linear regression model has four underlying assumptions:

• Linearity: There is a linear connection between X and the mean of Y.
• Homoscedasticity: For every value of X, the residual variance is the same.
• Independence: Observations are independent of one another in terms of independence.
• Normality: When X is fixed, Y is normally distributed.

Linear regression performs admirably for data that can be separated along lines. It can control overfitting by using regularization, cross-validation, and dimensionality reduction techniques. However, there are instances where extensive feature engineering is required, which can occasionally result in overfitting and noise.

2. Logistic Regression

Logistic regression is another machine learning technique that departs from supervised learning. Its major use is classification, while it can also be utilized for regression problems.

Logistic regression is used to forecast the categorical dependent variable using the information from the independent factors. The goal is to classify outputs, which can only fall between 0 and 1.

The weighted total of the inputs is processed by the sigmoid function, an activation function that converts values between 0 and 1.

The basis of logistic regression is maximum likelihood estimation, a method for calculating the parameters of an assumed probability distribution given specific observed data.

3. Decision Tree

Another machine learning method that splinters off of supervised learning is the decision tree. For both classification and regression issues, the decision tree approach can be employed.

This decision-making tool, which resembles a tree, uses visual representations to show actions’ prospective results, costs, and repercussions. By dividing the data into separate portions, the idea is analogous to the human mind.

The data has been divided into distinct parts as much as we could granulate it. A Decision Tree’s main objective is to build a training model that can be used to forecast the class of the target variable. Missing values can be handled automatically using the Decision Tree.

There is no requirement for one-shot encoding, dummy variables, or other data pretreatment steps. It is rigid in the sense that it is difficult to add fresh data to it. If you got additional labeled data, you should retrain the tree on the entire dataset.

As a result, decision trees are a poor choice for any application that requires dynamic model change.

Based on the kind of target variable, decision trees are classified into two types:

• Categorical Variable: A Decision Tree in which the goal variable is Categorical.
• Continuous Variable: A Decision Tree in which the goal variable is Continuous.

4. Random Forest

The Random Forest Method is the next machine learning technique and is a supervised machine learning algorithm used extensively in classification and regression issues. It is also a tree-based method, similar to a decision tree.

A forest of trees, or many decision trees, is used by the random forest method to make judgments. When handling classification tasks, the random forest method employed categorical variables while handling regression tasks with datasets that contain continuous variables.

An ensemble, or mixing of many models, is what the random forest method does, which means predictions are made using a group of models rather than just one.

The ability to be used for both classification and regression problems, which make up the majority of modern machine learning systems, is a key benefit of the random forest.

Two different strategies are used by Ensemble:

• Bagging: By doing this, more data are produced for the training dataset. To lessen the variation in the forecasts, this is done.
• Boosting is the process of combining weak learners with strong learners by building successive models, resulting in the final model with maximum accuracy.

5. Naive Bayes

A binary (two-class) and multi-class classification issue can be solved using the Naive Bayes technique. When the method is explained using binary or category input values, it is simplest to grasp. An assumption made by a Naive Bayes classifier is that the existence of one feature in a class has no bearing on the presence of any other features. 

The above formula indicates:

• P(H): The likelihood that hypothesis H is correct. The prior probability is referred to as this.
• P(E): The likelihood of the evidence
• P(E|H): The likelihood that the hypothesis is supported by the evidence.
• P(H|E): The likelihood that the hypothesis is true, given the evidence.

A Naive Bayes classifier would take into account each of these characteristics individually when determining the likelihood of a certain result, even if these attributes are connected to one another. A Naive Bayesian model is simple to construct and effective for large datasets.

It is known to perform better than even the most complex categorization techniques while being basic. It is a collection of algorithms that are all based on the Bayes’ Theorem, rather than a single method.

6. K-Nearest Neighbors

The K-nearest neighbors (kNN) technique is a subset of supervised machine learning that can be used to address classification and regression issues. The KNN algorithm presumes that comparable objects can be found nearby.

I recall it as a gathering of like-minded individuals. kNN makes advantage of the idea of resemblance between other data points utilizing proximity, closeness, or distance. In order to label the unseen data based on the nearest labeled observable data points, a mathematical method is employed to determine the separation between points on a graph.

You must determine the distance between the data points in order to identify the nearest comparable spots. Distance measurements such as the Euclidean distance, Hamming distance, Manhattan distance, and Minkowski distance can be used for this. The K is known as the nearest neighbor number, and it is often an odd number.

KNN can be applied to classification and regression problems. The prediction made when KNN is used to regression issues is based on the mean or median of the K-most similar occurrences.

The result of a classification algorithm based on KNN can be determined as the class with the highest frequency among the K most similar occurrences. Every instance essentially casts a vote for their class, and the prediction belongs to the class that receives the most votes.

7. K-means

It is a technique for unsupervised learning that addresses clustering issues. Data sets are divided into a certain number of clusters—call let’s it K—in such a way that each cluster’s data points are homogeneous and distinct from those in the other clusters.

K-means clustering methodology:

• For each cluster, the K-means algorithm selects k centroids, or points.
• With the nearest centroids or K clusters, each data point forms a cluster.
• Now, new centroids are produced depending on the cluster members already present.
• The closest distance for each data point is calculated using these updated centroids. Up till the centroids do not change, this process is repeated.

It is quicker, more reliable, and simpler to comprehend. If there are issues, k-means’ adaptability makes adjustments simple. When the datasets are distinct or well isolated from one another, the results are best. It cannot manage erratic data or outliers.

8. Support Vector Machines

When using the SVM technique to classify data, raw data are shown as dots in an n-dimensional space (where n is the number of features you have). The data can then be easily classified because each feature’s value is then connected to a specific coordinate.

To separate the data and put them on a graph, employ lines known as classifiers. This approach plots each data point as a point in an n-dimensional space, where n is the number of features you have and each feature’s value is a specific coordinate value.

We will now locate a line that divides the data into two sets of data that have been categorized differently. The distances from the nearest points in each of the two groups will be the furthest apart along this line.

Since the two closest points are the ones that are most distant from the line in the example above, the line that divides the data into the two groups that were categorized differently is the middle line. Our classifier is this line.

9. Dimensionality Reduction

Using the approach of dimensionality reduction, training data might have fewer input variables. In simple terms, it refers to the process of shrinking the size of your feature set. Let’s imagine your dataset has 100 columns; dimensionality reduction will decrease that amount to 20 columns.

The model automatically grows more sophisticated and has a bigger risk of overfitting as the number of features rises. The largest issue with working with data in greater dimensions is what is known as the “curse of dimensionality,” which occurs when your data contains an excessive number of characteristics.

The following elements can be used to accomplish dimensionality reduction:

• To find and choose pertinent characteristics, feature selection is employed.
• Using already existing features, feature engineering manually creates new features.

Conclusion

Unsupervised or supervised machine learning is both possible. Choose supervised learning if your data is less abundant and well tagged for training.

Large data sets would often perform and produce better outcomes using unsupervised learning. Deep learning methods are best if you have a sizable data collection that is readily available.

Reinforcement learning and deep reinforcement learning are some topics you studied. Neural networks’ characteristics, uses, and constraints are now clear to you. Last but not least, you considered the options for different programming languages, IDEs, and platforms when it came to creating your own machine learning models.

The next thing you need to do is to start studying and using each machine learning approach. Even if the subject is broad, any topic can be understood in a few hours if you focus on its depth. Each subject stands alone from the others.

You must think about one issue at a time, study it, put it into practice, and use a language of your choice to implement the algorithm(s) in it.