With the advancement in technology, conventional practices and applications have been widely transformed. Artificial intelligence and machine learning are providing new means to perform the job effectively. Even in our daily lives, there are various use cases for machine learning algorithms.
Have you ever noticed how computer applications could easily play complex games such as chess, tennis and even perform surgeries with the help of robotics.
How is all of this possible?
It’s all because of AI and machine learning.
They not only perform various tasks but also improve their performance using existing and new data. Machine learning is the future, and soon it’ll be in every sector, from health to education, e-com to retail.
But before diving deeper, you should get clarity on the basics of machine learning. It lays a strong foundation for future understanding. Here are the top 10 machine learning algorithms every beginner needs to know.
So let’s get started.
What Is Machine Learning?
Machine learning is a subset of artificial intelligence (AI). It involves the development of algorithms allowing computers to learn patterns and make decisions or predictions without being explicitly programmed. It heavily relies on data to improve overall performance.
Here’s a quick glimpse of how this process goes.
Machine learning models = Creates models → Learn from data → Make predictions or decisions.
Let’s understand it with an example.
Have you ever noticed that some emails automatically go to your spam folder? Why does that happen?
It’s because Gmail’s system automatically recognizes these emails as spam. It recognizes patterns such as common spam words, email structure, or sender characteristics during the training phase. Based on these learning patterns, the model can easily identify whether the email is spam.
Types of Machine Learning Algorithm
Before learning machine learning algorithms, you should know about their variants. Each algorithm has a specific purpose that you must keep in mind. Let’s look at all of these in detail.
| Aspect | Supervised Learning | Unsupervised Learning | Reinforcement Learning |
| Training Data | Labeled | Unlabeled | Rewards and punishments |
| Objective | Predict an output | Discover patterns | Learn to make decisions through trial/error |
| Feedback | Correct output provided for training | No explicit feedback during training | Delayed feedback through rewards |
| Use Cases | Classification, Regression | Clustering, Dimensionality Reduction | Game playing, Robotics |
| Examples | Linear Regression, SVM, Neural Networks | K-Means Clustering, PCA, Autoencoders | Q-Learning, Deep Q Networks |
10 Popular Machine Learning Algorithms
Now let’s discuss popular machine learning algorithms beginners must know. It’ll help you understand more about machine learning and how it works.
1. Linear Regression
It is considered one of the simplest and most popular machine learning algorithms. It is commonly used for predictive analysis. With logistic regression, users can study the relationship between:
- Dependent variables
- Independent variables
It is done by defining a line and its equation. The line is known as the regression line, and the equation for this is:
y=mx+c
Now let’s look at what
this stands for:- y = dependent variable
- x = independent variable
- m = slope
- c = intercept
Another important thing you should remember is that m is calculated by minimizing the sum of the squared distance between two points and the regression line.
This algorithm helps predict movements and changes in the stock market.
2. Logistic Regression
It is a supervised machine learning algorithm using binary values 0 and 1. Logistic regression is used in a lot of areas, such as predicting values that are categorical and discrete. Solving classification problems can be extremely difficult, but you can do it easily with a logistic regression algorithm.
In logistic regression a transformation function is crucial. This function is:
h(x) = 1 / (1 + ex)
It forms an S-shaped curve. Another important thing that you should know is that it lies between 0 and 1.
If you want to predict the probability of an event, which is either yes or no, then you can use logistic regression. Some of the common examples include:
Will a debtor default or not?
Will the patient have a heart attack or not?
3. Decision Tree
In this machine learning algorithm, there is a classification of categorical and continuous dependent variables.
But what will the decision tree do?
It’ll divide the data into two or more sets. These sets are similar and are based on variables and attributes. The starting point of the decision tree is the root node, and the ending point is the lead node. Meanwhile, the branches are depicting the decision rules and conditions. The internal node shows the features of the dataset.
There are several real-world applications for this algorithm. Some of these include the identification of various cells, such as cancerous and non-cancerous cells. Recommending products to potential buyers.
4. Support Vector Machine
With this algorithm, you can plot raw data. But in a much more precise manner. It’s plotted as points in an n-dimensional space. But what is N?
N shows the number of features that have been defined so far. Likewise the value of every feature is connected with special coordinates. The SVM algorithm will make a hyperplane or a decision boundary.
It’ll separate or categorize data into different classes. Meanwhile, the support vectors act as data points that will define the hyperplane. Now, it’s for the classifiers to split data and plot into graphs accordingly.
The real-life applications of support vector machines include face detection, classification of images, and much more.
5. Naive Bayes
This algorithm is based on a famous theorem, also known as Bayes’ theorem. It is used to calculate the probability of an event that may occur. The term naive is used because the variables are independent of each other. It is a supervised machine-learning algorithm. It is based on the conditional probability.
Let’s look at the equation.
P(A|B) = P(B|A) * P(A)P(B)
But what does all the terms stand for?
- P(A|B) = posterior probability. It calculates the probability of event A with respect to data B.
- P(B|A) = The chances or likelihood. The probability or chances of data B if event A happens.
- P(A) = Class prior probability.
- P(B) = Prediction of prior probability.
If you want a solution for the classification of large datasets, then this algorithm is ideal.
6. K-nearest Neighbors
In this supervised learning algorithm, there’s classification and regression of data. It tells the likelihood of a data point being connected with another group. But how can you determine that?
It’s done by analyzing overall points and referencing them to a single data point. This algorithm assumes similarities that can be between data points. Based on this, these are classified and plotted separately on the graph.
It is also known as the lazy-learner algorithm. The primary reason behind that is that it uses the entire dataset for training. It is applied for various purposes such as medical, facial recognition, and text mining.
7. K-means Clustering
It’s an unsupervised machine-learning algorithm. It can solve complex clustering problems. But how’s that possible?
The datasets are classified into K-number clusters. It’s on the basis of similarities as well as dissimilarities between the data points. Now, the process has to be repeated on and in until every cluster has a specific data point.
The center point of each cluster is centroids. Now, you’ve to calculate the distance from a data point to a centroid.
Assign the data to a cluster that is closest to the centroid. But what happens next?
The algorithm will create a new centroid. The entire process is repeated until the centroids are not interchanged. This algorithm is widely applied in real-life applications such as image compression, segmentation, and more.
8. Random Forest
In this algorithm technique, ensemble learning techniques are followed. A lot of algorithms are combined to accomplish better results. But what is a random forest?
It’s a collection of various decision trees. They segment and then classify new objects with respect to their attributes.
- Trees = Votes for class
- Forest = Classification which has the highest number of votes.
A random forest most often has 64-128 trees. At the top of the decision tree, input is added. But it’ll travel down to subsets according to attributes and features.
It is used for the prediction of the behavior of customers, diagnosis, and fluctuations in the market, etc.
9. Apriori Algorithm
With the help of this unsupervised learning algorithm you can find answers to various association problems. But what’s the purpose of association problems?
It’s to figure out associations and relations between large sets of data items. Frequent item sets are used for generating association rules. These rules will determine the level of connection between two items. The algorithm heavily works on databases that consist of information that’s comparable.
It’s widely used in market analysis to determine which products can be bundled, the reactions of various drugs in patients, etc.
10. Principal Component Analysis
This unsupervised learning technique is widely used for dimensionality reduction. The algorithm will minimize the dataset dimensionality, such as reducing the number of similar attributes.
A statistical process is followed to transform observations with correlated features into completely different linear uncorrelated features. Variance is checked to ensure how well an attribute connects with others.
High Variance = Enhanced split between classes and less dimensionality.
Which Machine Learning Algorithm Should I Use?
It’s a typical question that many beginners ask, especially when you have various machine-learning algorithms. Finding the best algorithm can be difficult. The simple answer to this question is:
It Depends.
Let’s dive deeper to understand. When choosing a machine learning algorithm, you should consider four primary factors.
- What is the size, quality, and nature of data?
- What’s the available computational time?
- How urgently do you need to complete the task?
- What’s the main goal of acquiring this data?
Even most experienced data scientists face difficulty choosing the best algorithm for a specific task. That’s why you should ask these questions. It’ll help you understand the purpose of choosing an algorithm. Your aim should be finding a suitable algorithm that aligns with the purpose.
Final Words
It shows how machine learning algorithms play a crucial role in our lives. It’s important to experiment with various algorithms and see which works in your favor.
You should check the category and then analyze what’s the best use case according to your needs and requirements. Because these models offer different performance types, you can make the best use of your data by utilizing them.
