There’s no limit to what Machine Learning (ML) algorithms have revolutionized! Several fields, such as Speech Recognition, Recommendation Systems, Computer Vision and Natural Language Processing have seen its power.
This post will focus on ML algorithms, exploring their basics and types. If you want to know more about Machine Learning, this post is a must-read for you!
What is a Machine Learning Algorithm?
ML algorithms are essentially sets of instructions that allow computer programs to learn from data. These mathematical sets are like recipes that computers can use to analyze data, identify patterns and make predictions.
How to Build Machine Learning Algorithms?
The process starts with building a model based on ML algorithms that learn from labeled datasets. This dataset will contain input data and output labels to help the algorithms in pattern recognition.
After training, the algorithm undergoes evaluations to measure its performance. Likewise, these evaluations assess its ability to work with new input layers.
This assessment identifies areas for improvement to fine-tune its parameters. The goal is to create a new model that predicts or classifies unseen data accurately.
If the training and evaluation phases are successful, the ML algorithm can predict or decide on new data. In the prediction phase, it’ll apply the acquired knowledge to new instances for valuable insights and recommendations.
Types of Machine Learning Algorithms
Supervised Learning Algorithms
1. Linear Regression
Within Supervised Learning, Linear Regression is a popular ML algorithm for predicting continuous values.
This algorithm helps to determine the relationship between input and target variables. It does so by finding the best-fit line through data analysis and calculating coefficients.
The regression works by using two variables. First, there’s one that you can control or change, called the independent variable. Likewise, there’s another that you want to predict or explain, called the dependent variable.
Let’s say you want to know how the temperature affects ice cream sales. The temperature is the independent variable and the ice cream sales are the dependent variable.
2. Decision Trees
Decision Tree Algorithms are most effective for regression and classification tasks.
They work by creating a tree-like model of decisions based on input variables. This structure allows it to reach a prediction or classification.
Internal nodes in the tree structure represent these decision points. Likewise, the final destinations, called leaf nodes, represent the predicted value or class.
By following a series of questions through the tree, the model arrives at a conclusion based on the provided data.
3. Support Vector Machines
To sum it up, Support Vector Machines (SVM) help classify data.
SVMs are especially good for working with complex data that can't be separated into groups in a straight line.
As a result, they’re often used for a wide range of things like image recognition and anomaly detection.
4. Logistics Regression
Logistic Regression allows to find out how likely something is to happen based on some data and logistic function.
You might want to know if someone will use a product or not based on their age, gender, income and so on. Logistic Regression can help you by giving you a binary classification between 0 and 1 for each person.
To use Logistic Regression, you’ll need data with two possible binary outcomes, like yes/no, true/false or 0/1. The goal will be to find out how to combine the independent variables to get the outcome probability.
To do so, Logistic Regression uses a formula with some parameters. These are numbers that can be changed to make the formula better fit the data. The formula looks like this:
Let’s say e is a special number about 2.718, and a, b₁, b₂, (…) and bn are the parameters that need to be adjusted. The x₁, x₂, … and xn are the independent variables, such as age, gender, income, etc.
The formula will take these variables, multiply them by the parameters and add them all together. It will then subtract the result from e and divide one by the result to give you the probability of the outcome.
Unsupervised Learning Algorithms
1. Clustering Algorithms
Rather than relying on predefined labels, Clustering Algorithms group similar data points based on their characteristics. They identify patterns based on their similarity or proximity.
In customer segmentation, Clustering Algorithms can group customers with similar purchasing behaviors or demographics. This information will help teams tailor marketing strategies or recommendations for each group.
2. Association Rule Learning Algorithms
Association Rule Learning is an Unsupervised Learning algorithm that finds associations between variables in large datasets. These algorithms locate patterns, rules or correlations that happen frequently.
They’re often used in retail to analyze market baskets and identify frequently purchased items. The gathered data can be used to optimize store layouts, place products better, and suggest cross-selling opportunities.
In eCommerce, these algorithms can personalize customer recommendations by identifying product relationships.
For instance, by analyzing browsing or purchase history, the algorithm suggests related items. As a result, it can help increase customer engagement and fraud detection.
3. K-Means Clustering
K-means clustering groups data based on similarities.
Let’s say you want to group customers based on their preferences, habits or demographics. K-means clustering can help you divide your data into a number of groups that you specify (K or K-nearest neighbor).
This technique can help you understand your data better, segment your customers and optimize your resources. As a result, K-means clustering is widely used in marketing, finance, education and science.
Reinforcement Learning Algorithms
1. Q-Learning
This Reinforcement Learning algorithm allows an agent to learn through trial and error in a dynamic environment. Q-Learning learns an action-value function, Q-values, to maximize cumulative rewards over time.
It mostly applies to circumstances when an agent needs to make sequential decisions based on a declared strategy. Due to this focus, it’s widely used in robotics, game development and optimizing business processes.
2. Deep Q Network
Deep Q Network (DQN) is an extension of Q-Learning that leverages Deep Neural Networks. Its main differential is its ability to handle complex and high-dimensional input spaces for principal component analysis.
DQN algorithms have, for instance, successfully played Atari games or controlled self-driving cars.
By combining RL with Deep Learning, DQN has pushed the boundaries of what is possible in training intelligent agents!
Conclusion
As you can see, the field of ML Algorithms is quite extensive. Each algorithm has its specific strengths that make it key for specific projects and applications.
By understanding them, you can leverage their power and uncover valuable insights in your projects.
Get in touch with Capicua to find the right ML algorithm for your idea!
