Introduction
Machine learning has become an important tool in today’s technological era, with applications ranging from targeted advertisements to self-driving cars. However, it can seem difficult to distinguish between all the many machine learning algorithms out there. This article will discuss ten of the most popular machine learning algorithms and their applications. Without any further ado, let's see what are those 10 powerful machine learning algorithms.
Artificial Neural Networks
Artificial Neural Networks are one of the most powerful machine learning algorithms. They're popular because of the ability to learn complex relationships between data. The algorithm is inspired by biological neural networks in that it learns by example rather than being explicitly programmed. To use artificial neural networks, you need a large set of data (called training data) with known outputs (called labels). For example, if you wanted to predict whether or not someone has a heart disease based on their age, weight, height, medical conditions, etc., you would need two datasets: one for your training data and another for your testing data. The training dataset would contain information about each person's age, weight, height, medical conditions, etc., as well as whether or not they were a heart patient.
An Artificial Neural Network consists of a large number of nodes(neurons) that are connected to each other. The information from one node is passed on to another until it reaches a final node, which then produces an output value. The connections between nodes can be weighted. This means that some connections will be stronger than others and will have more influence on the final output value. In order for an artificial neural network to learn, it must go through a process called training, where we adjust its weights so that it correctly predicts our training data. Once trained, we can then use our neural network to predict outputs for new data (which isn't part of our training set).
In addition to being able to learn complex relationships between data, artificial neural networks are also really good at finding patterns in unstructured data like text or images.
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| ANN Diagram |
Applications of Artificial Neural Networks
- Used to identify credit card frauds. A neural network is trained on thousands of different examples of normal transactions and abnormal ones (fraudulent ones). After training, it can then be used to classify new transactions as either normal or fraudulent with a high degree of accuracy.
- Used in image recognition systems, such as self-driving cars or face detection software. The system is trained on a large number of images that have been manually labeled by humans as containing an object. After training, it can then be used to label new images as containing an object or not with a high degree of accuracy, without requiring human intervention for each example.
- Used to predict stock prices. A neural network is trained on historical data about stock prices and other factors that affect them (e.g., company earnings, news stories). After training, it can then be used to predict future changes in stock price with a high degree of accuracy.
- Used to analyze customer sentiment from social media posts or product reviews on e-commerce websites like Amazon or eBay.
Support Vector Machines
Support Vector Machines(SVM) is a popular and very powerful machine learning algorithm capable of performing both regression and classification. It works by finding an optimal hyperplane that separates two classes with maximum margin. SVM can be used to solve binary classification problems as well as multi-class classification problems.
There are mainly two types of SVM, namely Linear and Non-linear, the linear SVM is used when there is a linear relationship between a dependent variable and independent variables. The non-linear SVM is used when there is no such relationship between a dependent variable and independent variables.
A simple example of using an SVM to solve a binary classification problem would be to use it to classify whether a given email is spam or not spam. To do that we need to train our algorithm with lots of emails labeled as spam or not spam. Once we have done that we can run our algorithm on any email whose label we don't know and it will give us an answer for whether it's spam or not spam.
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| SVM Diagram |
Applications of Support Vector Machine
- It is used in image recognition, bioinformatics, natural language processing, etc.
- It is used in data mining to create models that are useful for classification or regression problems.
- It can be used to predict a numerical value based on a set of input variables.
- It can be used to find patterns in data sets by using techniques like principal component analysis (PCA).
Naive Bayes Classifier
Naive Bayes Classifier works on the concept of probabilistic models. It is one of the most popular algorithms used in machine learning. The model assumes that each feature (or attribute) is conditionally independent given a class label. The algorithm uses Bayes’ theorem to predict probabilities for unseen data based on training data. The model can be easily understood by considering an example: Suppose we have a training set consisting of 2 classes - 'spam' and 'not spam'. We are given features like word frequencies, email addresses, etc., for both these classes. Based on our training set, we can predict what will be the probability of a new email being spam or not spam using a naive Bayes classifier algorithm.
Naive Bayes is often used in text classification problems. It can be used to predict a label for a document based on features like words present in that document. The model works well when there are few features available for each data point, making it ideal for text classification problems where we don't have many attributes associated with each data.
Applications of Naive Bayes
- Predicting spam emails
- Sentiment analysis in social media platforms
- Predicting movies that a user will like
- Classification of diseases based on symptoms
- Credit card fraud detection
- Sentiment analysis of tweets (based on emotions)
- Classifying news articles into different categories like sports, entertainment, etc...
- Document classification in email marketing systems
Random Forest Classifier
When you have a lot of variables and need accuracy, Random Forest Classifier is your best friend. It is an ensemble learner (part of a class of algorithms called Boosting) which means it’s made up of several decision trees, allowing it to be more accurate than most single-tree learning algorithms. A forest classifier doesn’t always perform as well as algorithms like SVM or logistic regression when accuracy isn’t your top priority; it does, however, give you better speed/accuracy tradeoffs than most other learning algorithms.
The Random Forest Classifiers are capable of handling multiple classes directly. It is also quite efficient in terms of memory usage, which makes it a good choice for problems where training sets are very large. This algorithm can be used for both regression and classification problems.
It can handle both numerical and categorical variables. To use it on a dataset, you must convert all your features into numerical format (usually by discretizing them). However, if you have many categorical variables that take only two values (e.g., Yes/No), then it’s better to use Logistic Regression instead as it will be more accurate than Random Forest Classifier on such datasets.
.png "Random Foresr Classifier Diagram") |
| Random Forest Diagram |
Applications of Random Forest Classifier
- Image recognition in image processing (supervised machine learning)
- Face detection in images (supervised machine learning)
- Classification of handwritten digits (supervised machine learning)
- Natural language processing (NLP), such as text classification, authorship attribution, etc.(unsupervised machine learning)
K-Nearest Neighbors Classifier
K-Nearest Neighbors or simply KNN works by assuming the similarity of an object to another based on a number of other objects it is similar to. For example, if you are trying to predict whether a person will be a customer of your product, you could base that prediction on whether other people who have purchased your product have been customers. This is a very simple algorithm, but it can be extremely effective in situations where there are not many variables to take into account.
Sometimes KNN is also called a lazy learner algorithm since it will not learn at the time of training instead, it memorizes, ie, it stores the data first and perform an operation on the dataset at the time of classification. This makes it very fast in terms of execution. It can be used for both supervised and unsupervised learning.
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| KNN Example Diagram |
Applications of K-Nearest Neighbors
- K-Nearest Neighbors is commonly used in Fraud Detection, where it can be used to detect a fraudulent transaction based on similar transactions that have occurred previously. The algorithm can also be used to detect anomalies in data by comparing it with similar datasets. For example, KNN could be used to identify if a bank account is being used for fraudulent activities by analyzing similar accounts that have been flagged as being fraudulent.
Gradient Boosted Decision Trees
Gradient Boosted Decision Trees or GBDT is a powerful machine learning algorithm that can handle both regression and classification problems. It’s also one of the most popular algorithms in machine learning, as it offers both high accuracy and fast training times. The basis of GBDT lies in decision trees, which are created by recursively splitting data into smaller subsets based on certain criteria. With each split, more information about each subset is revealed, allowing for easier predictions. In order to make these predictions more accurate, GBDT uses gradient boosting to optimize decision trees based on previous predictions. This way, mistakes can be corrected before they become large enough to impact future decisions.
A GBDT is basically a sequence of decision trees, with each tree in that sequence using information from previous trees to make more accurate predictions. These decisions are based on two types of features: continuous features (features that can take any value within a range) and categorical features (features that can only take on one value). To create these decision trees, you must first create an ensemble of decision trees by combining different subsets of data. Then, you train each tree using gradient boosting.
Applications of Gradient Boosted Decision Trees
- Fraud Detection (credit card fraud, insurance fraud)
- Predicting Stock Prices (how will a company’s stock price change over time?)
- Image Recognition (recognizing faces, identifying what is in an image)
- Natural Language Processing (automatically tagging parts of speech in a sentence)
- Sentiment Analysis (determining whether or not someone is happy or sad about something)
- Recommendation Systems (recommending products to customers based on their previous purchases)
- Classification Problems (identifying if an email is spam or not spam)
Deep Neural Networks/Deep Learning
Deep Neural Networks are a part of Deep Learning algorithms, which are really powerful in machine learning. Deep learning has been around for decades but it's only recently that computers have become fast enough to use it effectively. The basic idea behind deep neural networks is to create multiple layers of artificial neurons where each layer learns from the previous layer and passes its knowledge to the next layer. This allows the neural network to learn complex patterns in data as the human brain does. One of the most popular applications for deep neural networks is image recognition systems, such as self-driving cars or facial recognition software.
Every neuron contains a set of weights that determine how much it affects other neurons. When a new data point is fed into a neural network, every neuron receives input from all previous layers. Each layer of neurons then computes its output by applying a mathematical function to its input, using its own set of weights. This process continues until each layer has calculated its output, producing a final result for that data point. The outputs from each layer are then combined to produce an overall result for that data point.
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| Deep Neural Networks Diagram |
Applications of Deep Neural Networks
- For building smart and intelligent chatbots
- For recognizing images, videos, text, and audio files in applications like Google Photos, Apple's Siri, Amazon Alexa, etc.
- For identifying malicious or inappropriate content on websites by analyzing images, videos, or text files with Natural Language Processing (NLP) tools like TensorFlow, etc.
Ensemble Methods (Bagging, Boosting)
Ensemble models are those types in which several models are combined for predicting the final output. The most common type of ensemble methods is Bagging (Bootstrap Aggregation) and Boosting. In bagging, a number of base classifiers are trained using a bootstrap sample with a replacement technique. The major advantage of bagging is that it reduces variance among individual classifiers while maintaining their generalization performance. On the other hand, boosting refers to a set of algorithms that iteratively improve a base classifier by sequentially adding weak learners that correct mistakes made by previous iterations. The major advantage of boosting is its ability to generate accurate predictions even when given noisy data or an incomplete training set, but it has high variance as compared to the bagging method.
Applications
- Bagging can be used for both classification and regression problems. Bagging is also known as Bootstrap Aggregation or Stochastic Aggregation. Bagging can be used to reduce variance in model predictions by averaging multiple models of different designs (base learners).
- The advantage of bagging is that it reduces variance among individual classifiers while maintaining their generalization performance.
Linear Regression
.png "Linear Regression") |
| Linear Regression |
Applications of Linear Regression
- Predicting house prices using area, number of rooms, etc.
- Predicting stock price based on various factors such as inflation rate, GDP growth rate, etc.
- Predicting employee attrition based on various factors such as salary hike, bonus structure, etc.
- Predicting customer churn based on various factors such as billing cycle, product usage pattern, etc.
- Predict sales for a particular region and for a particular time period using weather data and historical data about that region or time period.
Logistic Regression
Logistic Regression could be one of the most popular and powerful algorithms in machine learning. It is used to predict a binary outcome (0 or 1) based on continuous input variables. This algorithm is often used in spam detection, handwriting recognition, face detection, and credit card fraud detection. The key thing about logistic regression is that it can learn non-linear relationships between input variables and an output variable. For example, you can use logistic regression to determine if a person will buy your product or not based on their age, income level, gender, etc.
Logistic Regression can be also used for multiclass classification which is known as Multinomial Logistic Regression or Softmax Regression. The only difference between these two types of regression is that in the case of multinomial logistic regression, you have more than two classes. For example, let’s say you want to predict if a person will buy your product or not (0) or if they will buy your product in the next 6 months (1). In such a case, you would use multinomial logistic regression.
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| Logistic Regression |
Applications of Logistic Regression
- Predicting the probability of breast cancer (binary classification)
- Predicting weather conditions (binary classification)
- Stock market prediction (binary classification)
- Sentiment analysis
- Detecting fraudulent credit card transactions
- Pima Indians Diabetes data set (predict whether a person has diabetes or not)