Everything you Need to Know About Machine Learning Algorithms

Machine Learning, we’ve all heard these buzzwords being used in nearly all tech products, but what is it exactly? Most companies these days make use of the insights brought along by big data. Companies have always had access to that data; however, manually analysing it is challenging due to the sheer volume of data. Machine learning is a subset of Artificial Intelligence capable of analysing vast amounts of data to find insight and connections in them. There are several machine learning algorithms. Some specialise in data analysis, while others specialise in pattern recognition. This blog will explore the various ML algorithms you need to know!

What is Machine Learning?

Machine Learning, or ML, focuses on building programs that are not explicitly programmed. Rather, it learns from data on its own. It is based on the working principle of the human brain. 

ML is a field of study that uses algorithms to,

• Analyse data
• Identify patterns
• Make decisions 

Using large amounts of data and applying ML algorithms can be used to make predictions and decisions that are more accurate than those made by humans.

Types of Machine Learning Algorithms

ML algorithms are the building blocks of modern AI (Artificial Intelligence). ML algorithms are used in a variety of applications, such as, 

• Image recognition
• Natural Language Processing (NLP)
• Robotics 

Supervised Learning Algorithms

Supervised learning algorithms use labelled data to make forecasts. These algorithms use the labelled data to learn how to map inputs to outputs. Labelled data helps the algorithm to learn and recognise patterns in the data and make predictions about new data. 

• Linear Regression

 

• Linear regression algorithms are used to forecasts a continuous output.
• It is used to model the relationship between a dependent variable and one or more independent variables.
• For example 
• Predicting home prices based on features like,
• Size
• Market value 
• Location

• Logistic Regression

• Logistic regression algorithms are used to predict a categorical output. 
• It is used to model the probability of a certain class or event occurring.
• For  example
• Logistic regression is used to determine whether a transaction is fake or not.

• Decision Trees

• Decision trees are used to make rules-based predictions. 
• The algorithm uses a tree-like structure to make decisions based on the data.
• For example 
• Predicting a person's fitness level based on factors such as, 
• Age
• Diet
• Amount of physical activity

• Support Vector Machines (SVM)

• SVMs are used to classify data.
• The algorithm finds a hyperplane that best separates the data into two classes.
• For Example 
• SVM is used to classify genes, individuals according to their genes, and other biological issues.

Supervised learning algorithms are an important part of machine learning and can be used to predict new data. 

Unsupervised Learning Algorithms

Unsupervised learning algorithms are algorithms that work without any labelled data or output data. These algorithms explore and analyse data to discover patterns and insights. 

• Clustering

• Clustering algorithms are used to group data elements with similarities together.
• This algorithm is useful for the following.
• Exploring data
• Finding patterns
• Understanding the structure of the data
• For example 
• It can be used to identify customers who bought similar products.

• Principal Component Analysis (PCA)

• PCA is a technique used to reduce the dimensionality of data.
• It reduces the number of variables in a dataset while preserving the most important information. 
• For example 
• It is applied to reduce the number of dimensions in healthcare data.

• Association Analysis 

• Association analysis is used to uncover relationships between items in a dataset and can be used to identify patterns and trends that would otherwise be difficult to detect.
• It can identify correlations between variables, such as customer purchases, and use that data and other factors to discover relationships between items in a dataset. 
• Example, 
• Predicting that people that buy a new home are most likely to buy new furniture.

Unsupervised learning algorithms are useful tools for exploring data, finding patterns, and understanding the structure of the data. They are also used for feature extraction and anomaly detection.

Semi-Supervised Learning Algorithms

Semi-supervised learning algorithms use labelled and unlabeled data to train a model. These algorithms are useful when there is a limited amount of labelled data but a large amount of unlabeled data. 

They classify data using labelled data to learn the structure. And uses unlabeled data to fill in the gaps. 

• Self-Training Algorithms 

• Self-training algorithms use a single model to learn from labelled and unlabeled data. 
• This type of algorithm is useful for situations where labelled data is scarce or expensive to obtain.
• For example 
• Image recognition
• Natural Language Processing (NLP)
• Robotic sensors.

• Co-training Algorithms 

• Co-training algorithms use two models to learn from both labelled and unlabeled data.
• The two classifiers are then used to iteratively label the unlabeled data until the accuracy of the classifiers reaches a certain level.
• For example,
• Text classification
• Image classification
• Sentiment analysis

• Graph-based Algorithms 

• Graphs are a powerful data structure for representing relationships between objects, and graph-based algorithms can be used to uncover patterns and insights in complex data.
• Graph-based methods use a graph-like structure to learn from labelled and unlabeled data.
• For example, 
• Despite using numbers, this problem is a combinational puzzle that can be resolved with graph colouring rather than a mathematical one.

• Generative Algorithms 

• Generative models use a generative process to learn from labelled and unlabeled data.
• Generative algorithms are based on the idea that data can be generated from a set of parameters.
• For example 
• Drug discovery to create a hyperplane and divide groups of compounds based on the feature selector. 

Semi-supervised learning algorithms are becoming increasingly popular as they can learn from labelled and unlabeled data and can be used to improve the accuracy of machine learning models.

Reinforcement Learning (RL) Algorithms

Unlike supervised learning algorithms, which use labelled data to make predictions, RL algorithms use rewards and punishments to learn how to take the best action in a given situation. RL algorithms are used in a wide range of applications,

• Q-Learning

• Q-learning is a model-free RL algorithm that uses a reward-based system to learn the best action for a given state. 
• It is an effective algorithm for learning from experience and can be used to solve complex problems.
• Example 
•  Advertisement recommendation system.

• SARSA

• SARSA is a model-based RL algorithm that uses a combination of rewards and punishments to learn the best action for a given state. 
• It is based on learning from experience and is used to find the best action to take in a given state. 
• Example 
• Game playing is where a player will be rewarded for good conduct and punished for bad.

• Deep Q-learning

• Deep Q-learning is an extension of Q-learning that uses deep neural networks to learn the best course of action for a given state.
• It uses a deep neural network to approximate the Q-function. The algorithm can learn from experience and adapt to changing environments.
• Example, 
• Navigating autonomous vehicles to improve perception and trigger kinematic movements in autonomous vehicles.

RL algorithms are powerful tools for solving complex problems as they can learn from their mistakes and adapt to changing situations, making them ideal for dynamic decision-making applications. 

Additional Tools and Techniques

Along with these algorithms, a variety of techniques are used in ML, such as, 

• Feature engineering
• Data preprocessing
• Model evaluation

These tools and techniques ensure that the machine learning algorithms are trained properly and produce accurate results.

Feature Engineering

Feature engineering is an essential part of the ML process, as it helps to identify patterns and relationships between variables that can be used to make predictions. 

• Feature engineering involves transforming raw data into features that ML algorithms can use to make predictions.
• It involves the following steps.
• Selecting important features
• Transforming them into a suitable format
• Engineering new features from existing ones
• Some of the most popular ML algorithms that require feature engineering are: 
• Decision Trees
• Support Vector Machines (SVMs)

Data Preprocessing

Preprocessing techniques help reduce the data's complexity, make it easier to analyse, and improve the model's accuracy.

• Preprocessing techniques used are as follows.
• Normalisation
• Feature selection
• Feature extraction
• Dimensionality reduction 

Model Evaluation

Model evaluation measures the performance of models and determines which algorithms are best suited for data and objectives. It is important to evaluate a model before deploying it in production, as it can help identify any potential problems or areas of improvement. 

The top model evaluation algorithms that you need to know for include the following: 

• Cross-Validation

• Cross-validation is a technique used to evaluate a model’s performance by splitting the data into a training and a test set. 
• The model is trained on the training set and then tested on the test set. 

• Confusion Matrix

• A confusion matrix is a tool used to evaluate the performance of a classification model.
• The table displays the number of true positives, false positives, true negatives, and false negatives that the model produced. 

• Receiver Operating Characteristic (ROC) Curve

• The ROC curve is a graph that plots the true positive rate against the false positive rate for a given model.
• The curve helps to identify the model’s ability to distinguish between two classes. 

TensorFlow

TensorFlow is an open-source software library for machine learning developed by Google. 

• It is used for numerical computation using data flow graphs, which enables developers to create large-scale neural networks with many layers. 
• TensorFlow has a comprehensive library of algorithms, tools, and utilities for building, training, and deploying machine learning models. 
• It also offers a wide range of APIs for developing applications with machine learning. 

PyTorch

PyTorch is an open-source machine learning library developed by Facebook’s AI Research lab. 

• It is a deep learning framework that provides a seamless path from research to production. 
• PyTorch is designed to be intuitive and flexible, allowing developers to prototype models quickly and experiment with new ideas. 
• It strongly focuses on deep learning, allowing developers to quickly develop and deploy complex neural networks.
• PyTorch also provides strong support for distributed training, allowing developers to scale their models across multiple machines. 
• PyTorch is also highly extensible, allowing developers to customise and extend the framework to suit their needs. 
• It also provides a wide range of tools for debugging and optimising models. 

Conclusion 

ML algorithms are an essential part of modern data science. Which can uncover hidden insights, automate tedious tasks, and make predictions with the right algorithms. While there are many machine learning algorithms, the ones mentioned in this blog are some of the most popular and widely used. 

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