Table of Contents
Introduction
With the advent of automation and Artificial Intelligence (AI), we have gotten incredibly dependent on automated services and machine assistance. Machine Learning (ML) is one of the fundamental methods developers and data scientists use to power these machines, systems, and services.
ML also helps solve many business problems and mathematical challenges which can be time-consuming and ineffective if humans do it manually. ML allows machines to predict outcomes and figure out values that we are not aware of by accurately going through datasets, historical data and other variables.
With effective Machine Learning algorithms, ML can promote discovering realistic or accurate solutions to problems even when working with bad quality data or with changing values.
What is Machine Learning?
Machine Learning can be defined as the ability of machines to learn from data or respond (take action) to situations on their own. ML is one of the core components of AI and makes up a huge portion of the total research or work being done with AI.
ML automates various functions of the machines or services which lead to the automated functioning of the ML-powered systems. ML-powered systems operate on their own and also improve their effectiveness by learning with time. ML learning is also heavily used for business analytics, research, statistical problems and for various other fields which involve extracting more data from a given set of information.
Read about Top 10 Machine Learning Applications in 2021.
What are Machine Learning Algorithms?
Machine Learning algorithms are behind making machines and systems smarter on their own and fundamentally acquiring the ability to learn. Machine learning algorithms are programmed mathematics and logic that adjusts itself to learn from data and give better performance.
ML algorithms shape the framework and layout the foundations behind how the machine or system will function. Machine Learning algorithms power automated systems and many AI integrations. Algorithms expressed in mathematical forms will have an equivalent in most programming languages.
Similarly, Machine Learning algorithms can be used across multiple set of tools using a lot of programming languages. However, some programming languages are better than the others at ML and handling ML tasks.
There are two main types of Machine Learning algorithms. Namely,
- Supervised Learning Algorithms
These algorithms help with practical ML problems and use both the variables, input variables, and output variables. These algorithms help systems learn to determine similar output as required from the dataset provided by comparing the results with an already given output and then effectively predict the output better each time through learning how to map the function better by taking reference from the given output. The machine is successful once it produces the same result on its own.
- Unsupervised Learning Algorithms
These algorithms only use input variables as output variables are not given. These algorithms help the machines learn from the dataset on their own through modelling the data structure and are not supervised by humans. These machines are tasked with discovering information from the given datasets and are also programmed to solve association and clustering problems.
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Why is Python Highly Used for Machine Learning?
Python is a high-level objective-oriented programming language that is quite to understand and use, hence adding to its popularity. It is highly preferred by data scientists and developers due to it improving code reusability and program modularity.
It is also highly versatile in nature and boasts of huge support for many functions, libraries and external toolkits. Python is one of the best programming languages to build smart systems or machines due to having various advantages which promote ML. Here are a few reasons why Python is highly suitable for machine learning.
- Writing code is easier in Python as compared to other languages such as C++ or Java.
- Python is easy to be integrated with programming languages such as C++ and others. It has huge support for integration with other languages and platforms.
- Python is independent and portable, hence allowing the execution of the same programs and codes across Windows, macOS, or other operating systems.
- Python is object-oriented and supports various functions and elements such as classes, objects, encapsulation, polymorphism, and inheritance.
- Python is one of the few Dynamic programming languages around. Dynamic languages allow the data type to be declared alongside the variables during runtime and do not require the users to declare it during writing the code.
- Python is very versatile and works well with various Machine Learning algorithms.
12 Most Used Machine Learning Algorithms in Python
There are many Machine Learning algorithms that are used to provide machines with the ability to learn or determine the required information or insights from datasets. Here are some Machine Learning algorithms which are extensively used to power ML by using Python.
- Naive Bayes: This algorithm is based on the Bayes’ theorem and consists of a classification method which functions by assuming that the features inside a class are not affected by other features inside the same class. The algorithm assumes that the features have no relation with each other even if they are dependent on the other. This algorithm offers a model which works really well with massive datasets.
- Decision Tree: It is one of the most popular supervised Machine Learning algorithms out there, being used for both classification and regression problems. Decision tree functions by navigating the complete tree and comparing the features with the help of conditional statements. The decision tree runs on both categorical dependent variables and continuous dependent variables.
- Random Forest: It fundamentally represents an ensemble learning method for classification, regression and other problems that function by building a compilation of many decision trees during training time. Random Forest classifies objects based on attributes, and each decision tree is given a class. This algorithm then chooses the class which reports the highest number of trees.
- Apriori Machine Learning Algorithm: This algorithm is a categorisation algorithm and is used when one needs to sort massive amounts of data. This algorithm can be given a dataset to generate association rules and then used to track developments of the item sets to build categories. It is an unsupervised ML algorithm that can be used to predict occurrences and for the auto-completion of processes.
- Logistic Regression: It can be used to predict independent values such as In or Out, 0 or 1 and Negative or Positive. It is one of the most popular supervised ML algorithms, which uses a logistic function to determine the output as either 1 or 0. This is a classification algorithm that follows a statistical model to determine the probabilities of classes or occurrences.
- Linear Regression: It helps in predicting an outcome and while observing independent features. This ML algorithm helps establish a linear relationship between independent variables and a dependent variable. This fundamentally means that it shows how the dependent variable is being affected by the value of the independent variables.
- K-nearest Neighbors or KNN: It can analyse the labels of given data points surrounding target data points and make predictions to classify the data points. KNN is used for both regression and classification tasks. It is a supervised learning algorithm that is used to recognise patterns, mine data and detect abnormalities.
- Support Vector Machines or SVM: It differentiates various categories in the dataset and then effectively optimising this differential line through vector calculation. This is a supervised ML algorithm that helps in analysing data for regression and classification problems.
- Discreet Hopfield Network: It helps create an artificial neural network that stores information and can recall this information from partial input. This algorithm offers machines a recurring behaviour that can be termed as auto-associative.
- Back-propagation: This algorithm is able to design given functions by altering the weights of the input signals, thus producing the required output signals. This is a supervised learning algorithm that is used for classification and regression. Back-propagation finds the minimum values of error functions through gradient descent or the delta rule technique. This is how the algorithm discovers the required weights that will minimise or neutralise error functions.
- XGBoost: It is a gradient boosting algorithm that is used for various functions such as regression, ranking and classification. This contains the tree learning algorithm and the linear model both, which allows XGBoost to predict events with high accuracy. This algorithm is comparably about 10 times faster than prior gradient boosting techniques.
- Hierarchical Clustering: This algorithm follows a method of cluster analysis. It functions by putting similar objects into groups called clusters after developing a cluster tree to represent the data. These groups, also known as nodes, are interconnected with two or more successor groups, and each node is allotted data that is similar in nature.
Frequently Asked Questions
The five most preferred Machine Learning algorithms are Logistic Regression, Linear Regression, Naive Bayes, Decision Tree and Support Vector Machines. These five are the most popular ML algorithms globally and used extensively by developers and data scientists across the world to power many ML projects.
The five best algorithms in data science are also the most popular ones among the other algorithms. These include Logistic Regression, Linear Regression, Random Forest, Decision Tree and KNN.
Fundamentally, there are four types of machine learning algorithms. There are many sub-categories; however, these four are the main categories that ML algorithms belong to. They are namely, supervised learning algorithms, unsupervised learning algorithms, semi-supervised learning algorithms and reinforcement learning algorithms.
This can only be determined by analysing the nature of the project. This depends on various factors such as the variables and values one will be working with, the values or information that needs to be found out or discovered and especially the complexity of the learning requirement.
The kind of algorithm one needs also heavily depends on the dataset or training data one has and the quality of it, which leads to a requirement of noise removal, further processing, pre-processing or mining of data.
Python is very versatile and adaptive, with massive support for expansion, boasting of extensive libraries and tools. Python is fast and easy to use as well, making it one of the best object-oriented programming languages of all. Python is best suited for machine learning due to its massive number of features, versatile support of functions and various other benefits while being dynamic in nature.
Linear Regression can be declared as being the best ML algorithm in Python as it is extensively used for ML and preferred by many ML developers. This supervised ML algorithm is popular and acclaimed for predicting outcomes while observing features. It is capable of running on single variables or multiple variables. It is also relatively less complex and easier to apply; hence, it is preferred by a lot of beginners as well.
Even though Python is the most suited for machine learning, a lot of developers prefer to use languages such as R, Java and Julia to power many machine learning projects. R is especially popular being highly statistically oriented being able to solve statistical problems very effectively.
ML has an incredible future ahead with technology and AI getting better every day. ML has a very promising future with incredible innovations being achieved in several industries using ML as a foundation for solving challenges, tackling problems and powering automation.
One needs to be proficient in programming in order to be involved in machine learning, being able to code and execute algorithms through languages such as Python. Interested individuals should also have strong foundations in mathematics, advanced algebra and statistical concepts.
Key Takeaways
There are many Machine Learning algorithms that help developers and data scientists build machines and systems that are allowing organisations and companies to accomplish various IT, business and statistical goals. When choosing the right ML algorithm, one must consider the environment that he or she is working in and the immediate as well as the future requirement of the project.
ML projects can demand immediate results or solutions to problems, and many algorithms can take various approaches to the same problem. It also does not depend on languages as most of these will work across the common programming languages.
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Algorithms are fundamentally the same across all languages as they all originate from mathematics, so when choosing ML algorithms that can serve the same purpose, it completely depends on the user preference and language of choice.
However, when it is not about solving problems and machines need to be programmed into learning through data, user behaviour and real-time use, fundamentally functioning on its own, specific algorithms are best suited for these purposes.
