Machine Learning: Difference between revisions

Revision as of 09:07, 6 July 2025

Introduction

Machine Learning is a subfield of artificial intelligence that focuses on the development of algorithms and statistical models that enable computers to perform specific tasks without explicit programming instructions. It encompasses a variety of techniques that facilitate the learning of patterns from data, which can be used to make predictions, identify trends, or inform decision-making processes. The essence of machine learning lies in its ability to enable systems to learn from data rather than relying on rigid pre-defined rules.

Machine learning involves several key concepts, including supervised learning, unsupervised learning, and reinforcement learning. These paradigms help categorize the different types of learning tasks and determine the optimal approach to extracting knowledge from data.

Background or History

The history of machine learning can be traced back to the mid-20th century when the foundations of artificial intelligence were first laid. In 1950, Alan Turing proposed the concept of a machine that could simulate any human intelligence aspect, which led to considerable exploration in the field. The initial decades primarily consisted of rule-based systems where performance was based on predefined rules and logical reasoning.

By the 1980s, the field began to shift focus towards learning algorithms. This change was driven predominantly by the advent of more powerful computer hardware and a surge in available data. Early machine learning models, such as decision trees and neural networks, began to emerge during this period. The term "machine learning" itself was coined in 1959 by Arthur Samuel, who developed a program capable of playing checkers. Samuel’s work demonstrated that machines could improve their performance through experience.

The landmark moment for machine learning came with significant strides in neural network research, especially with the introduction of backpropagation in the 1980s. However, it wasn't until the 2000s and the arrival of large datasets, improved hardware, and advanced algorithms that machine learning experienced exponential growth. The rise of deep learning, a subset of machine learning involving neural networks with numerous layers, has been particularly transformative in applications such as image and speech recognition.

Architecture or Design

The architecture of machine learning systems can vary significantly depending on the specific application and type of learning employed. Generally, a machine learning model consists of several key components, which are as follows:

Data Input

Machine learning begins with data, which serves as the fundamental resource for learning. Data can come from various sources, including databases, sensors, social media, and many others. The quality and quantity of data directly influence the performance of the machine learning model. Data preprocessing steps are crucial in cleaning, normalizing, and transforming raw data into a suitable format for analysis.

Model Selection

Choosing the appropriate model for the specific task is vital. Common models include linear regression, decision trees, support vector machines, and neural networks. Each model has unique characteristics that make it more suitable for certain types of data and problems. For instance, linear regression is simple and interpretable, while neural networks can capture complex patterns but require more data and tuning.

Learning Process

The learning process involves training the selected model on the input data. This is where the model adjusts its parameters based on the input-output mapping defined in the data. In supervised learning, the model learns from labeled data, whereas, in unsupervised learning, it identifies patterns in unlabeled input. In reinforcement learning, an agent learns to take actions in an environment to maximize cumulative rewards over time.

Evaluation Metrics

After training, the model must be evaluated against a separate validation dataset to assess its performance. Standard evaluation metrics include accuracy, precision, recall, F1 score, and area under the receiver operating characteristic (ROC) curve. These metrics help determine how well the model generalizes to unseen data and its practical applicability.

Deployment

Once a model is trained and validated, it can be deployed to make predictions or analyze new data. Deployment involves integrating the model into an application or service where it can interact with users or other systems. Continuous monitoring of the model's performance in real-world scenarios is necessary to ensure its effectiveness over time.

Implementation or Applications

Machine learning has found applications across various domains, leveraging its ability to analyze and model complex systems. Notable implementations include the following sectors.

Healthcare

In healthcare, machine learning assists in diagnosing diseases, predicting patient outcomes, and personalizing treatment plans. Algorithms such as support vector machines and neural networks are often employed to analyze medical imaging data, genetic information, and electronic health records. For instance, machine learning models have shown promise in identifying cancers in imaging studies, predicting patient readmission rates, and crafting individualized medication regimens based on patient characteristics.

Finance

The finance industry utilizes machine learning for risk assessment, fraud detection, algorithmic trading, and customer service automation. Machine learning models can analyze transaction patterns to flag potential fraudulent activities or evaluate credit risk by assessing various financial metrics and consumer behavior. Additionally, algorithmic trading systems leverage advanced models to analyze market trends and automate investment decisions.

Retail

In the retail sector, companies apply machine learning to optimize inventory management, enhance recommendation systems, and improve customer experience. Machine learning algorithms analyze customer purchase data to provide personalized product recommendations. Additionally, predictive analytics enables retailers to forecast sales and manage supply chain logistics more effectively.

Transportation

Machine learning plays a significant role in transportation through applications like self-driving technology, route optimization, and demand forecasting. Autonomous vehicles utilize machine learning algorithms to process data from sensors and cameras, enabling safe navigation and decision-making in real-time. Furthermore, ride-sharing companies apply machine learning to efficiently match drivers with riders and predict future demand.

Natural Language Processing

Natural language processing (NLP), a subfield of machine learning, focuses on enabling machines to understand and interpret human language. Applications include language translation, sentiment analysis, and chatbots. Machine learning algorithms process large corpuses of text data to derive meaning, accomplish tasks such as translating languages in real-time, or automatically generating textual responses in conversational agents.

Real-world Examples

Machine learning has been integrated into numerous real-world products and services that define modern technology. Examples include:

Google Search

Google employs machine learning algorithms to continuously enhance the accuracy and relevance of its search results. These algorithms analyze user behavior, search trends, and webpage content to provide the best possible results for user queries. The personalized nature of search results heavily relies on user data that machine learning systems analyze to predict preferences.

Netflix Recommendations

Netflix uses machine learning to offer personalized viewing recommendations to its subscribers. By analyzing viewing habits, user ratings, and contextual factors, Netflix’s algorithms recommend content tailored to individual tastes, significantly improving user satisfaction and engagement.

Amazon Alexa

Amazon's virtual assistant, Alexa, harnesses the power of machine learning to understand and respond to voice commands. Machine learning enables Alexa to continually improve its speech recognition capabilities through user interactions, allowing it to better understand commands and provide relevant answers or perform tasks.

Facebook News Feed

Facebook employs machine learning to curate content displayed on its users' news feeds. Algorithms analyze user interactions, friend connections, and shared content, ensuring users are presented with posts that align with their interests. This personalized approach enhances user engagement and retention.

Autonomous Vehicles

The development of self-driving cars represents one of the most exciting applications of machine learning. Companies like Tesla, Waymo, and others are deploying machine learning techniques to enable vehicles to navigate autonomously, detect their environment, and make decisions similar to or better than human drivers.

Criticism or Limitations

Despite the many advancements and benefits of machine learning, the field is not without its criticisms and limitations. Chief among these concerns is the issue of transparency. Many machine learning models, particularly deep learning networks, operate as "black boxes," making it difficult to understand how decisions are made. This lack of interpretability poses challenges in critical applications such as healthcare and criminal justice, where stakeholders may demand insight into the decision-making process.

Another limitation revolves around data quality and bias. Machine learning models are highly dependent on the data used to train them. If biased or unrepresentative data is utilized, the resulting models may perpetuate or even exacerbate existing inequalities. This concern has garnered significant attention, particularly regarding facial recognition and predictive policing systems, which can disproportionately misclassify or misconstrue minority groups.

Additionally, the increasing complexity and capabilities of machine learning bring ethical considerations to the forefront. Issues such as algorithmic accountability, privacy, and surveillance raise important questions regarding the responsible use of machine learning technologies. As decisions become increasingly automated, the implications of those decisions require careful examination to avoid potential harm to individuals and society at large.

Finally, resource-intensive training processes represent another challenge, particularly with deep learning models that demand large datasets and significant computational power. The environmental impact of these algorithms, particularly as energy consumption grows, raises valid concerns about the sustainability of heavy computational needs in machine learning development.

References

@@ Line 1: / Line 1: @@
-== Machine Learning ==
+== Introduction ==
+'''Machine Learning''' is a subfield of artificial intelligence that focuses on the development of algorithms and statistical models that enable computers to perform specific tasks without explicit programming instructions. It encompasses a variety of techniques that facilitate the learning of patterns from data, which can be used to make predictions, identify trends, or inform decision-making processes. The essence of machine learning lies in its ability to enable systems to learn from data rather than relying on rigid pre-defined rules.
-Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computer systems to perform specific tasks without explicit instructions. Instead of being programmed to conduct a task, systems learn from data patterns and make decisions based on those learned patterns. This capability has led to significant advancements in various fields, revolutionizing industries and improving decision-making processes.
+Machine learning involves several key concepts, including supervised learning, unsupervised learning, and reinforcement learning. These paradigms help categorize the different types of learning tasks and determine the optimal approach to extracting knowledge from data.
-== History ==
+== Background or History ==
+The history of machine learning can be traced back to the mid-20th century when the foundations of artificial intelligence were first laid. In 1950, Alan Turing proposed the concept of a machine that could simulate any human intelligence aspect, which led to considerable exploration in the field. The initial decades primarily consisted of rule-based systems where performance was based on predefined rules and logical reasoning.
-Machine learning has roots dating back to the mid-20th century, with early efforts emerging from insights in statistics and the field of cognitive science. The term "machine learning" was first coined by Arthur Samuel in 1959 when he developed a program capable of playing checkers that improved its performance through experience.
+By the 1980s, the field began to shift focus towards learning algorithms. This change was driven predominantly by the advent of more powerful computer hardware and a surge in available data. Early machine learning models, such as decision trees and neural networks, began to emerge during this period. The term "machine learning" itself was coined in 1959 by Arthur Samuel, who developed a program capable of playing checkers. Samuel’s work demonstrated that machines could improve their performance through experience.
-=== Early Development ===
+The landmark moment for machine learning came with significant strides in neural network research, especially with the introduction of backpropagation in the 1980s. However, it wasn't until the 2000s and the arrival of large datasets, improved hardware, and advanced algorithms that machine learning experienced exponential growth. The rise of deep learning, a subset of machine learning involving neural networks with numerous layers, has been particularly transformative in applications such as image and speech recognition.
-In the 1960s and 1970s, foundational work in neural networks began with the introduction of models like the Perceptron, designed by Frank Rosenblatt. These initial attempts, however, faced challenges in scalability and applicability. The research in this area waned in the 1980s, a period often referred to as the "AI winter," when interest and funding in AI-related research decreased due to unmet expectations and limited computational power.
+== Architecture or Design ==
+The architecture of machine learning systems can vary significantly depending on the specific application and type of learning employed. Generally, a machine learning model consists of several key components, which are as follows:
-=== Revival and Growth ===
+=== Data Input ===
+Machine learning begins with data, which serves as the fundamental resource for learning. Data can come from various sources, including databases, sensors, social media, and many others. The quality and quantity of data directly influence the performance of the machine learning model. Data preprocessing steps are crucial in cleaning, normalizing, and transforming raw data into a suitable format for analysis.
-The late 1990s and early 2000s saw a resurgence of interest in machine learning, driven largely by advances in computing technology, the availability of vast amounts of data, and innovative algorithms. Techniques such as support vector machines (SVM), decision trees, and ensemble methods gained prominence. Additionally, the advent of the internet and big data provided the necessary fuel for the development of more robust machine learning models.
+=== Model Selection ===
+Choosing the appropriate model for the specific task is vital. Common models include linear regression, decision trees, support vector machines, and neural networks. Each model has unique characteristics that make it more suitable for certain types of data and problems. For instance, linear regression is simple and interpretable, while neural networks can capture complex patterns but require more data and tuning.
-=== Deep Learning Era ===
+=== Learning Process ===
+The learning process involves training the selected model on the input data. This is where the model adjusts its parameters based on the input-output mapping defined in the data. In supervised learning, the model learns from labeled data, whereas, in unsupervised learning, it identifies patterns in unlabeled input. In reinforcement learning, an agent learns to take actions in an environment to maximize cumulative rewards over time.
-The 2010s marked a revolutionary period for machine learning with the rise of deep learning, a subfield that leverages neural networks with many layers (deep neural networks). Pioneers in this domain, such as Geoffrey Hinton, Yann LeCun, and Yoshua Bengio, demonstrated the effectiveness of deep learning in image and speech recognition tasks. Breakthroughs like AlexNet, which won the ImageNet competition in 2012, showcased the immense potential of these techniques, leading to widespread adoption across various industries.
+=== Evaluation Metrics ===
+After training, the model must be evaluated against a separate validation dataset to assess its performance. Standard evaluation metrics include accuracy, precision, recall, F1 score, and area under the receiver operating characteristic (ROC) curve. These metrics help determine how well the model generalizes to unseen data and its practical applicability.
-== Design and Architecture ==
+=== Deployment ===
+Once a model is trained and validated, it can be deployed to make predictions or analyze new data. Deployment involves integrating the model into an application or service where it can interact with users or other systems. Continuous monitoring of the model's performance in real-world scenarios is necessary to ensure its effectiveness over time.
-Machine learning systems can be organized into various architectures and frameworks based on their functionality and approach to learning.
+== Implementation or Applications ==
+Machine learning has found applications across various domains, leveraging its ability to analyze and model complex systems. Notable implementations include the following sectors.
-=== Types of Learning ===
+=== Healthcare ===
+In healthcare, machine learning assists in diagnosing diseases, predicting patient outcomes, and personalizing treatment plans. Algorithms such as support vector machines and neural networks are often employed to analyze medical imaging data, genetic information, and electronic health records. For instance, machine learning models have shown promise in identifying cancers in imaging studies, predicting patient readmission rates, and crafting individualized medication regimens based on patient characteristics.
-Machine learning can be broadly classified into three categories:
-'''Supervised Learning:''' In this framework, models are trained on labeled datasets, where the input data is paired with the correct output. The goal is to learn a mapping from inputs to outputs that can be generalized to new, unseen data.
-'''Unsupervised Learning:''' Here, models work with unlabeled data, exploring the data's inherent structure to identify patterns. Common tasks include clustering, where similar data points are grouped together, and dimensionality reduction, which simplifies data while retaining essential characteristics.
-'''Reinforcement Learning:''' This type of learning involves agents that interact with an environment, learning to make decisions through trial and error. Agents receive rewards or penalties based on their actions, allowing them to improve their performance over time.
-=== Algorithms and Models ===
-Numerous algorithms and models have been developed for machine learning applications. Some widely used algorithms include:
-'''Linear Regression:''' A statistical method for predicting a continuous output based on one or more input features, assuming a linear relationship.
-'''Decision Trees:''' A tree-like model used for classification and regression, where internal nodes represent features, branches represent decision rules, and leaf nodes represent outcomes.
-'''Support Vector Machines:''' A classification algorithm that constructs hyperplanes in high-dimensional spaces to separate different classes effectively.
-'''Neural Networks:''' Complex architectures consisting of interconnected nodes (neurons) organized in layers. They can model intricate relationships in data and are particularly effective for tasks like image and speech recognition.
-'''Ensemble Methods:''' These techniques combine multiple models to improve overall performance, including methods like bagging, boosting, and stacking.
-=== Frameworks and Tools ===
-Machine learning frameworks facilitate the development, training, and deployment of models. Prominent frameworks include:
-* '''TensorFlow:''' An open-source framework developed by Google for deep learning applications.
-* '''PyTorch:''' A flexible and dynamic deep learning framework favored for research and production.
-* '''Scikit-learn:''' A widely-used library that provides simple and efficient tools for data mining and data analysis.
-* '''Keras:''' A high-level neural networks API that runs on top of TensorFlow, simplifying the development of deep learning models.
-== Usage and Implementation ==
-Machine learning's applications span diverse fields and industries, showcasing its versatility and capability to drive innovation.
 === Finance ===
+The finance industry utilizes machine learning for risk assessment, fraud detection, algorithmic trading, and customer service automation. Machine learning models can analyze transaction patterns to flag potential fraudulent activities or evaluate credit risk by assessing various financial metrics and consumer behavior. Additionally, algorithmic trading systems leverage advanced models to analyze market trends and automate investment decisions.
-In the financial industry, machine learning algorithms analyze market trends, assess credit risk, and detect fraudulent activities. For example, credit scoring models use historical data to evaluate an individual's creditworthiness, while algorithmic trading systems leverage ML to automate stock trading based on market parameters.
+=== Retail ===
+In the retail sector, companies apply machine learning to optimize inventory management, enhance recommendation systems, and improve customer experience. Machine learning algorithms analyze customer purchase data to provide personalized product recommendations. Additionally, predictive analytics enables retailers to forecast sales and manage supply chain logistics more effectively.
-=== Healthcare ===
+=== Transportation ===
+Machine learning plays a significant role in transportation through applications like self-driving technology, route optimization, and demand forecasting. Autonomous vehicles utilize machine learning algorithms to process data from sensors and cameras, enabling safe navigation and decision-making in real-time. Furthermore, ride-sharing companies apply machine learning to efficiently match drivers with riders and predict future demand.
-Machine learning is transforming healthcare by enabling predictive analytics, personalized medicine, and medical imaging analysis. ML models can predict patient outcomes, assist in diagnosing diseases from imaging data, and support drug discovery by identifying potential compounds efficiently.
 === Natural Language Processing ===
+Natural language processing (NLP), a subfield of machine learning, focuses on enabling machines to understand and interpret human language. Applications include language translation, sentiment analysis, and chatbots. Machine learning algorithms process large corpuses of text data to derive meaning, accomplish tasks such as translating languages in real-time, or automatically generating textual responses in conversational agents.
-Natural Language Processing (NLP) is a critical area of machine learning that focuses on enabling computers to understand, interpret, and generate human language. Applications include sentiment analysis, chatbots, and translation services. Techniques such as recurrent neural networks (RNNs) and transformers have revolutionized the capacity of machines to handle language-related tasks effectively.
-=== Autonomous Vehicles ===
-Autonomous vehicles depend significantly on machine learning algorithms for perception, decision-making, and navigation. Real-time data from sensors is processed by ML models to interpret surroundings and make informed driving decisions, enhancing safety and efficiency.
-=== Marketing and Customer Relationship Management ===
-In marketing, machine learning helps businesses optimize campaigns, personalize customer interactions, and predict customer behaviors. Machine learning algorithms analyze consumer data to identify trends, segment audiences, and determine the best strategies for customer engagement.
 == Real-world Examples ==
+Machine learning has been integrated into numerous real-world products and services that define modern technology. Examples include:
-Several notable applications of machine learning exemplify its impact across different sectors:
 === Google Search ===
+Google employs machine learning algorithms to continuously enhance the accuracy and relevance of its search results. These algorithms analyze user behavior, search trends, and webpage content to provide the best possible results for user queries. The personalized nature of search results heavily relies on user data that machine learning systems analyze to predict preferences.
-Google employs machine learning algorithms to enhance search results through personalization and relevance scoring. Techniques such as RankBrain utilize ML to interpret user queries better and deliver accurate search results by learning from user behavior.
 === Netflix Recommendations ===
+Netflix uses machine learning to offer personalized viewing recommendations to its subscribers. By analyzing viewing habits, user ratings, and contextual factors, Netflix’s algorithms recommend content tailored to individual tastes, significantly improving user satisfaction and engagement.
-Netflix utilizes machine learning to analyze viewing habits and preferences, providing personalized content recommendations. The recommendation system assesses user interactions with content, learning to predict preferences effectively.
+=== Amazon Alexa ===
+Amazon's virtual assistant, Alexa, harnesses the power of machine learning to understand and respond to voice commands. Machine learning enables Alexa to continually improve its speech recognition capabilities through user interactions, allowing it to better understand commands and provide relevant answers or perform tasks.
-=== Amazon's Product Recommendations ===
+=== Facebook News Feed ===
+Facebook employs machine learning to curate content displayed on its users' news feeds. Algorithms analyze user interactions, friend connections, and shared content, ensuring users are presented with posts that align with their interests. This personalized approach enhances user engagement and retention.
-Amazon leverages machine learning to power its recommendation engine, suggesting products based on user interactions, purchase history, and collaborative filtering methods. This approach enhances customer experience and drives sales.
+=== Autonomous Vehicles ===
+The development of self-driving cars represents one of the most exciting applications of machine learning. Companies like Tesla, Waymo, and others are deploying machine learning techniques to enable vehicles to navigate autonomously, detect their environment, and make decisions similar to or better than human drivers.
-=== Automated Customer Support ===
-Many companies employ AI chatbots powered by machine learning to provide customer support. These chatbots can understand and respond to customer inquiries, learn from interactions, and improve over time, offering efficient and effective service.
-=== Facial Recognition Technology ===
+== Criticism or Limitations ==
+Despite the many advancements and benefits of machine learning, the field is not without its criticisms and limitations. Chief among these concerns is the issue of transparency. Many machine learning models, particularly deep learning networks, operate as "black boxes," making it difficult to understand how decisions are made. This lack of interpretability poses challenges in critical applications such as healthcare and criminal justice, where stakeholders may demand insight into the decision-making process.
-Machine learning facilitates facial recognition technology used in various applications, from security systems to social media tagging. Algorithms analyze facial features to identify individuals accurately, leading to increased security measures and personalized experiences.
+Another limitation revolves around data quality and bias. Machine learning models are highly dependent on the data used to train them. If biased or unrepresentative data is utilized, the resulting models may perpetuate or even exacerbate existing inequalities. This concern has garnered significant attention, particularly regarding facial recognition and predictive policing systems, which can disproportionately misclassify or misconstrue minority groups.
-== Criticism and Controversies ==
+Additionally, the increasing complexity and capabilities of machine learning bring ethical considerations to the forefront. Issues such as algorithmic accountability, privacy, and surveillance raise important questions regarding the responsible use of machine learning technologies. As decisions become increasingly automated, the implications of those decisions require careful examination to avoid potential harm to individuals and society at large.
-Despite its numerous benefits, machine learning is not without criticism and controversy, encompassing ethical considerations, transparency, and biases inherent in algorithms.
+Finally, resource-intensive training processes represent another challenge, particularly with deep learning models that demand large datasets and significant computational power. The environmental impact of these algorithms, particularly as energy consumption grows, raises valid concerns about the sustainability of heavy computational needs in machine learning development.
-=== Ethical Concerns ===
+== See also ==
-The deployment of machine learning raises ethical concerns, particularly regarding privacy, consent, and the potential for surveillance. As machine learning models become integrated into various systems, there is growing apprehension about how data is collected, used, and stored.
-=== Algorithmic Bias ===
-Machine learning models can inadvertently perpetuate or exacerbate biases present in the training data. If historical data reflects systemic biases, the resulting predictions and decisions may also reflect these biases, leading to unfair outcomes. Ensuring fairness and mitigating bias in machine learning models is an ongoing challenge in the field.
-=== Transparency and Interpretability ===
-Machine learning models, especially deep learning networks, can act as "black boxes," where the decision-making process is not easily interpretable. This lack of transparency poses challenges in critical areas such as healthcare and finance, where understanding model predictions is essential for trust and accountability.
-=== Job Displacement ===
-The automation of tasks by machine learning systems raises concerns about potential job displacement across numerous industries. As machines become capable of performing tasks traditionally done by humans, there is an ongoing debate about the future of work and the necessity for reskilling and adaptation.
-== Influence and Impact ==
-The influence of machine learning extends beyond immediate applications; it reshapes entire industries and societal norms.
-=== Economic Transformation ===
-Machine learning has become a driving force behind economic transformation, optimizing processes and creating new avenues for innovation and efficiency. Businesses that effectively leverage machine learning gain a competitive advantage, fueling industry and economic growth.
-=== Scientific Research ===
-In scientific research, machine learning accelerates discovery by analyzing vast datasets and identifying patterns that would be difficult for human researchers to discern. This has implications across various disciplines, from climate science to genetics, leading to new insights and advancements.
-=== Education ===
-Machine learning is increasingly applied in education through personalized learning platforms that adapt to individual student needs and learning styles. These systems enhance engagement and promote better educational outcomes, reshaping the traditional educational landscape.
-=== Societal Impact ===
-As machine learning continues to advance, its societal impact becomes more pronounced. From enabling smarter cities to enhancing public safety and healthcare, machine learning technologies are reshaping everyday life and influencing societal structures.
-== See Also ==
 * [[Artificial Intelligence]]
 * [[Deep Learning]]
+* [[Big Data]]
 * [[Natural Language Processing]]
 * [[Data Mining]]
-* [[Big Data]]
 * [[Reinforcement Learning]]
-* [[Ethics of Artificial Intelligence]]
 == References ==
-* [https://www.ibm.com/cloud/learn/machine-learning Machine Learning - IBM]
+* [https://www.ibm.com/cloud/learn/machine-learning IBM: Machine Learning]
-* [https://aws.amazon.com/machine-learning/ Machine Learning on AWS - Amazon Web Services]
+* [https://azure.microsoft.com/en-us/overview/machine-learning/ Microsoft Azure: Machine Learning]
-* [https://www.microsoft.com/en-us/research/theme/machine-learning-research/ Machine Learning Research - Microsoft Research]
+* [https://www.tensorflow.org/ TensorFlow]
-* [https://www.tensorflow.org TensorFlow - An end-to-end open-source machine learning platform]
+* [https://www.kaggle.com/ Kaggle: Data Science & Machine Learning]
-* [https://pytorch.org PyTorch - The deep learning framework that puts Python first]
-* [https://scikit-learn.org/ Scikit-learn - Machine Learning in Python]
-[[Category:Machine Learning]]
 [[Category:Artificial Intelligence]]
 [[Category:Computer Science]]
+[[Category:Machine Learning]]