Machine Learning: Difference between revisions

Latest revision as of 09:43, 6 July 2025

Machine Learning is a subset of artificial intelligence (AI) that involves the development of algorithms and statistical models that enable computers to perform tasks without explicit instructions. Instead, these systems learn from data inputs and adapt through experience, thereby improving their performance in tasks over time. Machine Learning has a wide array of applications, ranging from natural language processing to computer vision, and has fundamentally transformed industries such as finance, healthcare, and transportation.

History

The roots of machine learning can be traced back to the inception of artificial intelligence in the mid-20th century. Early artificial intelligence research focused predominantly on symbolic approaches, where knowledge was explicitly programmed into systems. However, the limitations of these methods became evident, prompting researchers to explore alternative approaches.

The Beginnings

In the 1950s and 1960s, pioneering work by scientists such as Alan Turing and Marvin Minsky began to lay the groundwork for machine learning. The Turing Test, proposed by Turing, evaluated a machine’s ability to exhibit intelligent behavior indistinguishable from that of a human. During this period, Minsky and Seymour Papert published insights into neural networks, albeit with limited success due to computational constraints.

Growth and Decline

By the 1980s, interest in machine learning awakened with the re-discovery of backpropagation algorithms to train multi-layer neural networks, allowing for more complex functions to be learned. However, the rise of expert systems—AI that relied heavily on predefined rules—over machine learning techniques led to the "AI winter," a period characterized by reduced funding and interest in AI research from the late 1970s to the early 1990s.

The Resurgence

The early 21st century witnessed a resurgence in machine learning prompted by the advent of big data and advances in computational power. The rise of the internet provided vast quantities of data, enabling the training of more sophisticated models. In addition, increased interest in algorithms like Support Vector Machines (SVMs) and decision trees led to breakthroughs in supervised learning capabilities. This period is often referred to as the "deep learning revolution," where algorithms based on neural networks achieved remarkable successes in tasks such as speech recognition and image classification.

Key Concepts

Machine learning encompasses various techniques and methodologies. Understanding these concepts is essential for comprehending how machine learning systems operate.

Types of Machine Learning

Machine learning can be broadly categorized into three types: supervised learning, unsupervised learning, and reinforcement learning.

In supervised learning, the model is trained on a labeled dataset, which contains input-output pairs. The objective is for the model to learn to map inputs to the correct outputs so that it can make accurate predictions on unseen data. Common algorithms used include linear regression, logistic regression, decision trees, and neural networks.

Unsupervised learning, in contrast, works with unlabeled data. The goal is to identify patterns or structures within the dataset. Applications include clustering, where the system groups similar data points, and dimensionality reduction, where techniques like Principal Component Analysis (PCA) are used to simplify datasets by reducing the number of features.

Reinforcement learning (RL) is a distinct area that focuses on training agents to make sequential decisions by rewarding desirable outcomes and penalizing undesired ones. RL has gained traction for applications such as robotics, game playing, and autonomous driving.

Algorithms and Techniques

Machine learning leverages a range of algorithms, each suited to different types of tasks and data. Some of the most widely used algorithms include decision trees, neural networks, support vector machines, random forests, and k-nearest neighbors (KNN).

Decision trees create a model based on a series of questions that split the data into branches. This model is intuitive and easy to interpret, making it suitable for various applications. Neural networks, inspired by the human brain, consist of interconnected nodes (neurons) that process inputs through multiple layers. Their ability to model complex relationships has led to significant advancements in areas such as image and speech recognition.

Support vector machines are another powerful algorithm that performs classification by finding the optimal hyperplane that separates data points of different classes. Random forests build multiple decision trees and aggregate their results to improve prediction accuracy and reduce overfitting.

Evaluation Metrics

Evaluating machine learning models is critical to determine their effectiveness. Common metrics vary based on whether the task is classification or regression. For classification tasks, accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC) are commonly used. In regression tasks, evaluation metrics such as mean absolute error (MAE), mean squared error (MSE), and R-squared provide insights into model performance.

Implementation in Various Domains

Machine learning finds applications across numerous fields, enhancing processes and enabling new possibilities.

Healthcare

In healthcare, machine learning algorithms assist in predictive analytics, aiding doctors in diagnosing diseases based on patient data. For example, algorithms have shown success in identifying patterns in medical imaging, such as detecting tumors in radiographs or analyzing pathology reports. Moreover, natural language processing applications allow for the interpretation of unstructured data from medical records, streamlining patient care.

Finance

The finance sector utilizes machine learning for credit scoring, fraud detection, algorithmic trading, and risk management. By analyzing transaction patterns, institutions can identify anomalies that may indicate fraudulent activity. In addition, machine learning models are employed to forecast stock trends and optimize trading strategies based on patterns in historical data.

Transportation

In transportation, particularly in the development of autonomous vehicles, machine learning plays a crucial role in enabling vehicles to navigate real-world environments. Algorithms process data from sensors like cameras and LiDAR, helping vehicles understand their surroundings and make informed decisions. This technology is also used in optimizing traffic flow through smart traffic management systems.

Retail

Retail organizations employ machine learning to analyze consumer behavior and personalize marketing strategies. Predictive analytics assist in inventory management, ensuring popular products remain in stock while minimizing excess inventory. Machine learning algorithms enable recommendation systems that enhance the customer shopping experience by suggesting relevant products.

Real-world Examples

The impact of machine learning can be observed in various real-world applications, highlighting its transformative potential.

Natural Language Processing

Natural language processing (NLP) techniques have led to advancements in virtual assistants such as Siri and Alexa, allowing for conversational interfaces that understand and respond to human queries. Sentiment analysis in social media monitoring tools employs machine learning to gauge public opinion and brand perception.

Image and Video Analysis

Machine learning powers many image and video analysis applications, such as facial recognition technology used in security systems and social media platforms. Companies like Facebook and Google utilize machine learning algorithms to tag photos automatically and enhance user experience.

Fraud Detection

Financial institutions implement machine learning algorithms to analyze transaction data in real-time for signs of fraudulent behavior. These models improve over time, adapting to new fraud patterns and constantly enhancing security measures without requiring manual updates.

Autonomous Vehicles

Companies such as Tesla and Waymo rely on machine learning to drive innovations in autonomous vehicle technology. The ability of vehicles to process immense amounts of real-time data allows for dynamic decision-making, significantly enhancing safety and efficiency on roads.

Criticism and Limitations

Despite its advancements, machine learning is not without criticisms and limitations that merit consideration.

Data Bias

One of the primary concerns in machine learning is the potential for biased outcomes due to biased training data. If the data used to train machine learning models reflects societal biases, the algorithms may perpetuate or even exacerbate these biases when making decisions, particularly in fields such as hiring, law enforcement, and lending.

Interpretability

Many complex machine learning models, particularly deep learning networks, often function as "black boxes." This lack of transparency can pose challenges in understanding how decisions are made, leading to difficulties in accountability and trust. Stakeholders may be hesitant to adopt these technologies if they cannot ascertain the rationale behind specific outputs.

Overfitting

Overfitting occurs when a model learns to perform exceedingly well on training data but fails to generalize to new, unseen data. This issue can stem from models that are too complex relative to the amount of training data available. Techniques such as cross-validation and regularization are commonly employed to mitigate this risk.

Ethical Considerations

As machine learning systems become more integrated into everyday life, ethical considerations arise regarding privacy, consent, and surveillance. The potential for misuse of data and algorithmic decision-making necessitates ongoing discussions surrounding regulatory frameworks and ethical standards to guide machine learning deployment.

Future Directions

Looking forward, the future of machine learning is promising, with numerous advancements anticipated across various domains.

Explainable AI

Research into explainable AI (XAI) seeks to address issues of interpretability, striving to make machine learning models more understandable to humans. Developing techniques that clarify how models arrive at decisions will enhance trust and facilitate broader adoption in sensitive areas such as healthcare and law.

Integration of Multimodal Data

Future machine learning applications may increasingly involve integrating multimodal data—combining visual, textual, and auditory information to drive more holistic understanding and decision-making. Such advancements could lead to enhanced customer experiences in consumer-facing industries and more refined analytical capabilities in research.

Open-source Collaboration

The open-source movement within machine learning fosters collaboration and the democratization of technology. As advances in models and frameworks such as TensorFlow and PyTorch become widely accessible, it enables organizations across different sectors to harness machine learning capabilities, thereby accelerating innovation.

References

@@ Line 1: / Line 1: @@
-== Introduction ==
+'''Machine Learning''' is a subset of artificial intelligence (AI) that involves the development of algorithms and statistical models that enable computers to perform tasks without explicit instructions. Instead, these systems learn from data inputs and adapt through experience, thereby improving their performance in tasks over time. Machine Learning has a wide array of applications, ranging from natural language processing to computer vision, and has fundamentally transformed industries such as finance, healthcare, and transportation.
-'''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.
+== History ==
+The roots of machine learning can be traced back to the inception of artificial intelligence in the mid-20th century. Early artificial intelligence research focused predominantly on symbolic approaches, where knowledge was explicitly programmed into systems. However, the limitations of these methods became evident, prompting researchers to explore alternative approaches.
-== Background or History ==
+=== The Beginnings ===
-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.
+In the 1950s and 1960s, pioneering work by scientists such as Alan Turing and Marvin Minsky began to lay the groundwork for machine learning. The Turing Test, proposed by Turing, evaluated a machine’s ability to exhibit intelligent behavior indistinguishable from that of a human. During this period, Minsky and Seymour Papert published insights into neural networks, albeit with limited success due to computational constraints.
-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.
+=== Growth and Decline ===
+By the 1980s, interest in machine learning awakened with the re-discovery of backpropagation algorithms to train multi-layer neural networks, allowing for more complex functions to be learned. However, the rise of expert systems—AI that relied heavily on predefined rules—over machine learning techniques led to the "AI winter," a period characterized by reduced funding and interest in AI research from the late 1970s to the early 1990s.
-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.
+=== The Resurgence ===
+The early 21st century witnessed a resurgence in machine learning prompted by the advent of big data and advances in computational power. The rise of the internet provided vast quantities of data, enabling the training of more sophisticated models. In addition, increased interest in algorithms like Support Vector Machines (SVMs) and decision trees led to breakthroughs in supervised learning capabilities. This period is often referred to as the "deep learning revolution," where algorithms based on neural networks achieved remarkable successes in tasks such as speech recognition and image classification.
-== Architecture or Design ==
+== Key Concepts ==
-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:
+Machine learning encompasses various techniques and methodologies. Understanding these concepts is essential for comprehending how machine learning systems operate.
-=== Data Input ===
+=== Types of Machine Learning ===
-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.
+Machine learning can be broadly categorized into three types: supervised learning, unsupervised learning, and reinforcement learning.
-=== Model Selection ===
+In supervised learning, the model is trained on a labeled dataset, which contains input-output pairs. The objective is for the model to learn to map inputs to the correct outputs so that it can make accurate predictions on unseen data. Common algorithms used include linear regression, logistic regression, decision trees, and neural networks.
-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 ===
+Unsupervised learning, in contrast, works with unlabeled data. The goal is to identify patterns or structures within the dataset. Applications include clustering, where the system groups similar data points, and dimensionality reduction, where techniques like Principal Component Analysis (PCA) are used to simplify datasets by reducing the number of features.
-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.
+Reinforcement learning (RL) is a distinct area that focuses on training agents to make sequential decisions by rewarding desirable outcomes and penalizing undesired ones. RL has gained traction for applications such as robotics, game playing, and autonomous driving.
+=== Algorithms and Techniques ===
+Machine learning leverages a range of algorithms, each suited to different types of tasks and data. Some of the most widely used algorithms include decision trees, neural networks, support vector machines, random forests, and k-nearest neighbors (KNN).
+Decision trees create a model based on a series of questions that split the data into branches. This model is intuitive and easy to interpret, making it suitable for various applications. Neural networks, inspired by the human brain, consist of interconnected nodes (neurons) that process inputs through multiple layers. Their ability to model complex relationships has led to significant advancements in areas such as image and speech recognition.
+Support vector machines are another powerful algorithm that performs classification by finding the optimal hyperplane that separates data points of different classes. Random forests build multiple decision trees and aggregate their results to improve prediction accuracy and reduce overfitting.
 === 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.
+Evaluating machine learning models is critical to determine their effectiveness. Common metrics vary based on whether the task is classification or regression. For classification tasks, accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC) are commonly used. In regression tasks, evaluation metrics such as mean absolute error (MAE), mean squared error (MSE), and R-squared provide insights into model performance.
-=== Deployment ===
+== Implementation in Various Domains ==
-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 finds applications across numerous fields, enhancing processes and enabling new possibilities.
-== 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.
+In healthcare, machine learning algorithms assist in predictive analytics, aiding doctors in diagnosing diseases based on patient data. For example, algorithms have shown success in identifying patterns in medical imaging, such as detecting tumors in radiographs or analyzing pathology reports. Moreover, natural language processing applications allow for the interpretation of unstructured data from medical records, streamlining patient care.
 === 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.
+The finance sector utilizes machine learning for credit scoring, fraud detection, algorithmic trading, and risk management. By analyzing transaction patterns, institutions can identify anomalies that may indicate fraudulent activity. In addition, machine learning models are employed to forecast stock trends and optimize trading strategies based on patterns in historical data.
+=== Transportation ===
+In transportation, particularly in the development of autonomous vehicles, machine learning plays a crucial role in enabling vehicles to navigate real-world environments. Algorithms process data from sensors like cameras and LiDAR, helping vehicles understand their surroundings and make informed decisions. This technology is also used in optimizing traffic flow through smart traffic management systems.
 === 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.
+Retail organizations employ machine learning to analyze consumer behavior and personalize marketing strategies. Predictive analytics assist in inventory management, ensuring popular products remain in stock while minimizing excess inventory. Machine learning algorithms enable recommendation systems that enhance the customer shopping experience by suggesting relevant products.
-=== Transportation ===
+== Real-world Examples ==
-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.
+The impact of machine learning can be observed in various real-world applications, highlighting its transformative potential.
 === 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) techniques have led to advancements in virtual assistants such as [[Siri]] and [[Alexa]], allowing for conversational interfaces that understand and respond to human queries. Sentiment analysis in social media monitoring tools employs machine learning to gauge public opinion and brand perception.
+=== Image and Video Analysis ===
+Machine learning powers many image and video analysis applications, such as facial recognition technology used in security systems and social media platforms. Companies like [[Facebook]] and [[Google]] utilize machine learning algorithms to tag photos automatically and enhance user experience.
-== Real-world Examples ==
+=== Fraud Detection ===
-Machine learning has been integrated into numerous real-world products and services that define modern technology. Examples include:
+Financial institutions implement machine learning algorithms to analyze transaction data in real-time for signs of fraudulent behavior. These models improve over time, adapting to new fraud patterns and constantly enhancing security measures without requiring manual updates.
+=== Autonomous Vehicles ===
+Companies such as [[Tesla]] and [[Waymo]] rely on machine learning to drive innovations in autonomous vehicle technology. The ability of vehicles to process immense amounts of real-time data allows for dynamic decision-making, significantly enhancing safety and efficiency on roads.
-=== Google Search ===
+== Criticism and Limitations ==
-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.
+Despite its advancements, machine learning is not without criticisms and limitations that merit consideration.
-=== Netflix Recommendations ===
+=== Data Bias ===
-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.
+One of the primary concerns in machine learning is the potential for biased outcomes due to biased training data. If the data used to train machine learning models reflects societal biases, the algorithms may perpetuate or even exacerbate these biases when making decisions, particularly in fields such as hiring, law enforcement, and lending.
-=== Amazon Alexa ===
+=== Interpretability ===
-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.
+Many complex machine learning models, particularly deep learning networks, often function as "black boxes." This lack of transparency can pose challenges in understanding how decisions are made, leading to difficulties in accountability and trust. Stakeholders may be hesitant to adopt these technologies if they cannot ascertain the rationale behind specific outputs.
-=== Facebook News Feed ===
+=== Overfitting ===
-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.
+Overfitting occurs when a model learns to perform exceedingly well on training data but fails to generalize to new, unseen data. This issue can stem from models that are too complex relative to the amount of training data available. Techniques such as cross-validation and regularization are commonly employed to mitigate this risk.
-=== Autonomous Vehicles ===
+=== Ethical Considerations ===
-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.
+As machine learning systems become more integrated into everyday life, ethical considerations arise regarding privacy, consent, and surveillance. The potential for misuse of data and algorithmic decision-making necessitates ongoing discussions surrounding regulatory frameworks and ethical standards to guide machine learning deployment.
-== Criticism or Limitations ==
+== Future Directions ==
-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.
+Looking forward, the future of machine learning is promising, with numerous advancements anticipated across various domains.
-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.
+=== Explainable AI ===
+Research into explainable AI (XAI) seeks to address issues of interpretability, striving to make machine learning models more understandable to humans. Developing techniques that clarify how models arrive at decisions will enhance trust and facilitate broader adoption in sensitive areas such as healthcare and law.
-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.
+=== Integration of Multimodal Data ===
+Future machine learning applications may increasingly involve integrating multimodal data—combining visual, textual, and auditory information to drive more holistic understanding and decision-making. Such advancements could lead to enhanced customer experiences in consumer-facing industries and more refined analytical capabilities in research.
-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.
+=== Open-source Collaboration ===
+The open-source movement within machine learning fosters collaboration and the democratization of technology. As advances in models and frameworks such as [[TensorFlow]] and [[PyTorch]] become widely accessible, it enables organizations across different sectors to harness machine learning capabilities, thereby accelerating innovation.
 == See also ==
 * [[Artificial Intelligence]]
 * [[Deep Learning]]
-* [[Big Data]]
+* [[Data Mining]]
 * [[Natural Language Processing]]
-* [[Data Mining]]
 * [[Reinforcement Learning]]
+* [[Computer Vision]]
 == References ==
-* [https://www.ibm.com/cloud/learn/machine-learning IBM: Machine Learning]
+* [https://www.openai.com OpenAI Official Website]
-* [https://azure.microsoft.com/en-us/overview/machine-learning/ Microsoft Azure: Machine Learning]
+* [https://tensorflow.org TensorFlow Official Website]
-* [https://www.tensorflow.org/ TensorFlow]
+* [https://pytorch.org PyTorch Official Website]
-* [https://www.kaggle.com/ Kaggle: Data Science & Machine Learning]
+* [https://www.ibm.com/cloud/learn/machine-learning IBM Cloud: Machine Learning]
+* [https://www.microsoft.com/en-us/research/research-area/machine-learning/ Microsoft Research: Machine Learning]
+* [https://www.nvidia.com/en-us/deep-learning-ai/ Nvidia Deep Learning AI]
 [[Category:Artificial Intelligence]]
+[[Category:Machine Learning]]
 [[Category:Computer Science]]
-[[Category:Machine Learning]]