The document provides an overview of machine learning, including key concepts like supervised vs unsupervised learning, common algorithms like decision trees and neural networks, and how machine learning is used to build applications. It discusses how machine learning models are trained on large datasets to identify patterns and make predictions. Examples of machine learning in apps include predictive text, speech recognition, and personalized recommendations based on user behavior data. The document also outlines the steps involved in building a machine learning application.
How to build machine learning apps.pdfJamieDornan2
Machine learning is a sub-field of artificial intelligence (AI) that focuses on creating statistical models and algorithms that allow computers to learn and become more proficient at performing particular tasks. Machine learning algorithms create a mathematical model with the help of historical sample data, or “training data,” that assists in making predictions or judgments without being explicitly programmed.
Machine learning is a sub-field of artificial intelligence (AI) that focuses on creating statistical models and algorithms that allow computers to learn and become more proficient at performing particular tasks. Machine learning algorithms create a mathematical model with the help of historical sample data, or “training data,” that assists in making predictions or judgments without being explicitly programmed.
The document provides an overview of machine learning, including key concepts such as data, models, algorithms, and different machine learning methods. It discusses how machine learning uses large amounts of data to develop models that can make predictions without being explicitly programmed. The document also outlines several common machine learning algorithms like decision trees, k-nearest neighbors, support vector machines, neural networks, and reinforcement learning. Overall, the summary provides a high-level introduction to fundamental machine learning concepts and techniques.
This document provides an overview of machine learning presented by Mr. Raviraj Solanki. It discusses topics like introduction to machine learning, model preparation, modelling and evaluation. It defines key concepts like algorithms, models, predictor variables, response variables, training data and testing data. It also explains the differences between human learning and machine learning, types of machine learning including supervised learning and unsupervised learning. Supervised learning is further divided into classification and regression problems. Popular algorithms for supervised learning like random forest, decision trees, logistic regression, support vector machines, linear regression, regression trees and more are also mentioned.
Machine learning enables machines to learn from data and make predictions without being explicitly programmed. There are different types of machine learning problems like supervised learning (classification and regression), unsupervised learning (clustering), and reinforcement learning. Machine learning works by collecting data, preprocessing it, extracting features, selecting a model, training the model, evaluating it, and deploying it. Some common machine learning algorithms discussed are linear regression, logistic regression, and decision trees. Linear regression finds a linear relationship between variables to make predictions while logistic regression is used for classification problems.
The document discusses machine learning methods including supervised learning, unsupervised learning, and reinforcement learning. It provides examples of how each method is used, such as using historical data for prediction in supervised learning and organizing unlabeled data in unsupervised learning. Random forest, an ensemble supervised learning algorithm, is also summarized. It states random forest combines decision trees for improved performance and discusses its use in sectors like banking, medicine, land use, and marketing.
what-is-machine-learning-and-its-importance-in-todays-world.pdfTemok IT Services
Machine Learning is an AI method for teaching computers to learn from their mistakes. Machine learning algorithms can “learn” data directly from data without using an equation as a model by employing computational methods.
https://bit.ly/RightContactDataSpecialists
How to build machine learning apps.pdfJamieDornan2
Machine learning is a sub-field of artificial intelligence (AI) that focuses on creating statistical models and algorithms that allow computers to learn and become more proficient at performing particular tasks. Machine learning algorithms create a mathematical model with the help of historical sample data, or “training data,” that assists in making predictions or judgments without being explicitly programmed.
Machine learning is a sub-field of artificial intelligence (AI) that focuses on creating statistical models and algorithms that allow computers to learn and become more proficient at performing particular tasks. Machine learning algorithms create a mathematical model with the help of historical sample data, or “training data,” that assists in making predictions or judgments without being explicitly programmed.
The document provides an overview of machine learning, including key concepts such as data, models, algorithms, and different machine learning methods. It discusses how machine learning uses large amounts of data to develop models that can make predictions without being explicitly programmed. The document also outlines several common machine learning algorithms like decision trees, k-nearest neighbors, support vector machines, neural networks, and reinforcement learning. Overall, the summary provides a high-level introduction to fundamental machine learning concepts and techniques.
This document provides an overview of machine learning presented by Mr. Raviraj Solanki. It discusses topics like introduction to machine learning, model preparation, modelling and evaluation. It defines key concepts like algorithms, models, predictor variables, response variables, training data and testing data. It also explains the differences between human learning and machine learning, types of machine learning including supervised learning and unsupervised learning. Supervised learning is further divided into classification and regression problems. Popular algorithms for supervised learning like random forest, decision trees, logistic regression, support vector machines, linear regression, regression trees and more are also mentioned.
Machine learning enables machines to learn from data and make predictions without being explicitly programmed. There are different types of machine learning problems like supervised learning (classification and regression), unsupervised learning (clustering), and reinforcement learning. Machine learning works by collecting data, preprocessing it, extracting features, selecting a model, training the model, evaluating it, and deploying it. Some common machine learning algorithms discussed are linear regression, logistic regression, and decision trees. Linear regression finds a linear relationship between variables to make predictions while logistic regression is used for classification problems.
The document discusses machine learning methods including supervised learning, unsupervised learning, and reinforcement learning. It provides examples of how each method is used, such as using historical data for prediction in supervised learning and organizing unlabeled data in unsupervised learning. Random forest, an ensemble supervised learning algorithm, is also summarized. It states random forest combines decision trees for improved performance and discusses its use in sectors like banking, medicine, land use, and marketing.
what-is-machine-learning-and-its-importance-in-todays-world.pdfTemok IT Services
Machine Learning is an AI method for teaching computers to learn from their mistakes. Machine learning algorithms can “learn” data directly from data without using an equation as a model by employing computational methods.
https://bit.ly/RightContactDataSpecialists
This knolx is about an introduction to machine learning, wherein we see the basics of various different algorithms. This knolx isn't a complete intro to ML but can be a good starting point for anyone who wants to start in ML. In the end, we will take a look at the demo wherein we will analyze the FIFA dataset going through the understanding of various data analysis techniques and use an ML algorithm to derive 5 players that are similar to each other.
Machine learning applications nurturing growth of various business domainsShrutika Oswal
Machine learning is a science in which machines are becoming smarter and helping humans to make the best decisions based on previous data recommended practices. This technique is not new but is occupying fresh momentum. Machine Learning Algorithm learns from the previous records and analyses the data. Without any human interrupt, it will generate its own recommendation. A machine will add that recommendation as experience in its database and use it for further processing. In short, the machine learns from its own experience and gives you better and better output.
Machine learning is an iterative process as the more data added to machines learn from fresh feeds of data and then independently adapt new features to handle new data without constant human intervention. Machine learning was earlier used to predict what’s happing with the business but now the machine learning algorithm will suggest what action needs be taken by moving our business forward.
This PowerPoint presentation presents the results of a literature survey of machine learning applications nurturing the growth of various business domains. More specifically, it gives a brief introduction of Machine Learning, four major types of Machine Learning, enhancement in various business domains by the use of various machine learning algorithms.
MB2208A- Business Analytics- unit-4.pptxssuser28b150
This document provides an overview of predictive analytics, including:
- Predictive analytics uses historical data and machine learning techniques to predict future outcomes. It focuses on forecasting rather than just describing past events.
- Common predictive analytics applications include customer churn prediction, demand forecasting, risk assessment, and equipment maintenance scheduling.
- There are two main types of predictive models: logic-driven models based on known relationships between variables, and data-driven models using statistics and machine learning.
- The predictive analytics process involves collecting and cleaning data, selecting a modeling technique, building and validating the model, and deploying it to make predictions.
This document summarizes an internship report submitted by Shikhar Srivastava to Eckovation about a machine learning internship. The internship focused on machine learning applications, algorithms, and implementations. Srivastava's project involved teaching a neural network to recognize handwritten text using the MNIST dataset. He used a random forest classifier algorithm for the project, which creates decision trees from random subsets of training data and aggregates the votes to determine classifications.
Machine learning is a method of data analysis that allows computer systems to automatically learn and improve from experience without being explicitly programmed. It works by building models from data to make predictions or decisions without relying on rule-based programming. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. There are several types of machine learning algorithms including supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. Machine learning has many applications and is used across various industries like healthcare, retail, finance, government and transportation to extract insights from data.
Machine learning (ML) is a type of artificial intelligence that allows software to become more accurate at predicting outcomes without being explicitly programmed. ML uses historical data as input to predict new output values. Common uses of ML include recommendation engines, fraud detection, and predictive maintenance. There are four main types of ML: supervised learning where the input and output are defined, unsupervised learning which looks for patterns in unlabeled data, semi-supervised which uses some labeled and some unlabeled data, and reinforcement learning which programs an algorithm to seek rewards and avoid punishments to accomplish a goal.
This document provides an introduction to machine learning and data science. It discusses key concepts like supervised vs. unsupervised learning, classification algorithms, overfitting and underfitting data. It also addresses challenges like having bad quality or insufficient training data. Python and MATLAB are introduced as suitable software for machine learning projects.
1. The document discusses machine learning types including supervised learning, unsupervised learning, and reinforcement learning. It provides examples of applications like spam filtering, recommendations, and fraud detection.
2. Key challenges in machine learning are discussed such as poor quality data, lack of training data, and imperfections when data grows.
3. The difference between data science and machine learning is explained - data science is a broader field that includes extracting insights from data using tools and models, while machine learning focuses specifically on making predictions using algorithms.
Linear algebra provides the tools needed for machine learning algorithms by allowing complex operations to be described using matrices and vectors. It is widely used in machine learning because operations can be parallelized efficiently. Linear algebra also provides the foundation and notation used in other fields like calculus and probability that are important for machine learning. Machine learning involves feeding training data to algorithms that produce mathematical models to make predictions without being explicitly programmed. It works by learning from experience to improve performance at tasks over time. There are various applications of machine learning like image recognition, speech recognition, recommendations, and fraud detection.
This document provides an overview of machine learning concepts and techniques. It discusses supervised learning methods like classification and regression using algorithms such as naive Bayes, K-nearest neighbors, logistic regression, support vector machines, decision trees, and random forests. Unsupervised learning techniques like clustering and association are also covered. The document contrasts traditional programming with machine learning and describes typical machine learning processes like training, validation, testing, and parameter tuning. Common applications and examples of machine learning are also summarized.
Machine learning is a subfield of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" with data, without being explicitly programmed. The goal is to build models that predict outcomes accurately from large amounts of data. There are two primary machine learning methods: supervised learning, where the computer is provided labeled training data to learn from, and unsupervised learning, where the computer must find hidden patterns in unlabeled data. Common machine learning tasks include classification, prediction, clustering, and association.
IRJET- Machine Learning: Survey, Types and ChallengesIRJET Journal
This document provides an overview of machine learning, including its types and challenges. It discusses supervised and unsupervised machine learning algorithms. Supervised learning uses labeled training data to predict discrete or continuous output values. Unsupervised learning finds hidden patterns in unlabeled data through clustering. Common supervised algorithms are logistic regression, decision trees, k-nearest neighbors and common unsupervised algorithm is clustering. The document also gives examples to explain machine learning concepts and algorithms.
Supervised Machine Learning Techniques common algorithms and its applicationTara ram Goyal
The document provides an introduction to supervised machine learning, including definitions, techniques, and applications. It discusses how supervised machine learning involves training algorithms using labeled input data to make predictions on unlabeled data. Some common supervised learning algorithms mentioned are naive Bayes, decision trees, linear regression, support vector machines, and neural networks. Applications discussed include self-driving cars, online recommendations, fraud detection, and spam filtering. The key difference between supervised and unsupervised learning is that supervised learning uses labeled training data while unsupervised learning does not have pre-existing labels.
Unlocking the Potential of Artificial Intelligence_ Machine Learning in Pract...eswaralaldevadoss
Machine learning is a subset of artificial intelligence that involves training computers to learn from data and make predictions or decisions based on that data. It involves building algorithms and models that can learn patterns and relationships from data and use that knowledge to make predictions or take actions.
Here are some key concepts that can help beginners understand machine learning:
Data: Machine learning algorithms require data to learn from. This data can come from a variety of sources such as databases, spreadsheets, or sensors. The quality and quantity of data can greatly impact the accuracy and effectiveness of machine learning models.
Training: In machine learning, training involves feeding data into a model and adjusting its parameters until it can accurately predict outcomes. This process involves testing and tweaking the model to improve its accuracy.
Algorithms: There are many different algorithms used in machine learning, each with its own strengths and weaknesses. Common machine learning algorithms include decision trees, random forests, and neural networks.
Supervised vs. Unsupervised Learning: Supervised learning involves training a model on labeled data, where the desired outcome is already known. Unsupervised learning, on the other hand, involves training a model on unlabeled data and allowing it to identify patterns and relationships on its own.
Evaluation: After training a model, it's important to evaluate its accuracy and performance on new data. This involves testing the model on a separate set of data that it hasn't seen before.
Overfitting vs. Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, leading to poor performance on new data. Underfitting occurs when a model is too simple and fails to capture important patterns in the data.
Applications: Machine learning is used in a wide range of applications, from predicting stock prices to identifying fraudulent transactions. It's important to understand the specific needs and constraints of each application when building machine learning models.
Overall, machine learning is a powerful tool that can help businesses and organizations make more informed decisions based on data. By understanding the basic concepts and techniques of machine learning, beginners can begin to explore the potential applications and benefits of this exciting field.
This document provides an overview of machine learning, including definitions, applications, and common methods. Machine learning allows systems to learn from data and improve automatically without being explicitly programmed. It is a form of artificial intelligence. Common machine learning methods discussed are supervised learning, unsupervised learning, and reinforcement learning. The document also outlines applications of machine learning in various industries such as financial services, marketing, healthcare, transportation, and more.
Buddi health class imbalance based deep learningRam Swaminathan
BUDDI Health is a disruptive Deep Learning Platform which helps automate healthcare administrative functions such as Medical Coding, Claims Denial Prediction, Clinical Documentation Improvement and more.
The document discusses various aspects of machine learning including:
- Machine learning involves extracting knowledge from data to enable machines to learn without being explicitly programmed. It uses algorithms to model data and make predictions.
- The machine learning process includes data acquisition, processing, modeling, execution, and deployment. Algorithms are used to model the data and refine solutions.
- Machine learning has applications in healthcare, finance, retail, travel, and media by providing personalized recommendations, detecting fraud, optimizing prices and improving customer service.
- Data preprocessing is required to clean and transform raw data before training machine learning models. This includes data integration, cleaning, and transformation techniques.
1) Machine learning involves analyzing data to find patterns and make predictions. It uses mathematics, statistics, and programming.
2) Key aspects of machine learning include understanding the business problem, collecting and preparing data, building and evaluating models, and different types of machine learning algorithms like supervised, unsupervised, and reinforcement learning.
3) Common machine learning algorithms discussed include linear regression, logistic regression, KNN, K-means clustering, decision trees, and handling issues like missing values, outliers, and feature engineering.
The document provides an introduction to machine learning including its history, components, classifications, and applications. It discusses key events in the history of machine learning from 1950 to 1985. It defines machine learning and describes how it works through algorithms and data to make autonomous decisions without human intervention. The main components of machine learning include gathering raw data, converting data into information, gathering knowledge from information, and using that knowledge to make decisions. The document also describes the main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Finally, it outlines several applications of machine learning such as traffic prediction, speech and image recognition, medical diagnosis, spam detection, and more.
Shaping the future of ai for management consulting.pdfJamieDornan2
In recent years, the integration of artificial intelligence has redefined the landscape of AI management consulting, ushering in an era of unprecedented capabilities and efficiencies. For consultants, this iteration of artificial intelligence holds the power to dissect data intricacies, elevate decision-making processes, and facilitate efficient problem-solving, effectively condensing hours, if not days, of traditional work. On the client side, advanced data analysis augments the quality and depth of recommendations offered by consultants.
Blockchain in Healthcare - An Overview.pdfJamieDornan2
Blockchain technology is like a new spark in the financial industry. It isn't like the old financial systems because it's decentralized and can't be changed. This tech can change traditional methods and systems. Blockchain has several benefits. It makes things safer and more efficient. If we use blockchain technology in different parts of finance, there's the exciting chance to change the way the industry works.
This knolx is about an introduction to machine learning, wherein we see the basics of various different algorithms. This knolx isn't a complete intro to ML but can be a good starting point for anyone who wants to start in ML. In the end, we will take a look at the demo wherein we will analyze the FIFA dataset going through the understanding of various data analysis techniques and use an ML algorithm to derive 5 players that are similar to each other.
Machine learning applications nurturing growth of various business domainsShrutika Oswal
Machine learning is a science in which machines are becoming smarter and helping humans to make the best decisions based on previous data recommended practices. This technique is not new but is occupying fresh momentum. Machine Learning Algorithm learns from the previous records and analyses the data. Without any human interrupt, it will generate its own recommendation. A machine will add that recommendation as experience in its database and use it for further processing. In short, the machine learns from its own experience and gives you better and better output.
Machine learning is an iterative process as the more data added to machines learn from fresh feeds of data and then independently adapt new features to handle new data without constant human intervention. Machine learning was earlier used to predict what’s happing with the business but now the machine learning algorithm will suggest what action needs be taken by moving our business forward.
This PowerPoint presentation presents the results of a literature survey of machine learning applications nurturing the growth of various business domains. More specifically, it gives a brief introduction of Machine Learning, four major types of Machine Learning, enhancement in various business domains by the use of various machine learning algorithms.
MB2208A- Business Analytics- unit-4.pptxssuser28b150
This document provides an overview of predictive analytics, including:
- Predictive analytics uses historical data and machine learning techniques to predict future outcomes. It focuses on forecasting rather than just describing past events.
- Common predictive analytics applications include customer churn prediction, demand forecasting, risk assessment, and equipment maintenance scheduling.
- There are two main types of predictive models: logic-driven models based on known relationships between variables, and data-driven models using statistics and machine learning.
- The predictive analytics process involves collecting and cleaning data, selecting a modeling technique, building and validating the model, and deploying it to make predictions.
This document summarizes an internship report submitted by Shikhar Srivastava to Eckovation about a machine learning internship. The internship focused on machine learning applications, algorithms, and implementations. Srivastava's project involved teaching a neural network to recognize handwritten text using the MNIST dataset. He used a random forest classifier algorithm for the project, which creates decision trees from random subsets of training data and aggregates the votes to determine classifications.
Machine learning is a method of data analysis that allows computer systems to automatically learn and improve from experience without being explicitly programmed. It works by building models from data to make predictions or decisions without relying on rule-based programming. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. There are several types of machine learning algorithms including supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. Machine learning has many applications and is used across various industries like healthcare, retail, finance, government and transportation to extract insights from data.
Machine learning (ML) is a type of artificial intelligence that allows software to become more accurate at predicting outcomes without being explicitly programmed. ML uses historical data as input to predict new output values. Common uses of ML include recommendation engines, fraud detection, and predictive maintenance. There are four main types of ML: supervised learning where the input and output are defined, unsupervised learning which looks for patterns in unlabeled data, semi-supervised which uses some labeled and some unlabeled data, and reinforcement learning which programs an algorithm to seek rewards and avoid punishments to accomplish a goal.
This document provides an introduction to machine learning and data science. It discusses key concepts like supervised vs. unsupervised learning, classification algorithms, overfitting and underfitting data. It also addresses challenges like having bad quality or insufficient training data. Python and MATLAB are introduced as suitable software for machine learning projects.
1. The document discusses machine learning types including supervised learning, unsupervised learning, and reinforcement learning. It provides examples of applications like spam filtering, recommendations, and fraud detection.
2. Key challenges in machine learning are discussed such as poor quality data, lack of training data, and imperfections when data grows.
3. The difference between data science and machine learning is explained - data science is a broader field that includes extracting insights from data using tools and models, while machine learning focuses specifically on making predictions using algorithms.
Linear algebra provides the tools needed for machine learning algorithms by allowing complex operations to be described using matrices and vectors. It is widely used in machine learning because operations can be parallelized efficiently. Linear algebra also provides the foundation and notation used in other fields like calculus and probability that are important for machine learning. Machine learning involves feeding training data to algorithms that produce mathematical models to make predictions without being explicitly programmed. It works by learning from experience to improve performance at tasks over time. There are various applications of machine learning like image recognition, speech recognition, recommendations, and fraud detection.
This document provides an overview of machine learning concepts and techniques. It discusses supervised learning methods like classification and regression using algorithms such as naive Bayes, K-nearest neighbors, logistic regression, support vector machines, decision trees, and random forests. Unsupervised learning techniques like clustering and association are also covered. The document contrasts traditional programming with machine learning and describes typical machine learning processes like training, validation, testing, and parameter tuning. Common applications and examples of machine learning are also summarized.
Machine learning is a subfield of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" with data, without being explicitly programmed. The goal is to build models that predict outcomes accurately from large amounts of data. There are two primary machine learning methods: supervised learning, where the computer is provided labeled training data to learn from, and unsupervised learning, where the computer must find hidden patterns in unlabeled data. Common machine learning tasks include classification, prediction, clustering, and association.
IRJET- Machine Learning: Survey, Types and ChallengesIRJET Journal
This document provides an overview of machine learning, including its types and challenges. It discusses supervised and unsupervised machine learning algorithms. Supervised learning uses labeled training data to predict discrete or continuous output values. Unsupervised learning finds hidden patterns in unlabeled data through clustering. Common supervised algorithms are logistic regression, decision trees, k-nearest neighbors and common unsupervised algorithm is clustering. The document also gives examples to explain machine learning concepts and algorithms.
Supervised Machine Learning Techniques common algorithms and its applicationTara ram Goyal
The document provides an introduction to supervised machine learning, including definitions, techniques, and applications. It discusses how supervised machine learning involves training algorithms using labeled input data to make predictions on unlabeled data. Some common supervised learning algorithms mentioned are naive Bayes, decision trees, linear regression, support vector machines, and neural networks. Applications discussed include self-driving cars, online recommendations, fraud detection, and spam filtering. The key difference between supervised and unsupervised learning is that supervised learning uses labeled training data while unsupervised learning does not have pre-existing labels.
Unlocking the Potential of Artificial Intelligence_ Machine Learning in Pract...eswaralaldevadoss
Machine learning is a subset of artificial intelligence that involves training computers to learn from data and make predictions or decisions based on that data. It involves building algorithms and models that can learn patterns and relationships from data and use that knowledge to make predictions or take actions.
Here are some key concepts that can help beginners understand machine learning:
Data: Machine learning algorithms require data to learn from. This data can come from a variety of sources such as databases, spreadsheets, or sensors. The quality and quantity of data can greatly impact the accuracy and effectiveness of machine learning models.
Training: In machine learning, training involves feeding data into a model and adjusting its parameters until it can accurately predict outcomes. This process involves testing and tweaking the model to improve its accuracy.
Algorithms: There are many different algorithms used in machine learning, each with its own strengths and weaknesses. Common machine learning algorithms include decision trees, random forests, and neural networks.
Supervised vs. Unsupervised Learning: Supervised learning involves training a model on labeled data, where the desired outcome is already known. Unsupervised learning, on the other hand, involves training a model on unlabeled data and allowing it to identify patterns and relationships on its own.
Evaluation: After training a model, it's important to evaluate its accuracy and performance on new data. This involves testing the model on a separate set of data that it hasn't seen before.
Overfitting vs. Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, leading to poor performance on new data. Underfitting occurs when a model is too simple and fails to capture important patterns in the data.
Applications: Machine learning is used in a wide range of applications, from predicting stock prices to identifying fraudulent transactions. It's important to understand the specific needs and constraints of each application when building machine learning models.
Overall, machine learning is a powerful tool that can help businesses and organizations make more informed decisions based on data. By understanding the basic concepts and techniques of machine learning, beginners can begin to explore the potential applications and benefits of this exciting field.
This document provides an overview of machine learning, including definitions, applications, and common methods. Machine learning allows systems to learn from data and improve automatically without being explicitly programmed. It is a form of artificial intelligence. Common machine learning methods discussed are supervised learning, unsupervised learning, and reinforcement learning. The document also outlines applications of machine learning in various industries such as financial services, marketing, healthcare, transportation, and more.
Buddi health class imbalance based deep learningRam Swaminathan
BUDDI Health is a disruptive Deep Learning Platform which helps automate healthcare administrative functions such as Medical Coding, Claims Denial Prediction, Clinical Documentation Improvement and more.
The document discusses various aspects of machine learning including:
- Machine learning involves extracting knowledge from data to enable machines to learn without being explicitly programmed. It uses algorithms to model data and make predictions.
- The machine learning process includes data acquisition, processing, modeling, execution, and deployment. Algorithms are used to model the data and refine solutions.
- Machine learning has applications in healthcare, finance, retail, travel, and media by providing personalized recommendations, detecting fraud, optimizing prices and improving customer service.
- Data preprocessing is required to clean and transform raw data before training machine learning models. This includes data integration, cleaning, and transformation techniques.
1) Machine learning involves analyzing data to find patterns and make predictions. It uses mathematics, statistics, and programming.
2) Key aspects of machine learning include understanding the business problem, collecting and preparing data, building and evaluating models, and different types of machine learning algorithms like supervised, unsupervised, and reinforcement learning.
3) Common machine learning algorithms discussed include linear regression, logistic regression, KNN, K-means clustering, decision trees, and handling issues like missing values, outliers, and feature engineering.
The document provides an introduction to machine learning including its history, components, classifications, and applications. It discusses key events in the history of machine learning from 1950 to 1985. It defines machine learning and describes how it works through algorithms and data to make autonomous decisions without human intervention. The main components of machine learning include gathering raw data, converting data into information, gathering knowledge from information, and using that knowledge to make decisions. The document also describes the main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Finally, it outlines several applications of machine learning such as traffic prediction, speech and image recognition, medical diagnosis, spam detection, and more.
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The Decision Transformer model, introduced by Chen L. et al. in “Decision Transformer: Reinforcement Learning via Sequence Modeling,” transforms the reinforcement learning (RL) landscape by treating RL as a conditional sequence modeling problem.
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EDA or Exploratory Data Analysis is a method of examining and understanding data using multiple techniques like visualization, summary statistics and data transformation to abstract its core characteristics. EDA is done to get a sense of data and discover any potential problems or issues which need to be addressed and is generally performed before formal modeling or hypothesis testing.
How to build an AI-powered chatbot.pdfJamieDornan2
A chatbot is an Artificial Intelligence (AI) program that simulates human conversation by interacting with people via text or speech. Chatbots use Natural Language Processing (NLP) and machine learning algorithms to comprehend user input and deliver pertinent responses.
The Action Transformer Model represents a groundbreaking technological advancement that enables seamless communication with other software and applications, effectively bridging humanity and the digital realm.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
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CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
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Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
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How to build machine learning apps?
leewayhertz.com/how-to-build-a-machine-learning-app
Machine learning is a subfield of artificial intelligence that focuses on developing models that
learn from data and demonstrate incremental improvement in performance over time. Even
though many still associate “machine learning” with unrealistic sci-fi tales, we now see
applications of this technology everywhere around us. Facebook’s chatbots, Spotify and
Netflix suggestions, Alexa from Amazon, online spellcheckers, and predictive text on your
phone are just a few instances of machine learning technology at work. Market revenue for
machine learning (ML) in 2021 was estimated at $15.44 billion. The market is projected to
increase from USD 21.17 billion in 2022 to USD 209.91 billion in 2029, with a CAGR of
38.8%.
Industries are progressively embracing the creation of machine-learning apps that perform
various tasks using machine-learning algorithms, including predictive text input, speech
recognition, object detection, and more. Additionally, they can gather information from user
behavior and preferences, allowing for more individualized recommendations and a better
user experience. Machine learning enables tracking heart rate, activity level, and other
metrics in real-time on wearable devices like smartwatches and fitness trackers.
Incorporating machine learning into applications can enhance user experience and offer
insightful data on user behavior and preferences, creating new opportunities for businesses
to engage with their clients.
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In this article, we will dive deep into machine learning fundamentals and their components
and learn how to build a machine learning app.
What is machine learning?
Key components of machine learning
Machine learning methods
Machine learning algorithms
Use cases of machine learning
How to build a machine learning app?
What is machine learning?
Machine learning is a sub-field of artificial intelligence (AI) that focuses on creating statistical
models and algorithms that allow computers to learn and become more proficient at
performing particular tasks. Machine learning algorithms create a mathematical model with
the help of historical sample data, or “training data,” that assists in making predictions or
judgments without being explicitly programmed. Machine learning uses computer science
and statistics to create prediction models. It requires a huge amount of data to perform well;
in short, the higher the volume of data, the higher the accuracy.
When a machine learning system receives new data, it forecasts the results based on the
patterns learned from the historical data. The amount of data used to train the model affects
how precisely the output is predicted because a larger data set allows for the creation of a
more accurate model.
One may readily see the importance of machine learning by looking at numerous
applications. Machine learning is presently used in self-driving cars, cyber fraud detection,
and face recognition, among other use cases. Some top companies like Netflix and Amazon
have created machine-learning models that analyze user interests and offer personalized
product suggestions.
Key components of machine learning
The key components of machine learning are:
Data
Data is a key component in machine learning and provides the foundation for machine
learning algorithms. Machines require vast amounts of data to learn from to function and
make informed decisions. Any unprocessed information, value, sound, image, or text can be
considered data. The accuracy and effectiveness of a machine learning model heavily
depend on the quality and quantity of data used for its training.
While creating a data set, ensure that it has the 5V characteristics:
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Volume: The amount of information required for the model to be accurate and effective
matters. The accuracy of the machine learning model will increase with the size of the data
collected.
Velocity: The speed of data generation and processing is also crucial. Real-time data
processing may be required in some cases to get accurate results.
Variety: The data set should include variety in form, for instance, structured, unstructured,
and semi-structured data.
Veracity: Data cleanliness, consistency, and error-free are all qualities and accuracy aspects
of data. Only with accurate data a precise output can be expected.
Value: The information in the data must be valuable to draw any conclusion.
Model
The model serves as the underlying core component of machine learning and represents the
link between input and output to generate precise and fresh data. It is trained on a dataset to
identify underlying patterns and produce accurate results. Following training, the model is
tested to determine if it can provide fresh and precise data; if the test is successful, it is then
used for real-world applications.
Let’s take an example to understand this further. You want to create a model that considers
characteristics like age, body mass index (BMI), and blood sugar levels, to identify whether a
person has diabetes. We must first compile a dataset of people with diabetes and the
associated health metrics. The algorithm uses the dataset of people with diabetes and
considers their health indicators to analyze the data for patterns and relationships and
generate accurate results. It identifies the underlying relationships between the outcome
(diabetes status) and the input features (blood sugar level, BMI, and age). Once trained, the
model can predict if new patients have diabetes using information like blood sugar level,
weight, and age.
Algorithm
A model is trained using an algorithm that can learn the hidden patterns from the data,
predict the output, and improve the performance from experiences. It is an essential part of
machine learning since it powers the learning process and affects the precision and potency
of the model.
A training dataset consists of input data and associated output values. Once patterns and
associations have been identified in the data, several mathematical and statistical techniques
are used to determine the underlying relationship between the input and output. For
example, when we have a dataset of animal photos with their matching species labels, we
need to train a machine-learning model to identify the species of animals in the photograph.
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A convolutional neural network (CNN) may be used for this purpose. The CNN method
breaks down the incoming visual data into numerous layers of mathematical operations that
recognize features like edges, shapes, and patterns. The image is then classified into one of
the species categories using these characteristics.
However, several alternative methods exist, including decision trees, logistic regression, k-
nearest neighbors, etc. The provided dataset and the issues that must be solved determine
your algorithm.
Machine learning methods
Machine learning can be used in various methods for producing valuable outcomes. The
various machine learning techniques include:
Supervised learning
Supervised learning is a subset of machine learning that trains its algorithm using labeled
datasets. Labeled data are those that have output tagged along with the input. In this
approach, machines are trained using some labeled datasets, and then the machine is
expected to produce accurate results using those training data. Here’s an example to help
you understand: Images of several animals, including cats and dogs, are given to the
machine with labels. The machine uses the animals’ attributes, like shape, size, color, etc., to
learn and generate responses. Here the response generated is based on the labeled
dataset.
The two major types of supervised learning are:
Classification
Classification is used when an output variable is categorical and has two or more classes.
For instance, yes or no, true or untrue, male or female, etc. For example, if we want a
machine to recognize spam mail, we must first teach it what spam mail is so that it can
determine whether a message is a spam. This is done using different spam filters, which
examine the email’s topic, body, heading, etc., to check whether it contains deceptive
information. Spammers who have already been blacklisted use specific phrases and blacklist
filters to blackmail. The message is assessed using all these characteristics to determine its
spam score.
Regression
Regression is utilized when the output variable has a real or continuous value. There is a
relationship between two or more variables; a change in one variable is proportional to a
change in the other. For instance, estimating a house’s cost based on its size, location, and
other factors. Here price of the house is dependent on size, location etc.
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Unsupervised learning
Unsupervised learning involves using unlabeled data for machine learning. The computer
seeks trends and patterns in unlabeled data without being specifically told the desired result.
Let’s use the prior example to understand this better. Without labels, the machine is provided
pictures of animals, such as cats and dogs, to produce the response. The machine analyses
patterns in the input and categorizes the data appropriately using characteristics like form,
size, color etc. The datasets are completely unlabeled here, and the system compares and
analyzes the patterns to generate desired results.
Semi-supervised learning
Semi-supervised learning refers to machine learning that falls between supervised and
unsupervised learning. It is a training method that blends a significant amount of unlabeled
data with a small amount of labeled data. Semi-supervised learning seeks to create a
function that can accurately predict the output variable from the input variables, just like
supervised learning does. An unsupervised learning technique is used to cluster comparable
data, which also helps to categorize the unlabeled data into labeled ones. When there is a
large amount of unlabeled data accessible but categorizing it all would be expensive or
difficult, semi-supervised learning is extremely useful.
For example, suppose we have a collection of 10,000 photos of animals, but only 1000 have
been assigned to the appropriate class, such as dog or cat. With semi-supervised learning,
we can train a convolutional neural network (CNN) model to distinguish between dogs and
cats using the labeled photos. The remaining 9,000 photos that are not identified can then be
labeled using this trained model. With the addition of these anticipated labels, the original
labeled dataset can grow from 1000 to 10,000 labels. To increase the model’s accuracy in
categorizing dogs and cats, it can then be retrained on the new, larger labeled dataset.
Reinforcement learning
Reinforcement learning (RL) is a type of machine learning that involves an autonomous
agent learning to make decisions and take actions in an environment to maximize a reward
signal. The agent interacts with the environment by taking actions and receiving feedback in
the form of rewards or penalties, depending on the outcome of its actions.
RL aims to find the optimal policy, which is a set of rules that tells the agent which action to
take in a given state to maximize its long-term reward. The agent learns this policy through
trial and error by taking actions, observing the resulting state and reward, and updating its
decision-making strategy accordingly.
The environment in RL is typically defined by a set of states and actions that the agent can
take. The agent starts in a given state, takes action, and transitions to a new state. The
reward function evaluates the outcome of each action, providing the agent with feedback on
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its performance. The agent’s objective is to learn the policy that leads to the highest possible
reward over time.
In summary, reinforcement learning is a type of machine learning where an agent learns to
take actions in an environment to maximize a reward signal. It is a trial-and-error process
where the agent interacts with the environment and receives feedback through rewards or
penalties. The goal is to learn the optimal policy, which maximizes the long-term reward.
Deep learning
Deep learning algorithms are created to learn and develop over time through a process
known as backpropagation. The structure and operation of the human brain inspire these
algorithms. Deep learning includes training artificial neural networks with numerous layers to
evaluate and comprehend complicated data. Deep learning neural networks often include
numerous layers of linked nodes, where each layer picks up increasingly abstract properties
from the input data. The first layer receives the input data, processes it, and then transfers
the results to the following layer. This process is carried out with successive layers, refining
the features the prior layer had discovered. The network’s final output is a prediction or
classification based on the learned features.
Let’s say we wish to group pictures of animals into subcategories like cats, dogs, and birds.
A big collection of labeled photos, where each image is tagged with its appropriate category,
could be used to train a deep learning algorithm. Starting with the raw pixel values from the
photographs, the algorithm would feed them into a deep neural network comprising many
layers of interconnected nodes. Edges, textures, and forms are just a few examples of the
abstract qualities each layer would gradually learn from the input data. The network’s final
output layer would comprise nodes for each potential category, with each node producing a
score indicating a probability that the input image falls into that category.
Machine learning algorithms
An algorithm is a series of guidelines or instructions created to solve a particular issue or
carry out a certain operation. It is a set of precise actions a computer can carry out to solve a
problem or achieve a goal. There are various types of algorithms, a few of which are
discussed below:
Decision trees
The decision tree algorithm belongs to the supervised learning subset and can be applied to
classification and regression issues. Each leaf node of the decision tree corresponds to a
class label, and the tree’s internal nodes represent the attributes to resolve the problem.
Suppose you want to determine a person’s health based on age, dietary habits, level of
physical activity, etc. Questions like “What’s the age?” “Does he exercise?” and “Does he eat
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healthy food?” are the decision nodes in this scenario. And the leaves, which represent
either “healthy” or “unhealthy,” are the consequences. In this instance, the issue was binary
classification (a yes-no type problem). There are various types of decision trees, they are:
Classification trees: These decision trees group or classify input data into multiple classes
or categories by their traits or characteristics.
Regression trees: These decision-making structures are employed to forecast a continuous
numerical value in light of the input data.
Binary decision trees: These decision trees are classified as binary since each node only
has two possible outcomes.
Multiway decision trees: Each node has multiple alternative outcomes in these decision
trees.
K-nearest neighbors (K-NN)
K-nearest neighbors are a supervised machine-learning technique for solving classification
and regression issues. Nevertheless, classification issues are where it’s most frequently
applied. It is considered a non-parametric method since it makes no assumptions about the
underlying data distribution. When accepting input, it doesn’t do any calculations or other
operations; instead, it retains the information until the query is executed. It is a great option
for data mining and is also referred to as a lazy learning algorithm.
The parameter “K” in K-NN controls how many nearest neighbors will participate in the
voting. K-NN uses a voting system to identify the class of an unobserved observation. As a
result, the class with the most votes will be the class of the relevant data point. If K equals 1,
we will only utilize the data point’s closest neighbor to classify the data point. We will use the
ten closest neighbors if K equals ten, and so on. It is straightforward to comprehend and
apply the k-nearest neighbors categorization method, and it works best when the data points
are non-linear.
Support vector machines
One of the most prominent supervised learning algorithms, Support Vector Machine, or SVM,
is used to solve Classification and Regression problems. However, it is largely employed in
Machine Learning Classification issues. The SVM algorithm aims to establish the best
decision boundary or line to divide n-dimensional space into classes so that subsequent data
points can be assigned to the appropriate category. The term “hyperplane” refers to this
optimal decision boundary. To create the hyperplane, SVM selects the extreme points and
vectors.
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Finding a hyperplane in an N-dimensional space that classifies the data points is the goal of
the SVM method. The number of features determines the hyperplane’s size. The hyperplane
is essentially a line if there are just two input features, and the hyperplane turns into a 2-D
plane if there are three input features. Imagining something with more than three features
gets challenging.
The SVM technique can be used to develop a model that can accurately determine the
outcome. For example, the image contains a basket of fruits (apple and banana), and we
want the system to identify these fruits. We will train our model with several photographs of
apples and bananas to become familiar with the attributes of these fruits. As a result, the
support vector will see the extreme cases and attributes of apples and bananas when
drawing a judgment border between these two data sets. Based on the support vectors, it will
categorize it as an apple or banana.
Neural networks
A neural network is an artificial intelligence technique instructing computers to analyze data.
The human brain is the inspiration behind neural network architecture. Human brain cells,
called neurons, form a complex, highly interconnected network and send electrical signals to
each other to help humans process information. Similarly, an artificial neural network
comprises neurons that work together to solve problems. Artificial neurons are software
modules called nodes, and artificial neural networks are software programs or algorithms
that, at their core, use computing systems to solve mathematical calculations. Computers
can use this to build an adaptive system that helps them improve by learning from their
failures. As a result, artificial neural networks try to tackle challenging issues like
summarising documents or identifying faces.
The input layer, hidden layer, and output layer are the three layers that make up a simple
neural network. The input layer is where data from the outside world enters an artificial
neural network. Input nodes process, analyze, or categorize data before forwarding it to the
hidden layer. The input or other hidden layers serve as the input for hidden layers. Artificial
neural networks can have a lot of hidden layers. Each hidden layer evaluates the output from
the preceding layer, refines it, and then sends it to the output layer. The artificial neural
network’s output layer presents the complete data processing results.
Feedforward neural networks, also known as multi-layer perceptrons (MLPs), convolutional
neural networks (CNNs), and recurrent neural networks (RNNs), are among the various
types of neural networks that are used for different purposes.
Clustering
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It is an unsupervised learning method, meaning the algorithm gets no supervision and works
with an unlabeled dataset. The unlabeled dataset is grouped using machine learning
clustering or cluster analysis. It can be described as a method of dividing the data points into
clusters of related data points.
It identifies some comparable patterns in the unlabeled dataset, including shape, size, color,
behavior, etc., and divides them into groups based on the presence or absence of those
similar patterns. For example, in a shopping center, t-shirts are classified in one section,
trousers in another, and fruits and vegetables are arranged in different sections to help
customers find the products easily. The clustering process operates similarly.
The two main clustering techniques are Hard clustering (where each data point belongs to a
single group) and Soft Clustering (data points can belong to another group also). However,
there are several different Clustering techniques also available. The primary clustering
techniques in machine learning are listed below:
1. Partitioning clustering– This clustering method divides the data into non-hierarchical
groups. It’s also known as the centroid-based approach. The K-Means Clustering
algorithm is the most popular example of partitioning clustering. The dataset is split into
k groups, where K refers to the number of pre-defined groups. The cluster centroid is
designed to have a minimum distance between data points in one cluster compared to
other cluster centroids.
2. Density-based clustering– The density-based clustering algorithm joins into clusters,
and arbitrary shapes are formed as long as the highly packed regions can be
connected. After discovering several clusters in the dataset, the algorithm groups the
high-density areas into clusters. The sparser areas in data space separate the dense
sections from one another. If the dataset includes many dimensions and densities,
these algorithms may have trouble clustering the data points.
3. Distribution model-based clustering– In the distribution model-based clustering
method, the data is separated according to the likelihood that each dataset
corresponds to a specific distribution. A few distributions—most frequently the
Gaussian distribution—are presumptively used to classify the objects.
4. Hierarchical clustering– Hierarchical clustering can be utilized as an alternative to
partitioned clustering, as there is no need to pre-specify the number of clusters to be
produced. This method divides the dataset into clusters to produce a structure
resembling a tree known as a dendrogram. Removing the appropriate amount of the
tree makes it possible to choose the observations or any number of clusters. The
Agglomerative Hierarchical algorithm is the most popular example of this technique.
5. Fuzzy clustering– When using fuzzy clustering, data points might be included in more
than one category (or “cluster”). To identify patterns or commonalities between items in
a set, clustering separates data points into sections based on the similarity between
items; items in clusters should be similar and distinct from items in other groups.
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Apart from the above-discussed algorithms, many other algorithms are used in machine
learning, such as Naïve Bayes Algorithm, Random Forest Algorithm, Apriori Algorithm etc.
Use cases of machine learning
Automating numerous time-consuming processes has made machine-learning apps a vital
part of our life. Here are a few examples of typical use cases:
Self-driving cars
Self-driving cars heavily use machine learning algorithms to assess and analyze huge
amounts of real-time information from sensors, cameras, and other sources. Machine
learning algorithms are employed to identify and categorize items, such as cars, people
walking on the pavement, and traffic signs, to decide how the automobile should navigate the
road. To get better at driving over time, such as responding to shifting road conditions and
avoiding collisions, these algorithms also learn from prior driving experiences. Utilizing
machine learning, self-driving vehicles can provide passengers with increased safety,
effectiveness, and convenience while reducing traffic congestion.
Predict traffic patterns
Machine learning is commonly used in the logistics and transportation industries to predict
traffic patterns. To accurately predict traffic patterns and congestion levels, machine learning
algorithms can examine enormous volumes of historical traffic data, including weather, time
of day, and other factors. These forecasts can streamline traffic flow, lessen obstructions and
delays, and optimize vehicle travel times. Moreover, ML can forecast demand for public
transportation, improve traffic signal timing, and give drivers real-time traffic alerts and
alternate routes. Cities and transportation agencies may increase the effectiveness and
safety of their transportation systems, lower carbon emissions, and improve the overall travel
experience for customers by utilizing machine learning to predict traffic patterns.
Fraud detection
Machine learning is crucial for detecting fraud in the financial, e-commerce, and other
sectors. Machine learning algorithms can evaluate massive volumes of transactional data,
including user behavior, past transactions, and other factors, to find trends and abnormalities
that might indicate fraudulent conduct. To increase their precision and recognize new forms
of fraudulent activity, these algorithms can learn from prior instances of fraud. Organizations
can decrease financial losses, avoid reputational harm, and increase consumer trust by
utilizing machine learning for fraud detection. Moreover, it can optimize fraud protection
methods, detect fraud in real time, and streamline investigation processes. In general,
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machine learning is a potent fraud detection technique that can assist firms in staying ahead
of the growing cyber risks and safeguarding their resources and clients from criminal
activities.
Image recognition
Machine learning is used extensively in various sectors, including security, retail, and
healthcare. ML algorithms can evaluate and classify huge amounts of visual data, including
medical photos, product images, and surveillance footage, to find patterns and features that
distinguish one thing from another.
Image recognition is used in the healthcare industry to evaluate medical pictures such as X-
rays, MRIs, and CT scans to detect and treat diseases. In the retail industry, it is used to
analyze product images, find flaws, and locate counterfeit goods. Image recognition is used
in security to review surveillance footage, spot potential dangers, and monitor crowd
behavior. Organizations may automate image analysis activities, increase accuracy and
efficiency, and uncover new insights and prospects by utilizing machine learning algorithms
for image recognition.
Speech recognition
A well-known use of machine learning (ML) is speech recognition. Computers can
understand and recognize human voices using machine learning (ML) techniques and then
translate them into text or other data types that may be utilized for various tasks.
Among the frequent applications of speech recognition enabled by ML are:
Virtual assistants: The core technology behind virtual assistants like Amazon’s Alexa,
Apple’s Siri, Google Assistant, and Microsoft’s Cortana is speech recognition. These helpers
can answer inquiries, play music, create reminders, and execute other chores by
understanding natural language commands.
Transcription: Voice recognition is frequently used to convert audio and video content,
including meetings, interviews, podcasts, and dictations, into written text. Machine learning
(ML) algorithms can accurately convert spoken words into text, saving time and effort
compared to manual transcription.
Customer service: Customer support interactions can be automated in call centers using
ML-powered speech recognition. Automatic voice systems can recognize client inquiries,
respond to them, and route calls to the agent or department as and when required.
How to build a machine learning app?
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Here is a general breakdown of the procedure needed to create machine-learning apps. We
will use the Naive Bayes Classifier, one of the simplest machine learning techniques, and
construct an app that forecasts airline information whenever a query is generated. The
following stages demonstrate how to build a machine-learning app:
Step 1- Libraries and datasets
In this step, install scikit-learn and pandas:
pip install scikit-learn
pip install pandas
We’ll apply a dataset using an example of airline travel where we create a query regarding
the information on flights, and we get the response as per the query generated. Let’s
examine this dataset using Pandas.
import pandas as pd
df_train = pd.read_csv('atis_intents_train.csv')
df_test = pd.read_csv('atis_intents_test.csv')
Step 2- Preprocess data
In this process, we clean the raw data. Before running the algorithm, the dataset is
preprocessed to look for missing values, noisy data, and other abnormalities.
df_train = pd.read_csv('atis_intents_train.csv')
df_train.columns = ['intent','text']
df_train = df_train[['text','intent']]
df_test = pd.read_csv('atis_intents_test.csv')
df_test.columns = ['intent','text']
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df_test = df_test[['text','intent']]
Step 3- Remove extra spaces
Extra spaces between the text and intent are removed using the strip method. Use the
following code to remove spaces.
df_train['text'] = df_train['text'].str.strip()
df_train['intent'] = df_train['intent'].str.strip()
df_test['text'] = df_test['text'].str.strip()
df_test['intent'] = df_test['intent'].str.strip()
Step 4- Vectorize the text data using TF-IDF
We will vectorize the data to convert it into numerical form, which would be the training input.
vectorizer = TfidfVectorizer(stop_words='english')
X_train = vectorizer.fit_transform(df_train['text'])
X_test = vectorizer.transform(df_test['text'])
Step 5- Encode the target variable
We store the labels corresponding to the data points from the training and testing dataset in
y_train and y_test, respectively.
y_train = df_train['intent']
y_test = df_test['intent']
Step 6- Train a Naive Bayes classifier
We are using the Naive Bayes classifier, and the code we used are:
clf = MultinomialNB()
clf.fit(X_train, y_train)
Step 7- Install flask and quick set up
Run the following line in the terminal to install the flask.
pip install flask
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Step 8- Define the flask app
Now follow these instructions to ensure everything is operating properly.
The following code is an example of a simple app taken from the Flask documentation.
app = Flask(__name__)
@app.route('/')
def home():
return render_template('index.html')
Let’s explain this further:
app = Flask( name__) – Creates a Flask class instance with the command app Flask
name. The name of the application’s module is represented by the variable name,
which aids Flask in knowing where to seek resources like “templates,” which we’ll
utilize later.
@app.route(“/”): @ – denotes decorators (they modify the behavior of a function or
class). Flask is informed by the route() decorator, which URL should call our function.
Step 9 – Make an HTML file
To begin building the front end, first, create a folder where your app is stored called
“templates” (you are not allowed to use any other name). Make an HTML file and place it in
the “templates” folder. You can give this file whatever name you like; we named it index.html.
Open the HTML file, type doc or HTML and hit the Tab key. Furthermore, your IDE will
produce a simple HTML template for you. If not, copy and paste the template below.
<!DOCTYPE html>
<html >
<head>
<meta charset="UTF-8">
<title>My Machine Learning Model</title>
<!-- Quick Start: CSS -->
<link href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css"
rel="stylesheet" integrity="sha384-
1BmE4kWBq78iYhFldvKuhfTAU6auU8tT94WrHftjDbrCEXSU1oBoqyl2QvZ6jIW3"
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crossorigin="anonymous">
Step 10- Add navigation bar
Go to the website’s navbar section, copy and paste the code into the< head> section. We’ll
also add a navigation bar (we don’t need it, but it’ll help you get accustomed to bootstrap).
We will modify the navbar’s color to dark before running the script by changing the light
components in the first line of code to dark (if you leave it as is, it will have a light color).
<!-- Nav Bar -->
<nav class="navbar navbar-expand-lg navbar-dark bg-dark">
<div class="container-fluid">
<a class="navbar-brand" href="#">Navbar</a>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-
target="#navbarSupportedContent" aria-controls="navbarSupportedContent" aria-
expanded="false" aria-label="Toggle navigation">
<span class="navbar-toggler-icon"></span>
</button>
<div class="collapse navbar-collapse" id="navbarSupportedContent">
<ul class="navbar-nav me-auto mb-2 mb-lg-0">
<li class="nav-item">
<a class="nav-link active" aria-current="page" href="#">Home</a>
</li>
<li class="nav-item">
<a class="nav-link" href="#">Link</a>
</li>
<li class="nav-item dropdown">
<a class="nav-link dropdown-toggle" href="#" id="navbarDropdown" role="button" data-bs-
toggle="dropdown" aria-expanded="false">
Dropdown
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</a>
<ul class="dropdown-menu" aria-labelledby="navbarDropdown">
<li><a class="dropdown-item" href="#">Action</a></li>
<li><a class="dropdown-item" href="#">Another action</a></li>
<li><hr class="dropdown-divider"></li>
<li><a class="dropdown-item" href="#">Something else here</a></li>
</ul>
</li>
<li class="nav-item">
<a class="nav-link disabled">Disabled</a>
</li>
</ul>
<form class="d-flex">
<input class="form-control me-2" type="search" placeholder="Search" aria-label="Search">
<button class="btn btn-outline-success" type="submit">Search</button>
</form>
</div>
</div>
</nav>
</head>
Step 11 – POST request using form element through UI
We are making a post request via form element, which will hit the endpoint at the backend,
providing us with the desired outcome.
<body>
<div class="login">
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<h2>Airline Travel Information System</h2>
<p>Enter Your Query:</p>
<!-- Inputs for our ML model -->
<form action="{{ url_for('predict')}}" method="post">
<input type="text" name="text" placeholder="Text" required="required" />
<button type="submit" class="btn btn-primary btn-block btn-large">Predict Value!</button>
</form>
<br>
<br>
<b> {{ prediction_text }} </b>
</div>
</body>
</html>
Step 12- See the result
When you add a query, the system will provide the desired outcomes.
Airline Travel Information System
Enter Your Query
Intent: axis_ground_service
Text Predict Value!
Endnote
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Machine learning is a subset of AI that enables machines to extract information from data
and make predictions or judgments without being explicitly programmed. As a result, it has
become a useful tool for businesses looking to gather information, streamline operations,
and enhance decision-making. Its capacity to learn from data and improve over time has
caused a rise in its use across industries, from finance and e-commerce to healthcare.
Machine learning applications have improved the ability to detect fraud, recognize faces in
pictures, recognize speech, and perform other time-consuming tasks.
Developing machine learning apps is a complex process that demands a deep
understanding of algorithms, data science, and statistics. The right techniques must be
adopted to develop machine learning-based solutions. It is advised to collaborate with an
expert that can guide you through the process and assist you in creating highly valuable
applications. Machine learning has the power to transform your business and keep you one
step ahead of the competition in a digital world that is rapidly changing.
Want to develop robust machine-learning apps? Contact LeewayHertz for your requirements.
We create reliable and highly performant machine-learning apps with advanced features.