Category Archives: Machine Learning
How NAU is making self-driving cars safer and smarter The NAU … – NAU News
How do we make autonomous cars safer?
That question, which is critical as self-driving cars are increasingly found on American roads, is just one that NAU researcher Truong Nghiem hopes to answer with a new project that looks at ways to integrate machine learning and physical principles into large-scale cyber-physical systems.
Nghiem, an assistant professor in the School of Informatics, Computing, and Cyber Systems, received an NSF CAREER grant for this project, which aims to develop a comprehensive and flexible framework for effective and efficient machine learning with physical constraints, which can fundamentally change how we apply machine learning to complex systems like smart energy systems, industrial automation systems and autonomous robots and cars. The CAREER award is the National Science Foundations most prestigious award for early-career faculty.
A critical challenge is how to guarantee the performance and safety of these systems, as they are typically performance- and/or safety-critical, where any failure could have devastating consequences, Nghiem said. Our approach is to tightly integrate machine learning and physical principles. The framework developed in this project will be a foundation for such an integration and will be a stepping stone toward solving the challenge. It will help make future autonomous cyber-physical systems reliable and safe.
A cyber-physical system (CPS) is an engineered system that is built from, and depends on, seamless integration of computational and physical components. They are the foundation of many modern engineering systems that make up our daily life, including cars, robots, medical devices, power grids and more, and they are becoming even more common as our lives become more automated.
Many of these systems employ machine learning and, increasingly, artificial intelligence. However, machine learning, which isnt always informed by physics, doesnt always provide the best way to teach these systems. Nghiems research focuses on physics-informed machine learning (PIML), which is capable of developing methods that seamlessly embed knowledge of a physical system into machine learning, leading to robust, accurate and consistent models.
In autonomous cars, rovers, drones and similar systems, that means fewer system errors and a safer experience for the vehicle and nearby people. However, current PIML methods are functionally too small to meet those needs.
Enter composite physics-informed machine learning, or CPIML. Nghiems project aims to advance the data-driven learning of complex, large-scale systems by synthesizing many PIML and physical component modelsits the physics equivalent of LEGO blocks that can be put together to build much larger, more complex models, with each block being an already-developed model or piece of machine learning.
This groundbreaking solution will require integrating the cyber world (machine learning, AI and computing) and the physical world (dynamic and control systems) in engineered systems, so that each world is aware of and can integrate with the other. The result will be a safer world through which people move.
Smart and autonomous cyber-physical systems will tremendously impact our lives in the near future, Nghiem said. Our productivity will substantially increase with autonomous helper robots, advanced industrial automation (Industry 4.0) and many autonomous systems in our work and personal life. Our energy infrastructures will be more efficient and reliable, and our transportation will be safer and faster. These all depend on modern technologies, including cyber-physical systems and recent advancements in machine learning and AI.
Nghiems research will also offer valuable opportunities for graduate and undergraduate students to engage in software development and real-world applications.
Heidi Toth | NAU Communications(928) 523-8737 | heidi.toth@nau.edu
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How NAU is making self-driving cars safer and smarter The NAU ... - NAU News
LSU Partners to Launch AI and Machine Learning Program – Biz New Orleans
NEW YORK LSU Online & Continuing Education and national tech education provider Fullstack Academy have announced the launch of an Artificial Intelligence and Machine Learning Bootcamp program.
The curriculum, designed and delivered by industry-experienced tech practitioners, is designed to provide the skills and hands-on training needed to build specialized data career paths in AI and machine learning in 26 weeks.
Demand for AI and machine learning professionals is projected to increase by nearly 36% over the next decade, according to the U.S. Bureau of Labor Statistics, far surpassing the average growth rate of roughly 6% for all occupations. Notably, this AI boom also has the potential to contribute a staggering $15.7 trillion to the global economy by 2035, according to PwC.
The rapid, widespread adoption and influence of AI and machine learning technologies are revolutionizing the way we work, live, and interact with technology every day. This unfolding potential across various industries has prompted companies and organizations worldwide to intensify their investments, including efforts to expand talent pools rather than reducing them, said Nelis Parts, CEO of Fullstack Academy. This new program with LSU Online & Continuing Education will enable professionals from all skill levels and interests to embark on a rewarding career path and contribute to an ever-evolving sector.
Graduates of the LSU AI & Machine Learning Bootcamp can qualify for entry-level positions across the country, where the U.S. median salaries for Data Analyst, Artificial Intelligence Engineer, and Machine Learning Engineer roles range from $71,034 to $151,063 (ZipRecruiter). Many positions are available with prominent companies, including Cox Communications, United Rentals, Inc., Veusol Technologies Inc., and the Internal Revenue Service of Louisiana.
The LSU AI & Machine Learning Bootcamp powered by Fullstack Academy will teach students practical and theoretical machine learning with hands-on, application-based training using real-world tools. Designed for both beginners and experienced tech professionals, students of the 26-week, part-time program will learn practical skills used by AI professionals in the fieldincluding Applied Data Science with Python, Machine Learning, Deep Learning, and Deep Neural Networksand their applications within Artificial Intelligence technology.
We are thrilled to add to our successful portfolio of program offerings in partnership with Fullstack Academy. The LSU AI & Machine Learning Bootcamp presents a comprehensive curriculum encompassing the entire spectrum of the field, from foundational principles to advanced concepts, said Kappie Mumphrey, vice president of LSU Online & Continuing Education. By equipping students with knowledge and skills in AI, we empower them to become the next generation of AI experts and problem solvers. Emphasizing the importance of AI education not only cultivates a skilled workforce but also ensures that our future leaders are equipped to navigate the opportunities and ethical considerations of an AI-driven world.
Applications are now open for the live online LSU AI & Machine Learning Bootcamp. The deadline to apply is July 25, 2023, for the programs inaugural cohort commencing July 31, 2023.
The LSU AI & Machine Learning Bootcamp does not require university enrollment. Scholarships are available to current LSU students and alumni, as well as active-duty service members and veterans. Interested learners can see these details and more on the LSU AI & Machine Learning Bootcamp website.
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LSU Partners to Launch AI and Machine Learning Program - Biz New Orleans
AI & machine learning are improving weather forecasts, but won’t … – The Weather Network
Australian meteorologist Dean Narramore explains why its hard to forecast large thunderstorms.
Today, weather forecasters primary tools are numerical weather prediction models. These models use observations of the current state of the atmosphere from sources such as weather stations, weather balloons and satellites, and solve equations that govern the motion of air.
These models are outstanding at predicting most weather systems, but the smaller a weather event is, the more difficult it is to predict. As an example, think of a thunderstorm that dumps heavy rain on one side of town and nothing on the other side. Furthermore, experienced forecasters are remarkably good at synthesizing the huge amounts of weather information they have to consider each day, but their memories and bandwidth are not infinite.
Artificial intelligence and machine learning can help with some of these challenges. Forecasters are using these tools in several ways now, including making predictions of high-impact weather that the models cant provide.
In a project that started in 2017 and was reported in a 2021 paper, we focused on heavy rainfall. Of course, part of the problem is defining heavy: Two inches of rain in New Orleans may mean something very different than in Phoenix. We accounted for this by using observations of unusually large rain accumulations for each location across the country, along with a history of forecasts from a numerical weather prediction model.
We plugged that information into a machine learning method known as random forests, which uses many decision trees to split a mass of data and predict the likelihood of different outcomes. The result is a tool that forecasts the probability that rains heavy enough to generate flash flooding will occur.
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AI & machine learning are improving weather forecasts, but won't ... - The Weather Network
Machine Learning Used to Discover New Superconductors – Fagen wasanni
Superconductors, known for their ability to exhibit zero electrical resistance when cooled below a critical temperature, have tremendous potential for applications in energy, transportation, and cutting-edge electronics. Researchers from Georgia Tech and Hanoi University of Science and Technology have taken the first step towards incorporating atomic-level information into machine learning pathways to discover new conventional superconductors.
To overcome the barrier of lacking atomic level information, the researchers curated a dataset of 584 atomic structures with over 1100 computed values of and log at different pressures. Machine learning models were developed for and log and used to screen over 80,000 entries in the Materials Project database. Through first-principles computations, the researchers identified two materials that may exhibit superconductivity at a critical temperature of approximately 10^-15K and ambient pressure.
The researchers used the machine learning models to predict superconducting properties for 35 candidates, with six of them having the highest predicted critical temperatures. Further stabilization calculations were required for some candidates. After verifying the stability of two remaining candidates, CrH and CrH2, the researchers calculated their superconducting properties using first-principles calculations. The accuracy of the predictions was validated within 2-3% of the reported values through additional calculations using the local-density approximation (LDA) XC functional.
Additionally, the researchers investigated the synthesizability of the superconductors by tracing their origin in the Inorganic Crystalline Structure Database (ICSD). They found that these materials had been experimentally synthesized in the past, providing hope for future tests to confirm their predicted superconductivity.
In future research, the researchers plan to enhance their machine learning approach by expanding and diversifying the dataset, employing deep learning techniques, and integrating an inverse design strategy for more efficient exploration of materials. They also aim to collaborate with experimental experts for real-world testing and synthesis of high critical temperature superconductors.
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Machine Learning Used to Discover New Superconductors - Fagen wasanni
Artificial Intelligence and Machine Learning Can Revolutionize … – Fagen wasanni
The Nigerian Communications Commission (NCC) has highlighted the potential of Artificial Intelligence (AI) and Machine Learning (ML) to revolutionize various industries. The Executive Vice Chairman of NCC, Prof. Umar Danbatta, made this statement at the 2023 ICTEL Expo organized by the Lagos Chamber of Commerce and Industry (LCCI). The theme of the event was Tech Disruption: Transforming Industries with Innovation.
According to Danbatta, AI and ML technologies have the power to shape sectors such as healthcare, finance, manufacturing, and transportation. He mentioned that AI-powered algorithms enable accurate predictions, improved decision-making, and automation of mundane tasks. By analyzing vast amounts of data, businesses can gain valuable insights and optimize their operations to deliver better products and services.
Danbatta also highlighted the impact of the Internet of Things (IoT) on industries such as agriculture, energy, and logistics. IoT enables resource optimization, equipment monitoring, and overall efficiency improvement through real-time data provided by sensors and smart devices.
Furthermore, Danbatta noted the transformative force of blockchain technology, particularly in finance and supply chain management. Blockchain creates decentralized and transparent ledgers, ensuring secure and efficient transactions while reducing costs and eliminating intermediaries.
The fifth generation network (5G) was also mentioned as an enabler of new possibilities in autonomous vehicles, augmented reality, and telemedicine. The convergence of Virtual Reality (VR) and Augmented Reality (AR) technologies is disrupting multiple industries, especially in entertainment, education, and retail, by offering immersive and interactive experiences.
To embrace innovation and adapt to the changing landscape, businesses are advised to be agile and experiment with emerging technologies. The NCC believes that this disruption and innovation will drive sustainable growth, economic diversification, and enhanced living standards for all Nigerians.
The commissions strategic vision plan, Aspire 2024, prioritizes connectivity and broadband access as vital for socio-economic development. By expanding network coverage and promoting broadband infrastructure deployment, the NCC aims to provide reliable and affordable internet access to every corner of Nigeria.
As of May 2023, telecom subscriptions in Nigeria reached 227,179,946 with a teledensity of 119 percent. The telecom industry contributed 14.13 percent to the GDP in the first quarter of 2023. The NCC also focuses on consumer protection, privacy, data security, and efficient spectrum management to optimize connectivity and facilitate emerging technologies.
By promoting AI, ML, IoT, blockchain, 5G, VR, and AR, the NCC intends to unlock the transformative potential of these technologies and enable new services and applications in Nigeria.
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Artificial Intelligence and Machine Learning Can Revolutionize ... - Fagen wasanni
AI-enhanced night-vision lets users see in the dark – Nature.com
In this episode:
There are many methods for better night-vision, but often these rely on enhancing light, which may not be present, or using devices which can interfere with one another. One alternative solution is to use heat, but such infrared sensors struggle to distinguish between different objects. To overcome this, researchers have now combined such sensors with machine learning algorithms to make a system that grants day-like night-vision. They hope it will be useful in technologies such as self-driving cars.
Research article: Bao et al.
News and Views: Heat-assisted imaging enables day-like visibility at night
Benjamin Franklins anti-counterfeiting money printing techniques, and how much snow is on top of Mount Everest really?
Research Highlight: Ben Franklin: founding father of anti-counterfeiting techniques
Research Highlight: How much snow is on Mount Everest? Scientists climbed it to find out
We discuss some highlights from the Nature Briefing. This time, the cost to scientists of English not being their native language, and the mysterious link between COVID-19 and type 1 diabetes.
Nature News: The true cost of sciences language barrier for non-native English speakers
Nature News: As COVID-19 cases rose, so did diabetes no one knows why
Subscribe to Nature Briefing, an unmissable daily round-up of science news, opinion and analysis free in your inbox every weekday.
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AI-enhanced night-vision lets users see in the dark - Nature.com
Human Activity Recognition Using Deep Learning Techniques – Fagen wasanni
Advances in sensor technology have led to a surge of interest in recognizing human activities based on sensor data. This recognition, known as Human Activity Recognition (HAR), has wide-ranging applications in everyday life, such as medical care, movement analysis, intelligent monitoring systems, and smart homes.
HAR can be categorized into two main classes: video-based and sensor-based. Video-based HAR systems rely on cameras to capture videos and images and utilize computer vision technology to identify human actions. However, these systems are susceptible to environmental factors and privacy concerns. In contrast, sensor-based systems use environmental or wearable sensors embedded in smart devices like smartphones and smartwatches to determine human actions.
Wearable sensors present a complex challenge in HAR due to the classification of time-series data with multiple variables. Traditional machine learning algorithms have been successful in categorizing human behaviors, but manual feature extraction requires specialized knowledge, limiting its practicality. Deep learning models, particularly convolutional neural networks (CNN), have revolutionized HAR by automating the feature extraction process.
CNN models have proven effective in extracting features and achieving accuracy in sensor-based HAR. The combination of CNN and recurrent neural networks (RNN) allows for a comprehensive representation of spatial and temporal features. To enhance the effectiveness of HAR, the squeeze-and-excitation (SE) block acts as a channel-attention mechanism to prioritize valuable feature maps while suppressing unreliable ones.
In this study, a novel approach called ResNet-BiGRU-SE is proposed, combining a hybrid CNN with a channel attention system for human activity recognition. Experiments using standard datasets demonstrated that the proposed model outperforms previous deep learning architectures in terms of accuracy.
The utilization of sensor-based HAR holds immense potential in various domains, such as healthcare, sports analysis, surveillance systems, and human-robot interactions. It enables advanced movement tracking systems, automatic interpretation of player actions, user identification in surveillance, and gesture recognition.
Harnessing the power of sensor-based HAR can bring significant advantages and advancements to these diverse sectors. The proposed model presents a promising solution for accurately identifying and predicting human behaviors based on sensor data.
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Human Activity Recognition Using Deep Learning Techniques - Fagen wasanni
Machine learning and computer vision allow study of animal behavior without markers – Phys.org
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With a new markerless method it is now possible to track the gaze and fine-scaled behaviors of every individual bird and how that animal moves in the space with others. A research team from the Cluster of Excellence Center for the Advanced Study of Collective Behavior (CASCB) at the University of Konstanz developed a dataset to advance behavioral research.
Researchers are still puzzling over how animal collectives behave, but recent advances in machine learning and computer vision are revolutionizing the possibilities of studying animal behavior. Complex behaviors, like social learning or collective vigilance can be deciphered with new techniques.
An interdisciplinary research team from the Cluster of Excellence Center for the Advanced Study of Collective Behavior (CASCB) at the University of Konstanz and the Max Planck Institute of Animal Behavior has now succeeded in developing a novel markerless method to track bird postures in 3D just by using video recordings. Credit: University of Konstanz
It is no longer necessary to attach position or movement transmitters to the animals. With this method called 3D-POP (3D posture of pigeons) it is possible to record a group of pigeons and identify the gaze and fine-scaled behaviors of every individual bird and how that animal moves in the space with others. "With the dataset, researchers can study collective behavior of birds by just using at least two video cameras, even in the wild," says Alex Chan, Ph.D. student at the CASCB.
The dataset was released at the Conference on Computer Vision and Pattern Recognition (CVPR) in June 2023 and available via open access so that it can be reused by other researchers. The researchers Hemal Naik and Alex Chan see two potential application areas: Scientists working with pigeons can directly use the dataset. With at least two cameras they can study the behavior of multiple freely moving pigeons. The annotation method can be used with other birds or even other animals so that researchers can soon decipher the behavior of other animals.
More information: 3D-POPAn Automated Annotation Approach to Facilitate Markerless 2D-3D Tracking of Freely Moving Birds With Marker-Based Motion Capture. openaccess.thecvf.com/content/ CVPR_2023_paper.html
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Machine learning and computer vision allow study of animal behavior without markers - Phys.org
The Role of AI and Machine Learning in Optimizing Irrigation Emitters – EnergyPortal.eu
Exploring the Impact of AI and Machine Learning on the Optimization of Irrigation Emitters
The advent of Artificial Intelligence (AI) and Machine Learning (ML) has ushered in a new era of technological advancements, revolutionizing various sectors, including agriculture. In particular, these technologies are playing a significant role in optimizing irrigation emitters, thereby improving water efficiency and crop yield.
Irrigation emitters, the components of an irrigation system that distribute water to the plants, are critical to the success of agricultural endeavors. Traditionally, the optimization of these emitters has been a manual and time-consuming process, often leading to water wastage and sub-optimal crop yield. However, with the integration of AI and ML, this scenario is rapidly changing.
AI and ML algorithms can analyze vast amounts of data from various sources, such as weather forecasts, soil moisture sensors, and crop health indicators. This data analysis allows the system to make informed decisions about when and how much to irrigate, minimizing water waste and maximizing crop yield. For instance, if the system detects an upcoming rainfall, it can reduce or even stop irrigation, saving significant amounts of water.
Moreover, these technologies can also predict future irrigation needs based on historical data and current conditions. This predictive capability enables farmers to plan their irrigation schedules more effectively, further enhancing water efficiency. Additionally, AI and ML can identify patterns and trends that may not be apparent to the human eye, providing valuable insights for improving irrigation strategies.
The application of AI and ML in optimizing irrigation emitters also contributes to sustainability. Agriculture is one of the largest consumers of freshwater globally, and efficient irrigation is key to reducing water usage. By optimizing irrigation emitters, AI and ML can significantly reduce water consumption, contributing to the conservation of this precious resource.
Furthermore, these technologies can also help in mitigating the effects of climate change on agriculture. As weather patterns become increasingly unpredictable, the ability to adapt irrigation strategies in real-time becomes crucial. AI and ML, with their predictive and adaptive capabilities, can help farmers navigate these challenges, ensuring the continued productivity of their farms.
However, the implementation of AI and ML in optimizing irrigation emitters is not without challenges. The accuracy of these systems depends on the quality and quantity of data available. Therefore, there is a need for robust data collection and management systems to support these technologies. Additionally, there is a need for ongoing research and development to further refine these technologies and make them more accessible to farmers worldwide.
In conclusion, AI and ML are playing a pivotal role in optimizing irrigation emitters, improving water efficiency, and enhancing crop yield. These technologies are not only transforming agriculture but also contributing to sustainability and climate change mitigation. As we continue to explore and harness the potential of AI and ML, we can look forward to a future where agriculture is more efficient, sustainable, and resilient.
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The Role of AI and Machine Learning in Optimizing Irrigation Emitters - EnergyPortal.eu
Best Machine Learning Books: Inspire Your Technological Journey with Expert Knowledge – Economic Times
Embark on an extraordinary journey into the world of machine learning with our carefully curated selection of the best machine learning books. Discover the power of cutting-edge algorithms, delve into the realm of artificial intelligence, and unlock the secrets of data-driven decision-making. Whether you're an aspiring data scientist, a seasoned programmer, or a curious mind eager to explore the limitless possibilities of this transformative field, these books offer unparalleled insights and practical guidance. From foundational principles to advanced techniques, each page is a treasure trove of knowledge that will empower you to create intelligent solutions, gain a competitive edge, and embrace the future of technology. Elevate your skills and stay ahead in the dynamic landscape of machine learning with the best machine book.List of the best machine learning booksName and authorAmazon RatingsAmazon PriceDeep Learning by Aaron Courville, Ian Goodfellow, Yoshua Bengio4.7 / 5Rs. 5,737Advances in Financial Machine Learning by Marcos Lopez de Prado4.4 / 5Rs. 3,406Machine Learning for Algorithmic Trading by Stefan Jansen4.4 / 5Rs. 3,385Python Machine Learning by Sebastian Raschka , Vahid Mirjalili4.5 / 5Rs. 3,020MATHEMATICS FOR MACHINE LEARNING by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong4.5 / 5Rs. 1,569Introduction to Machine Learning with Python by Andreas Muller4.5 / 5Rs. 1,300Machine Learning For Absolute Beginners by Oliver Theobald4.4 / 5Rs. 1,151Machine Learning by Tom M. Mitchell4.3 / 5Rs. 999Machine Learning using Python by Manaranjan Pradhan, U Dinesh Kumar4.3 / 5Rs. 579The Hundred-Page Machine Learning Book by Andriy Burkov4.6 / 5Rs. 4421. Deep Learning by Aaron Courville, Ian Goodfellow, Yoshua Bengio"Deep Learning (Adaptive Computation and Machine Learning series) Hardcover" by Aaron Courville, Ian Goodfellow, and Yoshua Bengio is a comprehensive guide to the fascinating world of machine learning. Exploring the hierarchy of concepts in deep learning, the book covers mathematical foundations, practical methodologies, and real-world applications. Whether you're a student, researcher, or software engineer, this book equips you with the tools to harness the power of deep learning in your projects and career pursuits.Buy Deep Learning by Aaron Courville, Ian Goodfellow, Yoshua Bengio2. Advances in Financial Machine Learning by Marcos Lopez de Prado"Advances in Financial Machine Learning" by Marcos Lopez de Prado offers a practical and scientifically backed approach to real-world financial challenges. Through math, code, and examples, readers gain valuable insights to implement effective solutions in their contexts. As a trusted expert and portfolio manager, the author equips investment professionals with groundbreaking tools essential for thriving in the dynamic landscape of modern finance.Buy Advances in Financial Machine Learning by Marcos Lopez de Prado3. Machine Learning for Algorithmic Trading by Stefan Jansen"Machine Learning for Algorithmic Trading: Master Systematic Strategies with Python, 2nd Edition" by Stefan Jansen equips traders and investment professionals with end-to-end machine learning techniques. From idea to backtesting, the book covers market, fundamental, and alternative data usage for predictive modelling and strategy design. Readers learn to evaluate alpha factors, optimize portfolios, and implement trading strategies with Python. Suitable for data analysts, Python developers, and traders seeking hands-on machine learning expertise for systematic trading success. Prior Python and ML knowledge is recommended.Buy Machine Learning for Algorithmic Trading by Stefan Jansen4. Python Machine Learning by Sebastian Raschka , Vahid Mirjalili"Python Machine Learning: ML and Deep Learning with scikit-learn" by Raschka and Mirjalili is a comprehensive guide to machine learning techniques, principles and applications. With updated content on TensorFlow 2.0 and new Keras API features, the book delves into cutting-edge reinforcement learning techniques and introduces GANs and sentiment analysis in NLP. Ideal for Python developers and data scientists, this resource-packed book empowers readers to build, train, and evaluate ML models, making it essential for those seeking to harness the power of machine learning and deep learning in real-world projects.Buy Python Machine Learning by Sebastian Raschka , Vahid Mirjalili5. MATHEMATICS FOR MACHINE LEARNING by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong"Mathematics for Machine Learning" by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong is a comprehensive textbook that seamlessly integrates mathematical principles with machine learning techniques and concepts. Suitable for both students with a mathematical background and those new to the subject, the book presents four key machine-learning methods: linear regression, principal component analysis, Gaussian mixture models, and support vector machines. With work examples and exercises in each chapter, readers gain valuable hands-on experience and a solid understanding of applying mathematical concepts in the field of machine learning.Buy MATHEMATICS FOR MACHINE LEARNING by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong6. Introduction to Machine Learning with Python by Andreas Muller"Introduction to Machine Learning with Python: A Guide for Data Scientists" by Andreas Muller provides a comprehensive learning experience. Delve into fundamental concepts and applications of machine learning, understanding the strengths and weaknesses of popular algorithms. Learn data representation and explore advanced techniques for model evaluation and parameter tuning. Discover the power of pipelines for workflow organization. Enhance your skills by mastering text data processing and gain valuable insights to excel in the field of machine learning and data science.Buy Introduction to Machine Learning with Python by Andreas Muller7. Machine Learning For Absolute Beginners by Oliver Theobald"Machine Learning For Absolute Beginners: A Plain English Introduction" by Oliver Theobald offers a practical and beginner-friendly approach to machine learning. While no programming experience is needed, two later chapters introduce Python to demonstrate a machine-learning model. Designed for newcomers, this book lays the foundation for understanding machine learning, but further learning is recommended for mastering this exciting field.Buy Machine Learning For Absolute Beginners by Oliver Theobald8. Machine Learning by Tom M. Mitchell"Machine Learning" by Tom M. Mitchell is a comprehensive textbook catering to advanced undergraduate and graduate students, developers, and researchers interested in machine learning. Without assuming prior knowledge in AI or statistics, the book offers a unified introduction to primary machine learning approaches. It includes accessible algorithms, example datasets, and project-oriented homework assignments accessible through the World Wide Web. A clear, precise, and explanatory writing style ensures a seamless understanding of concepts and techniques from various fields, encompassing recent topics like genetic algorithms, reinforcement learning, and inductive logic programming.Buy Machine Learning by Tom M. Mitchell9. Machine Learning using Python by Manaranjan Pradhan, U Dinesh Kumar"Machine Learning using Python" offers a robust introduction to machine learning with Python libraries, enriched by real-life case studies and examples. It spans essential topics, including Python fundamentals, and descriptive and predictive analytics. Advanced concepts like decision tree learning, random forest, boosting, recommended systems, and text analytics are explored with a balanced focus on theory and practical applications. Through real-world examples and step-by-step guidance, readers gain proficiency in exploring, building, evaluating, and optimizing machine learning models. This book serves as an invaluable resource for learners seeking a solid foundation in machine learning and its Python implementations.Buy Machine Learning using Python by Manaranjan Pradhan, U Dinesh Kumar10. The Hundred-Page Machine Learning Book by Andriy Burkov"The Hundred-Page Machine Learning Book" by Andriy Burkov caters to both beginners and experienced practitioners, making it an excellent resource for anyone seeking to delve into or expand their knowledge of machine learning. It's particularly beneficial for engineers looking to seamlessly integrate ML into their daily tasks without investing excessive time. With concise yet comprehensive content, this book provides valuable insights and practical guidance for all levels of learners in the field of machine learning.Buy The Hundred-Page Machine Learning Book by Andriy BurkovSimilar products for youFAQs related to the best machine learning books1. What are the best machine-learning books?Ans. Deep Learning by Aaron Courville, Ian Goodfellow, Yoshua Bengio, Advances in Financial Machine Learning by Marcos Lopez de Prado, Python Machine Learning by Sebastian Raschka, Vahid Mirjalili, and Machine Learning using Python by Manaranjan Pradhan, U Dinesh Kumar are a few of the best machine learning books.2. What are the 4 basics of machine learning?Ans. The four basics of machine learning are data collection, data preprocessing, model building, and model evaluation. These fundamental steps form the foundation for developing effective machine learning systems.3. Is machine learning better for AI?Ans. Machine learning is a crucial subset of artificial intelligence (AI) that enables systems to learn from data and improve their performance without being explicitly programmed, making it a fundamental aspect of AI.
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Best Machine Learning Books: Inspire Your Technological Journey with Expert Knowledge - Economic Times