Machine learning is a dynamic and rapidly expanding field focused on creating algorithms that empower computers to recognize patterns, make predictions and continually enhance performance. It enables computers to learn from data and experiences, making decisions without explicit programming. For learners, mastering the fundamentals of machine learning opens doors to a world of possibilities to build robust and accurate models. In the ever-evolving landscape of machine learning, datasets play a pivotal role in shaping its future. The field has been revolutionized with the introduction of…mehr
Machine learning is a dynamic and rapidly expanding field focused on creating algorithms that empower computers to recognize patterns, make predictions and continually enhance performance. It enables computers to learn from data and experiences, making decisions without explicit programming. For learners, mastering the fundamentals of machine learning opens doors to a world of possibilities to build robust and accurate models. In the ever-evolving landscape of machine learning, datasets play a pivotal role in shaping its future. The field has been revolutionized with the introduction of oneAPI, which provides a unified programming model across different architectures, including CPUs, GPUs, FPGAs and accelerators, fostering an efficient and portable programming environment. Embracing this unified model empowers practitioners to build efficient and scalable machine learning solutions, marking a significant stride in cross-architecture development. Dive into this fascinating field to master machine learning concepts with the step-by-step approach outlined in this book and contribute to its exciting future.
Akshay B R, a final-year BTech computer science engineering student and an Intel student ambassador, is a passionate data science enthusiast. He primarily works on problems involving disease prediction and developing healthcare systems. He has published multiple articles in a variety of top journals and conferences. He is also a freelance machine learning tutor and has trained around 2000+ students. Sini Raj Pulari is Professor and Tutor currently working at the Government University (Bahrain Polytechnic, Faculty of EDICT) in the Kingdom of Bahrain. She has 16 years of teaching experience at various Indian universities and in industry, contributing to the teaching field and carrying out activities to maintain and develop research and professional activities relevant to computer science engineering. Her research interests include natural language processing, recommender systems, information retrieval, deep learning, and machine learning. She has authored more than 20 Scopus Indexed Publications and co-authored Deep Learning: A Comprehensive Guide (CRC Press/Taylor & Francis). Sini has developed and guided more than 40 undergraduate and postgraduate projects, and she is an active member of boards of curriculum development for various universities. Sini has delivered more than 40 invited lectures on applications and emerging trends in a variety of technological and research advancements. She was a speaker at the workshops AI for All, Understanding Deep Learning Algorithms - Convolution Neural Networks with Real Time Applications, Using Python, Keras and Tensor Flow. She has also participated in the MENA Hackathon group discussion on "innovating tech-based solutions for challenges in the healthcare and energy, environment and sustainability sectors," which was in partnership with Tamkeen, powered by Amazon Web Services (AWS) and Elijah Coaching and Consulting Services. Sini has completed various certifications such as Apple Certified Trainer, SCJP, Oracle Certified Associate, APQMR-Quality Matters, etc. T S Murugesh has 24 years of experience in academia in the fields of analog and digital electronics, automation and control, IoT, system design, image processing, artificial intelligence, machine learning, instrumentation, and computational bio-engineering. After a tenure of 19 years with the Department of Electronics and Instrumentation Engineering, Faculty of Engineering and Technology, Annamalai University, Tamil Nadu, India, he is an Associate Professor in the Department of Electronics and Communication Engineering, Government College of Engineering, Srirangam, Tiruchirappalli, Tamil Nadu, India. He has delivered several talks at international conferences and given more than two dozen invited lectures at the national level at various institutions, including Sastra University, Annamalai University, Mahatma Gandhi University, Kerala, National Institute of Technology, Tiruchirappalli, etc. He has 50+ peer-reviewed indexed papers in journals, including Springer, Springer Nature, Elsevier, Wiley, Inderscience, etc. He has organized faculty development programs at the national level, and he is a reviewer for IEEE, Inderscience and many other peer-reviewed journals. He has coauthored five books for CRC Press/Taylor & Francis (UK) and is currently co-authoring a book for Nova Science Publishers, USA and two books for CRC Press. Dr. Murugesh has donned the role of Mentor, Primary Evaluator for the Government of India's Smart India Hackathon 2022, Toycathon2021, Judge in the Grand Finale in Toycathon 2021, evaluator in The Kavach2023 Cybersecurity Hackathon, organized by the Ministry of Home Affairs (MHA) in collaboration with the Ministry of Education's (MoE) Innovation Cell, Government of India. He is a hackathon enthusiast, and his team has won first prize in the CloudFest Hackathon 2 presented by Google Cloud, DigitalGov Hack, the Hackathon by WSIS Forum 2023 and Digital Government Authority, Saudi Arabia. His team has also won the MSME Idea Hackathon 2.0 and received 15 Lakhs funding from the Ministry of Micro, Small and Medium Enterprises (MSME) Innovative Scheme, Government of India, as well as the Second Prize in the IFGxTA Hub Hackathon 2022. Dr. Murugesh is a certified Intel oneAPI Innovator, Mentor under the National Initiative for Technical Teachers Training programme from AICTE, and the National Institute of Technical Teachers Training and Research, and a Certified Microsoft Educator Academy Professional. He is also a Master Assessor for a Naan Mudhalvan Program, 2023, devised by the Government of Tamil Nadu, a reviewer of BE/BTech technical books in the regional language scheme of AICTE, coordinated by the Centre for Development of Tamil in Engineering and Technology, Anna University, Tamil Nadu, India. Huawei has recognized Dr. Murugesh for his academic collaboration. He is a Conference Committee Member, Publishing Committee Member of the International Association of Applied Science and Technology. He holds editorial board membership with the American Journal of Embedded Systems and Applications. He has also served as Technical Program Committee Member for a Springer-sponsored, Scopus-Indexed International Conference conducted at Sharda University, India, and he is a Scientific Committee Member for an International Conference conducted at the Sultanate of Oman and a chairperson of an International Hybrid Conference at Mahatma Gandhi University Kerala, India. Dr. Murugesh has held various academic responsibilities, such as Chairman for Anna University Central Valuation, Chief Superintendent for the Anna University Theory Examinations, and the Exam Cell Coordinator for his institution. He also holds professional body membership in the Institution of Engineers (India). Shriram K Vasudevan has more than 17 years of experience in industry and academia. He earned a PhD in embedded systems. He has authored or co-authored 45 books for various publishers, including Taylor & Francis, Oxford University Press, and Wiley. He also has been granted 13 patents so far. Shriram is a hackathon enthusiast and has been awarded by Harvard University, AICITE, CII, Google, TDRA Dubai, the Government of Saudi Arabia, the Government of India, and many more. He has published more than 150 research articles. He was associated with L&T Technology Services before joining Intel in a current role. Dr. Vasudevan operates a YouTube channel in his name, which has more than 41,000 subscribers and maintains a wide range of playlists on varied topics. He is a public speaker as well. Dr. Vasudevan is a oneAPI-Certified Instructor, Intel oneAPI-Certified Instructor, Google Cloud Ambassador, Streamlit Education Ambassador, AWS Ambassador, ACM Distinguished Speaker and NASSCOM Prime Ambassador. He is a Fellow IEI, Fellow IETE and Senior Member IEEE.
Inhaltsangabe
Introduction: What is Machine Learning? 1. Exploring the Iris dataset. 2. Heart failure prediction with oneAPI. 3. Handling water quality dataset. 4. Breast cancer classification with hybrid ML models. 5. Flower recognition with Kaggle dataset and Gradio interface. 6. Drug classification with hyperparameter tuning. 7. Evaluating model performance: Metrics for diabetes prediction. 8. Parkinson's disease detection: An overview with feature engineering and outlier analysis. 9. Sonar mines vs. rock prediction using ensemble learning. 10. Bankruptcy risk prediction. 11. Hotel reservation prediction. 12. Crop recommendation prediction. 13. Brain tumor classification. 14. Exploratory data analysis and classification on wine quality dataset with oneAPI. 15. Cats vs. Dogs classification using deep learning models optimized with oneAPI. 16. Maximizing placement predictions with outlier removal. 17. A deep dive into Mushroom classification with oneAPI. 18. Smart healthcare - Machine learning approaches for kidney disease prediction with oneAPI. 19. A deep dive into multiclass flower classification with ResNet and VGG16 using oneAPI. 20. Dive into X (formerly Twitter's) emotions using oneAPI - Sentiment analysis with NLP.
Introduction: What is Machine Learning? 1. Exploring the Iris dataset. 2. Heart failure prediction with oneAPI. 3. Handling water quality dataset. 4. Breast cancer classification with hybrid ML models. 5. Flower recognition with Kaggle dataset and Gradio interface. 6. Drug classification with hyperparameter tuning. 7. Evaluating model performance: Metrics for diabetes prediction. 8. Parkinson's disease detection: An overview with feature engineering and outlier analysis. 9. Sonar mines vs. rock prediction using ensemble learning. 10. Bankruptcy risk prediction. 11. Hotel reservation prediction. 12. Crop recommendation prediction. 13. Brain tumor classification. 14. Exploratory data analysis and classification on wine quality dataset with oneAPI. 15. Cats vs. Dogs classification using deep learning models optimized with oneAPI. 16. Maximizing placement predictions with outlier removal. 17. A deep dive into Mushroom classification with oneAPI. 18. Smart healthcare - Machine learning approaches for kidney disease prediction with oneAPI. 19. A deep dive into multiclass flower classification with ResNet and VGG16 using oneAPI. 20. Dive into X (formerly Twitter's) emotions using oneAPI - Sentiment analysis with NLP.
Introduction: What is Machine Learning? 1. Exploring the Iris dataset. 2. Heart failure prediction with oneAPI. 3. Handling water quality dataset. 4. Breast cancer classification with hybrid ML models. 5. Flower recognition with Kaggle dataset and Gradio interface. 6. Drug classification with hyperparameter tuning. 7. Evaluating model performance: Metrics for diabetes prediction. 8. Parkinson's disease detection: An overview with feature engineering and outlier analysis. 9. Sonar mines vs. rock prediction using ensemble learning. 10. Bankruptcy risk prediction. 11. Hotel reservation prediction. 12. Crop recommendation prediction. 13. Brain tumor classification. 14. Exploratory data analysis and classification on wine quality dataset with oneAPI. 15. Cats vs. Dogs classification using deep learning models optimized with oneAPI. 16. Maximizing placement predictions with outlier removal. 17. A deep dive into Mushroom classification with oneAPI. 18. Smart healthcare - Machine learning approaches for kidney disease prediction with oneAPI. 19. A deep dive into multiclass flower classification with ResNet and VGG16 using oneAPI. 20. Dive into X (formerly Twitter's) emotions using oneAPI - Sentiment analysis with NLP.
Introduction: What is Machine Learning? 1. Exploring the Iris dataset. 2. Heart failure prediction with oneAPI. 3. Handling water quality dataset. 4. Breast cancer classification with hybrid ML models. 5. Flower recognition with Kaggle dataset and Gradio interface. 6. Drug classification with hyperparameter tuning. 7. Evaluating model performance: Metrics for diabetes prediction. 8. Parkinson's disease detection: An overview with feature engineering and outlier analysis. 9. Sonar mines vs. rock prediction using ensemble learning. 10. Bankruptcy risk prediction. 11. Hotel reservation prediction. 12. Crop recommendation prediction. 13. Brain tumor classification. 14. Exploratory data analysis and classification on wine quality dataset with oneAPI. 15. Cats vs. Dogs classification using deep learning models optimized with oneAPI. 16. Maximizing placement predictions with outlier removal. 17. A deep dive into Mushroom classification with oneAPI. 18. Smart healthcare - Machine learning approaches for kidney disease prediction with oneAPI. 19. A deep dive into multiclass flower classification with ResNet and VGG16 using oneAPI. 20. Dive into X (formerly Twitter's) emotions using oneAPI - Sentiment analysis with NLP.
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