I recently completed the Machine Learning with Python course offered by IBM SkillsBuild.
The Machine Learning with Python course by IBM on SkillBuild is designed to provide a comprehensive introduction to machine learning using Python. Here are some key highlights:
- Foundational Concepts: The course begins with an introduction to machine learning, covering the basics of supervised and unsupervised learning, as well as linear and non-linear regression.
- Classification Techniques: You will learn about various classification algorithms, including K-Nearest Neighbors (KNN), decision trees, and logistic regression.
- Clustering Methods: The course covers different clustering techniques such as k-means, hierarchical clustering, and DBSCAN.
- Hands-On Learning: Emphasis is placed on practical experience, using Python libraries like SciPy and scikit-learn. You will apply your knowledge through labs and projects.
- Final Project: By the end of the course, you will build, evaluate, and compare several machine learning models using different algorithms.
- Career Skills: Completing the course will equip you with job-ready skills and a certificate to showcase your competency in machine learning
This course is suitable for those looking to advance their data science career or get started in machine learning and deep learning.
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🎉I am thrilled to announce that I have been included in the prestigious list of the top 2% most influential researchers globally for the 6th year in a row. This esteemed recognition, compiled by scientists at Stanford University, USA, was published by Elsevier on Sept. 16, 2024.
I extend my heartfelt gratitude to all my co-authors, both within and outside the University of Peloponnese, for their unwavering dedication, effort, and hard work, which have been instrumental in achieving this milestone. Thank you for being an essential part of this journey! 🙏✨
Ioannidis, John P.A. (2024), “August 2024 data-update for "Updated science-wide author databases of standardized citation indicators"”, Elsevier Data Repository, V7, doi: 10.17632/btchxktzyw.7
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An article titled Enhancing 5G Performance: A Standalone System Platform with Customizable Features has been published in the AEU - International Journal of Electronics and Communications.
Congratulations to my PhD student Emmanouil-Zafeirios Bozis and all my co-authors, Michael Batistatos, George Xilouris, Akis Kourtis, and Tasos Kourtis.
The paper details the experimental outcomes of the integrated 5G platform utilizing SDR hardware and the most recent OpenAir Interface software, focusing on the key performance indicators of bandwidth and latency.
Here is a brief summary:
- Flexible 5G Platform: The paper proposes a standalone 5G system platform based on Open Air Interface software, which is fully reprogrammable and customizable.
- Improved Usability: The platform uses Linux shell scripts to simplify the implementation of different connectivity scenarios, speeding up the process.
- Performance Metrics: Achieved a mean Round Trip Time (RTT) latency of 6.78 ms with a 2 ms DL/UL Transmission periodicity, suitable for low-latency applications.
- Future Work: Plans to expand the system capabilities with additional hardware and software to test MIMO transmission and wider bandwidth parts.
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It's important for everyone to stay informed about the latest technological innovations.
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I recently completed the Artificial Intelligence Fundamentals course offered by IBM SkillsBuild. This comprehensive program covered essential AI concepts, including natural language processing, computer vision, machine learning, deep learning, chatbots, and neural networks. Additionally, it delved into AI ethics and practical applications of AI in various fields. The course provided hands-on experience with IBM Watson Studio, allowing me to build and test AI models. Earning this certification has significantly enhanced my understanding of AI and its potential, preparing me for future opportunities in the rapidly evolving AI landscape.
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