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(Free course coupon) What is signal processing, and why should you care? Signal processing is at the heart of countless modern technologies -- audio and image filtering, data compression, communication systems, time series analysis, neuroscience, machine learning, and so much more. If a system involves signals, chances are signal processing is behind it. But mastering signal processing must be, like, really complicated, right? Not necessarily! With the right approach, it can be intuitive, practical, and even fun. In my 9-hour video course on digital signal processing (DSP), you’ll learn the key concepts and practical tools to analyze and manipulate signals. We’ll cover everything from filters to Fourier transforms to time-frequency analysis -- all with clear explanations, MATLAB and Python code, and real-world examples. And yes, there’s math involved, but it’s explained in a way that makes sense for practical applications. The first 100 people to enroll using the link below will pay exactly zero units of any currency. Missed the free coupon? No worries -- you can find max-discount coupons to all my courses and textbooks at sincxpress.com. https://lnkd.in/eSzE2Hqe sincxpress.com

(Free course coupon) Who or what or when is the Fourier transform? The Fourier transform is one of the single most important operations in signal processing, time series analysis, image processing, data compression, anything related to music and audio filtering, and lots more applications in engineering, physics, neuroscience, finance, etc. So, the Fourier transform must be, like, reeeaaallllly hard to understand and implement, right? Wrong! It's actually pretty straightforward, but only if it's explained clearly... In my 7-video-hour course on the Fourier transform, you'll learn the most important foundational, conceptual, and implementational aspects of the Fourier transform and spectral analysis. In MATLAB and Python. There are lots of clear explanations, diagrams, simulations, examples, and problemsets to help you learn. Yeah, there's math in there as well, but you won't be solving abstract integration problems by hand; you'll be learning about numerical details that are relevant to real-world applications. Come check it out! The first 100 people to enroll using the link below will pay exactly zero units of any currency. If you're too late to get that coupon, then don't worry -- you can visit my website sincxpress.com to find max-discount coupons to all of my online courses, as well as my textbooks. https://lnkd.in/eTT3e6UW sincxpress.com

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MIT University has released free online courses. Here are their most popular courses to learn new skills in 2024 1. Introduction to Computer Science and Programming Using Python - https://lnkd.in/e6hEBbAw An introduction to computer science as a tool to solve real-world analytical problems using Python 3.5. 2. Introduction to Computational Thinking and Data Science - https://lnkd.in/emMUJ8_P An introduction to using computation to understand real-world phenomena. 3. Machine Learning with Python - https://lnkd.in/eJsi2vg7 An in-depth introduction to the field of machine learning through hands-on Python projects. 4. Supply Chain Analytics - https://lnkd.in/eJztxKuN Master and apply the core methodologies used in supply chain analysis and modeling, including statistics, regression, optimization and probability. 5. Understanding the World Through Data - https://lnkd.in/es55EKsW Become a data explorer – learn how to leverage data and basic machine learning algorithms to understand the world. 6. Becoming an Entrepreneur - https://lnkd.in/eCqW8r_T Learn the business skills and startup mindset needed to embark on your entrepreneurial path. 7. Shaping Work of the Future - https://lnkd.in/eKfCBNa7 Explore ways to improve job opportunities and develop a personal plan for lifelong career success. 8. Foundations of Modern Finance I - https://lnkd.in/eAv6HG8u A mathematically rigorous framework to understand financial markets delivered with data-driven insights from MIT professors. 9. Probability - The Science of Uncertainty and Data - https://lnkd.in/ecju9zwq Build foundational knowledge of data science with this introduction to probabilistic models. 10. Introduction to Biology - The Secret of Life - https://lnkd.in/eiVVvZTq Explore the secret of life through the basics of biochemistry, genetics, molecular biology, recombinant DNA, genomics and rational medicine. ➡ Get tutorials and guides on using popular AI tools for FREE here: https://lnkd.in/ewyJYCPx #ai #artificialintelligence #innovation #technology #tech #productivity #learning #freecourses

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I am glad to complete the specialization "Mathematics for Machine Learning and Data Science", from DeepLearning.AI on Coursera. It has 3 courses on: - Linear Algebra (4 weeks). - Calculus (3 weeks). - Probability and Statistics (4 weeks). Link to my certificate: https://lnkd.in/dr_MQF8j Even in difficult or busy times, it was somehow calming for me to take online courses and learn. It was a good revision of theoretical concepts, and useful practice of Python programming implementing the concepts in online Jupyter notebooks. The instructor, Dr Luis Serrano, explained everything in an easy to understand way. All these mathematical concepts form an important foundation for machine learning / artificial intelligence and data science. I am eager to keep learning, improving, and applying my skills. #MachineLearning #ML #DataScience #AI #Python #ArtificialIntelligence #Programming #Mathematics #LinearAlgebra #Calculus #Probability #Statistics #Computing #Coding #TechIndustry #Coursera #DeepLearning #ComputerScience #Technology #Engineering

Just wrapped up Module 1 of my course "Mathematical, Numerical, Bayesian, and Causal Methods in Materials Science"! 🚀 In just a month, we've journeyed from Python basics to the intricate relationship between idealizations of numbers and functions and their practical numerical implementation in Python. 🐍✨ We've dived deep into how the dimensionality and differentiability of space and function dictate the solvability of problems, illuminating why there's always a future in ML for drug discovery. The exploration doesn't stop at theory; we've hands-on learned to fit functions in Python, unraveling the power of genetic algorithms and symbolic regression along the way. 🧬🔢 And there is always a question of how do new functions created and connect to fundamental physics. The culmination of this intense month? A final homework using Miles Cranmer's PySR (special thanks to Miles for instant help with installation after update) to explore the physics of the Ising model. It's been a journey of connecting mathematical concepts with real-world applications in materials science, setting a strong foundation for the modules to come. Going through the course, I am still amazed at how fast methods that only a decade ago were used for PhD-level research or beyond can be adopted and used in the classroom. ML and open code truly change the way we do science. It's fascinating to see these advanced tools becoming accessible to a wider audience, significantly accelerating the pace of discovery and learning. I also feel more confident that the idea of massive online open courses (MOOCs) is useful, but it's never going to substitute in-class education. There has to be a human in the loop if we are teaching humans. And the way to do it is through sharing open course materials. It's less glorious than MOOCs - since your contribution might become a footnote on a slide or notebook at best - but ultimately, this is the right way to go. Sharing knowledge openly fosters a collaborative environment that benefits everyone involved, from seasoned researchers to students just starting their journey in science. In my course, I owe a lot to materials by Volodymyr Vovchenko and hope that my materials including lectures and Colabs will benefit others: https://lnkd.in/eqMuHWzV To all my students, your engagement and curiosity have made this journey enriching. Here's to the next chapter, where we'll build on this foundation to delve into the world of differential equations and PINNs!. 📘🔭 #MaterialsScience #MachineLearning #PythonProgramming #Mathematics #BayesianMethods #GeneticAlgorithms #SymbolicRegression #Education

Although dubbed CT 2.0, we can’t get rid of 1.0 (yet). With the development of ML, AI and a whole lot of data -Computational Thinking skills need a bit more depth. Check out the latest article from Teaching Python. https://lnkd.in/egeW4AYY