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MOOC
Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision making process is either lost or not maximized at all too often, we don't have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand.
This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand.
Accordingly, in this course, you will learn:
- The major steps involved in tackling a data science problem.
- The major steps involved in practicing data science, from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment.
- How data scientists think!
- Course related:
- LGT6801 Guided Study in Logistics I, LGT6202: Stochastic Models and Decision under Uncertainty, LGT6802 Guided Study in Logistics II, and LGT6803: Guided Study in Logistics III
- Subjects:
- Business Information Technology and Computing
- Keywords:
- Electronic data processing Data mining Problem solving
- Resource Type:
- MOOC
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MOOC
This course covers the fundamentals of advanced fluid mechanics: including its connections to continuum mechanics more broadly, hydrostatics, buoyancy and rigid body accelerations, inviscid flow, and the application of Bernoulli’s theorems, as well as applications of control volume analysis for more complex fluid flow problems of engineering interest. This course features lecture and demo videos, lecture concept checks, practice problems, and extensive problem sets.
This course is the first of a three-course sequence in incompressible fluid mechanics: Advanced Fluid Mechanics: Fundamentals, Advanced Fluid Mechanics: The Navier-Stokes Equations for Viscous Flows, and Advanced Fluid Mechanics: Potential Flows, Lift, Circulation & Boundary Layers. The series is based on material in MIT’s class 2.25 Advanced Fluid Mechanics, one of the most popular first-year graduate classes in MIT’s Mechanical Engineering Department. This series is designed to help people gain the ability to apply the governing equations, the principles of dimensional analysis and scaling theory to develop physically-based, approximate models of complex fluid physics phenomena. People who complete these three consecutive courses will be able to apply their knowledge to analyze and break down complex problems they may encounter in industrial and academic research settings.
`The material is of relevance to engineers and scientists across a wide range of mechanical chemical and process industries who must understand, analyze and optimize flow processes and fluids handling problems. Applications are drawn from hydraulics, aero & hydrodynamics as well as the chemical process industries.
- Subjects:
- Mechanical Engineering
- Keywords:
- Fluid mechanics
- Resource Type:
- MOOC
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Others
Fuzzy Logic Toolbox™ provides MATLAB® functions, apps, and a Simulink® block for analyzing, designing, and simulating systems based on fuzzy logic. The product guides you through the steps of designing fuzzy inference systems. Functions are provided for many common methods, including fuzzy clustering and adaptive neurofuzzy learning. The toolbox lets you model complex system behaviors using simple logic rules, and then implement these rules in a fuzzy inference system. You can use it as a stand-alone fuzzy inference engine. Alternatively, you can use fuzzy inference blocks in Simulink and simulate the fuzzy systems within a comprehensive model of the entire dynamic system.
- Course related:
- SD5804 Design Research Methods
- Subjects:
- Computing and Mathematics and Statistics
- Keywords:
- Fuzzy systems Fuzzy logic
- Resource Type:
- Others
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Others
Agent based modeling focuses on the individual active components of a system. This is in contrast to both the more abstract system dynamics approach, and the process-focused discrete event method. With agent based modeling, active entities, known as agents, must be identified and their behavior defined. They may be people, households, vehicles, equipment, products, or companies, whatever is relevant to the system. Connections between them are established, environmental variables set, and simulations run. The global dynamics of the system then emerge from the interactions of the many individual behaviors. AnyLogic combines professional discrete event, system dynamics, and agent based modeling in one platform for efficient, no compromise results. In our white paper, Multimethod Simulation Modeling for Business Applications, we investigate these three main simulation modeling approaches and construct a multimethod model example to illustrate the advantages of multimethod simulation modeling. Read the white paper and see why hybrid models are always a better choice!
- Course related:
- CE631 Simulation and IT Applications in Construction
- Subjects:
- Business Information Technology and Computing
- Keywords:
- Computer simulation Multiagent systems System analysis -- Data processing
- Resource Type:
- Others
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Video
This channel contains a complete list of physics videos, as well as hundreds of chemistry, astronomy, math, and mechanical engineering videos. The physics videos explain the fundamental concepts of physics with some easy to follow examples on how to solve physics problems. The chemistry videos cover all the basic topics of chemistry, the astronomy videos explain the wonders of Earth and our Universe, and the math videos cover many topics in algebra, trigonometry, pre-calculus, calculus and differential equations.
- Subjects:
- Chemistry, Mathematics and Statistics, Cosmology and Astronomy, Physics, Mechanical Engineering, and Electrical Engineering
- Keywords:
- Chemistry Astronomy Electrical engineering Physics Mathematics Mechanical engineering Kalman filtering
- Resource Type:
- Video
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Video
This electronics video tutorial provides a basic introduction into the node voltage method of analyzing circuits. It contains circuits with voltage and current sources. It explains how to determine every current in a resistor as well as the electric potential of all nodal points in the circuit.
- Course related:
- AMA2111 Mathematics I
- Subjects:
- Electronic and Information Engineering
- Keywords:
- Electric circuits Electric network analysis
- Resource Type:
- Video
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Others
Learning cyber security on TryHackMe is fun and addictive. Earn points by answering questions, taking on challenges and maintain your hacking streak through short lessons. Learn by following a structured paths and reinforce your skills in a real-world environment by completing guided, objective-based tasks and challenges.
- Subjects:
- Computing
- Keywords:
- Computer security
- Resource Type:
- Others
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MOOC
This course explores the topic of solid objects subjected to stress and strain. The methods taught in the course are used to predict the response of engineering structures to various types of loading, and to analyze the vulnerability of these structures to various failure modes. Axial loading with be the focus in this course.
- Course related:
- ME3303 Mechanics of Solids
- Subjects:
- Mechanical Engineering
- Keywords:
- Strength of materials Mechanics Applied Materials
- Resource Type:
- MOOC
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Video
Welcome to Zero to Hero for Natural Language Processing using TensorFlow! If you’re not an expert on AI or ML, don’t worry -- we’re taking the concepts of NLP and teaching them from first principles with our host Laurence Moroney.
- Subjects:
- Computing
- Keywords:
- Machine learning Natural language processing (Computer science)
- Resource Type:
- Video
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Others
Colaboratory, or "Colab" for short, allows you to write and execute Python in your browser, with
(1) Zero configuration required
(2) Free access to GPUs
(3) Easy sharing
Whether you're a student, a data scientist or an AI researcher, Colab can make your work easier. Watch Introduction to Colab to learn (https://www.youtube.com/watch?v=inN8seMm7UI) more, or just get started below!
- Course related:
- COMP3011 Machine Learning and Data Analytics
- Subjects:
- Computing
- Keywords:
- Computer programming Computer science Python (Computer program language)
- Resource Type:
- Others