Windows Privilege Escalation Penetration Testing - Part I

'SIMULINK : Beginner Course on MATLAB/SIMULINK'.This course gives a very small entry to MATLAB Software with Coding Interface and a broad Beginning for SIMULINK Interface of MATLAB Software, building great confidence to Design, Develop and simulate any model for any systems.This course is broadly presented in three parts viz,MATLAB Software Interface and coding Introduction.Introduction to SIMULINK Interface.Designing, Developing and Simulating few Applications (PID, Fuzzy Logic Control, 1DOF-2DOF vibration Models, Electric Motor) in the SIMULINK.Each section of the course curriculum consists of theory, actual MATLAB hands-on, an Exercise based on the learning from the respective section with its Solution, and Multiple Choice Questions to give confidence about the learning.Section 1: WELCOME TO THE COURSEIn this Section, Instructor has Welcome the learners with bio, and what learner will be able to at the end of the course.Section 2: INTRODUCTIONIn this Section, an Introduction to the MATLAB Software is given. Default windows that appear in the first start of software are well explained. Also, the Default setting necessary for ease of working and starting with software is demonstrated.Section 3: MATLAB BASIC CODINGIn this Section, basic coding in the Command Window and coding in MATLAB coding file .m files are done and explained with simple examples.Section 4: STARTING WITH SIMULINKIn this Section, Start with the SIMULINK Interface, Windows associated with Simulink Interface, Opening-Saving-Closing of Simulink, with small demonstration is very well explained.Section 5: SIMULINK COMMONLY USED BLOCKSFrom this Section, we start with a complete SIMULINK Interface, here the Simulink blocks such as Constant block, Display block, Gain block, Scope, Floating Scope, Dashb

This course is the fifth part of the lecture series on ANSYS Workbench software. It takes you through various modules of ANSYS Workbench like Design in ANSYS Workbench, Static Structural, Steady-State Thermal, Thermal-Structural Coupled, Modal, Buckling, Transient Thermal and Topology Optimization Analysis. Various numerical are solved to explain various concepts and features of the software and for practice so that expertise can be gained in handling the software. We start with Design in ANSYS Workbench which explains how to draw basic geometry using Design Modeler.Second section deals with Static Structural Analysis. Here we analyze in ANSYS Workbench like Exhaust Valve, Wash Basin, Disc Brake Rotor Design I and II, Piston Head, Piston Ring, Snap Ring, Piston Assembly and Gudgeon Pin. Third section is all about the Steady-State Thermal Analysis. Various numericals are solved for like Exhaust Valve, Wash Basin, Disc Brake Rotor Design I and II, Piston Head, Piston Ring, Connecting Rod, Snap Ring, Piston Assembly and Gudgeon Pin.Fourth section deals with Thermal-Structural Coupled Analysis of Disc Brake Rotor, Piston Head, Snap Ring and Piston Assembly. The procedure for solving such type of problem is explained.Fifth section deals with Modal Analysis of Exhaust Valve, Wash Basin, Disc Brake Rotor Design I and II, Piston Head, Piston Ring, Snap Ring, Connecting Rod and Gudgeon Pin. Sixth section deals with Buckling Analysis of Connecting Rod.Seventh section deals with Transient Thermal Analysis of Disc Brake Rotor, Piston Head and Gudgeon Pin.Last section deals with Topology Optimisation of Disc Brake Rotor.Basic version of this course is available in the previous part of this series ANSYS Workbench Tutorials. Advanced version of the courses are also available namely, Advanced Structu

Top 10 data analytics course using matplotlib 2022, Top 10 data visualization course using matplotlib 2022, Matplotlib 2022The data analytics is the process of finding insights of the data. It involves following important steps,1. Collection of relevant data2. Preprocessing and transforming data3. Plotting data using different types of graphs4. Understanding insight of the dataWe can plot data in different types of plots using matplotlib library. Matplotlib is a cross-platform, data visualization and graphical plotting library for Python and its numerical extension NumPy. It along with python numpy package provides open source alternative to MATLAB. Developers can use matplotlib library for plotting graphs. Also they can use matplotlib’s APIs (Application Programming Interfaces) to embed plots in GUI based applications. In this course you are going to learn details of matplotlib library. The content of this course is as follows,Chapter 1: Introduction to MatPlotLibA. What is Matplotlib?B. Pyploy APIC. PyLab ModuleD. Simple PlotChapter 2: Object Oriented MatplotlibA. Object oriented interfaceB. Figure classC. Axes classD. TransformsChapter 3: Multiple PlotsA. MultiplotsB. Subplots functionC. Subplot2grid functionChapter 4: Formatting PlotsA. GridsB. Formatting axesC. Setting limitsD. Setting ticks and tick labelsE. Twin axesChapter 5: Types of PlotsA. Bar plotB. Stacked bar chartC. HistogramD. Pie chartE. Scatter plotF. Contour plotG. Quiver plot

Time-to-application is number one goal of this course -- After the course you can directly start optimizing using a ready-to-use Python based Genetic Algorithm Tool!No time consuming "tool development from scratch" -- we work with ready-to-use, flexible Genetic Algorithm Optimization Tool written in the most basic Python and you get the tool at the end of the courseFor all the "Beginners" who want to be real-world users in the most effective way possibleNo advanced Python or programming skills needed -- Most basic Python is used for the whole algorithm -- Python lists and Numpy arrays thats it!Designed for Beginners who don't want to program the algorithm or spent a lot of time to transfer someones hard-coded program to their individual optimisation problemsComplete Beginners Guide to Genetic Algorithm OptimizationDo you want to learn about a very powerful optimization method that is used for many optimization problems such as neural networks, engineering, logistics, finance and many more? Do you want to avoid a ton of theory without practical application or very specific code snippets that are not really transferable to your individual optimization problems? If so, this course will help you enhance your optimization skills with:- Genetic Algorithm based Optimization- A ready-to-use python based simple but flexible Genetic Algorithm Based OptimizerGenetic algorithm based optimization is a metaheuristic optimization method for a large specturm of optimization problems with multiple design parameters (multi-parameter). Compared to other optimization methods it is more stable against local extrema and offers great flexibility. This course is desig

This course covers the concepts of Penetrant (also called Liquid or Dye Penetrant) Testing, an NDT method used to detect cracks and cavities open on the surface of nonporous metallic materials. Liquid Penetrant Testing is one of the simplest and most popular NDT methods for inspection.In this course, we will cover the required concepts around penetrant testing training necessary for completion of the PT certification program. This is essential for ensuring compliance as per NAS-410, SNT-TC-1A and NavSea Technical Publication T9074-AS-GIB-010/271.The training is divided into eight (8) chapters.Penetrant history, capillary action and wetting, cohesion and adhesion, and the methods of liquid penetrant testingSurface preparation; pre-cleaning; and the various liquid penetrant systemsSafety; LOX materials; penetrant application techniques; penetrant dwell times; temperature limits; excess penetrant removal techniques and over cleaning precautions; advantages and disadvantages of each removal technique; developer types and applicationInterpretation and evaluation; false, non-relevant and relevant indicationsQuality Control test; Tam panel; Comparator BlockTerminology; product discontinuities by product form such as casting, forgings etc.; discussion of methods to make the different product forms and weldingSystem classifications by Type, Method, Developer forms; penetrant terms such as wetting ability defined; procedure requirementsTypical qualification and certification documents; SNT-TC-1A overviewThis course content conforms to the training subjects listed in ANSI/ASNT CP-105, Standard Topical Outlines for Qualification of Nondestructive Testing Personnel. This training will meet most certification document for liquid penetrant testing such as SNT-TC-1A. It encompasses the subject matter listed in the ANSI/ASNT CP 105 outline for liquid penetrant ce

The course "Principles of Data Science and Machine Learning for Natural Sciences" is designed to connect traditional scientific disciplines with the rapidly growing fields of Data Science (DS) and Machine Learning (ML). As research increasingly depends on large datasets and advanced computational methods, it’s becoming essential for scientists to know how to leverage DS and ML techniques to improve their work.This course offers a solid introduction to the key concepts of Data Science and Machine Learning, specifically aimed at scientists and researchers in areas like biology, chemistry, physics, and environmental science. Participants will learn the basics of data analysis, including data collection, cleaning, and visualization, before moving on to machine learning algorithms that can help identify patterns and make predictions from data.The course doesn’t require any programming skills and focuses on fundamental theoretical concepts. It's structured into six main sections:1. Introduction We'll start by introducing the course, covering its main features, content, and how to follow along.2. Core DS/ML Concepts We’ll go over basic concepts like variables, data scaling, training, datasets, and data visualization.3. Classification In this section, we’ll discuss key classification algorithms such as decision trees, random forests, Naive Bayes, and KNN, with examples of how they can be applied in scientific research.4. Regression We’ll briefly cover linear and multiple linear regression, discussing the main ideas and providing examples relevant to science.5. Clustering This section will focus on standard and hierarchical clustering methods, along with practical examples for scientific applications.6. Neural Networks Finally, we’ll introduce neural