The Nest of Engineers

[Language: English - Number of Lessons: 87 - Duration: 6 hours and 41 minutes - Size: 831 MB] ... Within days you can master some of the most complex data analysis techniques available in SPSS software. Even if you are not a professional mathematician or statistician, you will understand these techniques well and be able to apply them in practical and real situations.

[Language: English - Number of Lessons: 9 - Duration: 2 hours and 27 minutes - Size: 1.36 GB] ... Advanced Earthquake Analysis Using PLAXIS 3D is an earthquake analysis and design training course published by Udemy Online Academy. Advanced Earthquake Analysis Using PLAXIS 3D is an intensive course for civil and geotechnical engineers looking to enhance their skills in seismic analysis and design. This course focuses on the application of PLAXIS 3D software for modeling and analyzing the effects of earthquakes on soil-structure interactions. Students will gain an in-depth understanding of dynamic analysis methods, including time history analysis and response spectrum analysis, as well as principles of soil liquefaction and ground motion propagation.

The key points of this training course include understanding the principles of earthquake engineering, mastering PLAXIS 3D for dynamic analysis, modeling soil and structure interactions under seismic loading, implementing response spectrum and time history analysis, assessing soil liquefaction potential, motion propagation analysis. Land and use the results for optimization. The curriculum includes detailed instructions on setting up complex models that simulate real-world earthquake scenarios, evaluate the behavior of various structures under seismic loads, and interpret the results to inform engineering decisions. By the end of the course, individuals will be proficient in performing advanced earthquake analysis and ensuring that structures are designed to effectively withstand seismic events.

What you will learn in Advanced Earthquake Analysis Using PLAXIS 3D: Creating a 3D numerical model including pile, pile cap, bridge support and approach + Calibration of different earthquake time histories to determine the most suitable signal + Using the design time history in the created 3D model to determine the forces in the foundation piles at different time stages + Implementation of Python scripting to extract forces in piles through automation + and…

[Language: English - Number of Lessons: 12 - Duration: 2 hours and 16 minutes - Size: 1.366 GB] ... Geotechnical analyzes of retaining walls in Plaxis 2D is a training course on the analysis and design of retaining walls using PLAXIS 2D software, which was published by the online academy of Udemy. Geotechnical analyzes of retaining walls in Plaxis 2D is a specialized course aimed at civil and geotechnical engineers who want to deepen their understanding of the analysis and design of retaining walls using PLAXIS 2D software. This course provides an overview of retaining wall behavior, including principles of earth pressure theory and stability considerations. Students will learn to model different types of retaining walls, analyze different loading conditions, and evaluate the effects of soil properties on wall performance. The curriculum includes step-by-step guidance in using PLAXIS 2D for effective geotechnical analyses, including real-world case studies and hands-on exercises.

The key points of this training course include understanding the mechanics of retaining walls and earth pressures, mastering the PLAXIS 2D software interface for geotechnical analysis, modeling the types and configurations of retaining walls, analyzing stability under different loading scenarios, evaluating the effects of soil and structure interaction, Perform limit equilibrium analyses, and interpret results for engineering design and decision making. At the end of the course, individuals will be equipped with the skills to perform full geotechnical analyzes of retaining walls and ensure safe and efficient design practices.

What you will learn in Geotechnical analyses of retaining walls in Plaxis 2D: Real examples of retaining wall analysis + Making 2D Plaxis models + Calculation of material properties of steel sheet piles, walls and concrete slabs, ground anchors and foundations + Analysis of Plaxis results + Optimization of the parameters of retaining walls and ground anchors based on the results of Plaxis analysis and…

Language: English - Level: Beginner - Number of Lessons: 78 - Duration: 5 hours and 26 minutes.

Statistics for Business Analytics: Data Analysis with Excel is a training course on the importance of statistics in business and business data analysis in Excel software, published by Udemy Academy. Statistical modeling is a very important skill for data analysts, and in this training course you will practice this skill with Excel software. Today, data has included all parts of our lives, and the success of business and various decisions in various industries depends on access to appropriate data and their correct analysis. The world is moving day by day towards a purely data-centric direction, and in this direction, many job positions have been created for data engineers and analysts.

This course is completely comprehensive and includes all the details of statistical modeling and business analysis. What you will learn?: Data Analysis with Excel:Basic principles and basics of statistics and their application in the world of business and various industries + Carrying out data analysis and analysis projects in the powerful Excel software environment + Various statistical methods and their exploitation to solve business problems and finalize data-driven decisions + Statistical assumption test with Excel software + Data-driven decision making and its principles + Business data analysis with descriptive statistics and statistical inference in Excel software + Construction and interpretation of various statistical models based on business data + Implementation of statistical analysis or regression analysis in Excel software to predict the future + Different techniques for analyzing huge and large data sets + Evaluation of different scenarios using available data ...

Language: English - Education Time: 7 hours and 28 minutes - Level: Elementary, Secondary - Size: 2.72 GB.

Data analysis is one of the leading jobs in the current technology market. As per the forecasts of Glassdoor and World Economic Forum, the demand for data scientists will also increase in the next few years. We are generating huge data every day from different domains like Social Media, Healthcare, Sensor data… we have a great tool to analyze them and the tool is R. R programming is a powerful language used widely for data analysis and statistical computing. It is completely free and has rich repositories for packages.

In this course first, you will learn how to install R and start programming on it. It will also help you to know the programming structures and functions. This R programming in Data Science and Data Analytics covers all the steps of Exploratory data analysis, Data pre-processing, and Modelling process. In EDA sections you will learn how to import data sets and create data frames from it. Then it will help you to visualize the variables using different plots. It will give you an initial structure of your data points. In Data pre-processing sections you will get the full idea of Missing value & outliers treatment and data split methods. Finally, you will be able to generate machine learning models using Linear and Logistic Regression.

This R programming for data science and data analytics is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science!

AutoCAD 2019 Course (2D drawing from A to Z) [Size: 2.00 GB] is going to be a full course which contains all of the subjects needed to work with 2D drawing using this software. The course is designed for beginners as well as experienced users. A beginner can start learning the software right from scratch by following the course along just from lecture one. A experienced AutoCAD user will also find this course very comprehensive. This course also contains practical examples and projects to work on. Anyone who wants to learn AutoCAD from scratch to professional level + People who may have used AutoCAD before and would like to brush up + People moving to AutoCAD from another drafting software type.

I’m very glad to have opportunity to teach you one of the most popular and powerful optimization algorithms in this course.

If you search FireFly optimization algorithm in google scholar, it could be seen that there are many vast range of papers has been published by implementing this optimization algorithm in different fields of science. In this course, after presenting the mathematical concept of each part of the considered optimization algorithm, I write its code immediately in matlab. All of the written codes are available, however, I strongly suggest to write the codes with me. Notice that, if you don’t have matlab or you know another programming language, don’t worry at all. You can simply write the codes in your own programming language because the behind concepts about all of the written codes are presented completely.

Artificial Neural Networks: Understand The Basic Concepts [Size: 285 MB] ... Artificial Intelligence is becoming progressively more relevant in today’s world. The rise of Artificial intelligence has the potential to transform our future more than any other technology. By using the power of algorithms, you can develop applications which intelligently interact with the world around you, from building intelligent recommender systems to creating self-driving cars, robots and chatbots. Neural networks are a key element of artificial intelligence.

Neural networks are one of the most fascinating machine learning models and are used to solve wide range of problems in different areas of artificial intelligence and machine learning. Yet too few really understand how neural networks actually work. This course will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. The purpose of this course is to make neural networks accessible to as many students as possible.

In this course I’m going to explain the key aspects of neural networks and provide you with a foundation to get started with advanced topics. You will build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. You’ll understand how to solve complex computational problems efficiently.

Practical Statistics For Data And Business Analysis [Size: 6.34 GB] ... This course material is prepared from highly experienced engineers worked in a leader companies like Microsoft , Facebook and Google. After hard working from five months ago we created +270 Lectures/Articles to cover everything related to practical statistics. In no time with simple and easy way you will learn and love statistics. We stress in this course to make it very spontaneous to make all students love statistics .

MATLAB Master Class: Go from Beginner to Expert in MATLAB [Size: 9.25 GB] ... MATLAB from beginner to advance level with Advanced Data Types and Applications from Data Science and Data Preprocessing: MATLAB (matrix laboratory) is one of the fundamental and leading programming language and is a must learn skill for anyone who want to develop a career in engineering, science or related fields. Excellent MATLAB programming skills is therefore a crucial factor in making or breaking your career. This course is designed from a perspective of a student who has no prior knowledge of MATLAB. The course starts from the very basic concepts and then built on top of those basic concepts and move towards more advanced topics such as visualization, exporting and importing of data, advance data types and data structures and advance programming constructs. To get the real feel of MATLAB in solving and analyzing real life problems, the course includes machine learning topics in data science and data preprocessing. The course is fun and exciting, but at the same time we dive deep into MATLAB to uncover its power of formulating and analyzing real life problems. The course is structured into four different Parts. Below is the detailed outline of this course.

Microsoft Office Suite 2016: Beginner to Pro [Size: 3.17 GB] .... Microsoft Office Suite 2016 for beginners and intermediates. This course covers wide range of topics such as Microsoft Word, Excel, Powerpoint, Access 2016 all in one place.

Excel: Master Microsoft Excel from Beginner to Advanced + Build a solid understanding on the Basics of Microsoft Excel + Learn the most common Excel functions used in the Office + Maintain large sets of Excel data in a list or table + Create dynamic reports by mastering one of the most popular tools, PivotTables. Word: You will learn how to take full advantage of Microsoft Word + Begin with the basics of creating Microsoft Word documents + Various techniques to create dynamic layouts + Preparing documents for printing and exporting + Format documents effectively using Microsoft Word Styles + Control page formatting and flow with sections and page breaks + Create and Manage Table Layouts + Work with Tab Stops to Align Content Properly + Perform Mail Merges to create Mailing Labels and Form Letters + Build and Deliver Word Forms + Manage Templates + Track and Accept/Reject Changes to a Document. PPT: Create a fully-animated and transition-filled business presentation + Rapidly improve your workflow and design skills + Minimize text quantity on presentations by using graphs and images + Work comfortably with PowerPoint and many of its advanced features + Become one of the top PowerPoint users in your team + Carrying out regular tasks faster than ever + Create sophisticated and well-organized PowerPoint presentations + Feel more confident when delivering presentations to superiors + Make an impression at work and achieve your professional goals. Access: Understand how Access is constructed and how to use the major objects within it. + Be confident in moving around within Access and be able to build effective database solutions for their unique data needs. + What you’ll learn + Understand the basics of Access tables, queries, forms and reports. + Know how to structure tables being imported from Excel. + Know how to create powerful queries and use them to create and modify tables. + Understand how reports work and how to base them on tables or queries. + Know how to create forms and subforms.

Artificial Intelligence Masterclass: Enter the new era of Hybrid AI Models optimized by Deep NeuroEvolution, with a complete toolkit of ML, DL & AI Models [Size: 6.10 GB] ... Are you keen on Artificial Intelligence? Do want to learn to build the most powerful AI model developed so far and even play against it? Sounds tempting right… Then Artificial Intelligence Masterclass course is the right choice for you. This ultimate AI toolbox is all you need to nail it down with ease. You will get 10 hours step by step guide and the full roadmap which will help you build your own Hybrid AI Model from scratch. In this course, we will teach you how to develop the most powerful Artificial intelligence model based on the most robust Hybrid Intelligent System. So far this model proves to be the best state of the art AI ever created beating its predecessors at all the AI competitions with incredibly high scores. This Hybrid Model is aptly named the Full World Model, and it combines all the state of the art models of the different AI branches, including Deep Learning, Deep Reinforcement Learning, Policy Gradient, and even, Deep NeuroEvolution. Learn how to combine the below models in order to achieve best performing artificial intelligence system: Fully-Connected Neural Networks + Convolutional Neural Networks + Recurrent Neural Networks + Variational AutoEncoders + Mixed Density Networks + Genetic Algorithms + Evolution Strategies + Covariance Matrix Adaptation Evolution Strategy (CMA-ES) + Parameter-Exploring Policy Gradients.

National Research University Higher School of Economics [Size: 1.26 GB] ... The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. Learners will study all popular building blocks of neural networks including fully connected layers, convolutional and recurrent layers. Learners will use these building blocks to define complex modern architectures in TensorFlow and Keras frameworks. In the course project learner will implement deep neural network for the task of image captioning which solves the problem of giving a text description for an input image. The prerequisites for this course are: 1) Basic knowledge of Python. 2) Basic linear algebra and probability. Please note that this is an advanced course and we assume basic knowledge of machine learning. You should understand: 1) Linear regression: mean squared error, analytical solution. 2) Logistic regression: model, cross-entropy loss, class probability estimation. 3) Gradient descent for linear models. Derivatives of MSE and cross-entropy loss functions. 4) The problem of overfitting. 5) Regularization for linear models. Who is this class for: Developers, analysts and researchers who are faced with tasks involving complex structure understanding such as image, sound and text analysis.

The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the machine learning, AI, and data mining techniques real employers are looking for, including: Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras + Sentiment analysis + Image recognition and classification + Regression analysis + K-Means Clustering + Principal Component Analysis + Train/Test and cross validation + Bayesian Methods + Decision Trees and Random Forests + Multivariate Regression + Multi-Level Models + Support Vector Machines + Reinforcement Learning + Collaborative Filtering + K-Nearest Neighbor + Bias/Variance Tradeoff + Ensemble Learning + Term Frequency / Inverse Document Frequency + Experimental Design and A/B Tests.

Machine Learning, Classification and Algorithms using MATLAB: Learn to Implement Classification Algorithms In One of the Most Power Tool used by Scientists and Engineer.

 This course is designed to cover one of the most interesting areas of machine learning called classification. I will take you step-by-step in this course and will first cover the basics of MATLAB. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox. We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Output Codes and Ensembles. Following that we will be looking at how to cross validate these models and how to evaluate their performances. Intuition into the classification algorithms is also included so that a person with no mathematical background can still comprehend the essential ideas. The following are the course outlines. Segment 1: Grabbing and Importing Dataset + Segment 2: K-Nearest Neighbor + Segment 3: Naive Bayes + Segment 4: Decision Trees + Segment 5: Discriminant Analysis + Segment 6: Support Vector Machines + Segment 7: Error Correcting Output Codes + Segment 8: Classification with Ensembles + Segment 9: Validation Methods + Segment 10: Evaluating Performance.

Welcome to my BIGGEST, most complete, Microsoft Excel course ever!: Excel is one of the most important skills in today’s job market. Having a solid working knowledge of Excel can improve your job performance, help you qualify for raises and promotions, or even help you land that new job that you deserve.

If you need to know Excel, you’re in the right place: This course [Size: 2.34GB] walks you through Excel from the very basics of opening Excel all the way to advanced Excel skills used by the pros, like pivot tables and macros. My goal with this course is to give you the skills you need to get that raise, promotion, or new job you deserve. This course was created using Excel 2016, but it is good for versions 2013 and 2010 as well. It was created for use with a PC (personal computer). Mac (Macintosh) users can still benefit from this course, but the course doesn’t cover Mac-specific keyboard shortcuts and interface. We’ll start at the very beginning, with opening and saving a worksheet in Excel: I’ll show you what all the tools do and walk you through each Excel skill that you need to know. I’ll show you how to enter and format your data and how to create charts, tables and pivot tables so you can organize and analyze your data. I’ll teach you amazing keyboard shortcuts to make your work faster and more productive. You’ll learn how to record macros so you can do a whole series of actions with the just click of a button. We’ll keep going until you’re a pro at Excel. When you finish this course, you’ll know more than most people who use Excel at their job every day.

AutoCAD Civil 3D Essential Training Course [Size: 1.03 GB] ... AutoCAD Civil 3D software is a design and documentation solution for civil engineering that supports building information modeling (BIM) workflows. By learning to use AutoCAD Civil 3D, you can improve project performance, maintain consistent data, follow standard processes, and respond faster to change. This course gets you up and running with AutoCAD Civil 3D. First, instructor Josh Modglin shows how to model a surface, lay out parcels, and design geometry, including the making of horizontal alignments and vertical profiles. Next, Josh demonstrates how to create corridors, cross sections, pipe networks, and pressure networks. Then, he covers working with feature lines and grading objects, and how to share your data. He wraps up by providing an overview of plan production tools.

Topics include: - Navigating the Civil 3D interface - Using point groups and description keys - Importing survey data - Managing figures - Creating and analyzing surfaces - Creating parcels - Working with alignments - Working with profiles and profile views - Working with assemblies and subassemblies - Creating Basic and Advanced Corridors - Using an Intersection Object - Making sample lines, cross sections, and section views - Creating a pipe network - Understanding pressure parts - Creating and editing feature lines - Creating and editing grading objects - Sharing and referencing data.

Machine Learning Classification Algorithms using MatLab [Size: 580 MB] ... This course is designed to cover one of the most interesting areas of machine learning called classification. I will take you step-by-step in this course and will first cover the basics of MATLAB. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox. We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Output Codes and Ensembles. Following that we will be looking at how to cross validate these models and how to evaluate their performances. Intuition into the classification algorithms is also included so that a person with no mathematical background can still comprehend the essential ideas. The following are the course outlines.

Table of Contents: - Segment 1: Instructor and Course Introduction - Segment 2: MATLAB Crash Course - Segment 3: Grabbing and Importing Dataset - Segment 4: K-Nearest Neighbor - Segment 5: Naive Bayes - Segment 6: Decision Trees - Segment 7: Discriminant Analysis - Segment 8: Support Vector Machines - Segment 9: Error Correcting Output Codes - Segment 10: Classification with Ensembles - Segment 11: Validation Methods - Segment 12: Evaluating Performance. This course is really good for a beginner. It will help you to start from the ground up and move on to more complicated areas. You receive knowledge from a Ph.D. in Computer science (machine learning) with over 10 years of teaching and research experience.