Data Structure Prerequisites : For Absolute Beginners
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Data Structure Prerequisites : For Absolute Beginners
Last updated 1/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 10.94 GB | Duration: 8h 28m
This course is a journey from Python programming to C++ fundamentals used to understand data structure.
What you'll learn
Map Python programming fundamentals with C++ Programming
All pragramming fundamentals required to learn Data Structure
How to start writing programs in C++ from scratch
Interesting facts about C++ programing with real world examples
Requirements
No programming or knowledge of computer needed. You will learn everything you need to know
Description
What will students learn in your course?Map Python programming fundamentals with C++ ProgrammingAll programming fundamentals required to learn Data StructureHow to start writing programs in C++ from scratchInteresting facts about C++ programing with real world examplesWhat Will Students Get from this course ?Understand programming fundamentals required to learn data structure conceptsLearn how to map python programming concepts with C++RequirementsNo programming knowledge is required for this course. You will learn everything from scratchTrainer 1: Tulsidas PatilMr. Tulsidas R. Patil is working as corporate trainer and visiting faculty at Engineering college. He has qualified all India GATE Exam and completed his Master of Technology with distinction. He has also qualified International level certification exams like SCJP, SCWCD and DB2 with excellent score. He is author of five books and presented his research papers in National as well as in International conferences. He is having good experience in Corporate Training, Academic teaching and training to international students from different countries like India, USA, Australia, UAE, UK ... on various online platforms.Trainer 2: Prathamesh BarveMr. Prathamesh V Barve has qualified all India GATE Exam and currently pursuing his Master of Technology. He has qualified International level certification exams in the field of deep learning, Machine learning and artificial intelligence with excellent score. He is very much committed to his work and commitments and well known by his right attitude and Practical Knowledge.
Overview
Section 1: Instalaltion and Configuration of Visual Studio Code
Lecture 1 Instalaltion and Configuration of Visual Studio Code
Section 2: Input, Output opearation and Variables Concept
Lecture 2 How to format and print output in C++
Lecture 3 Variables Concept
Lecture 4 Varable-Facts
Lecture 5 How to take input in C++
Lecture 6 Interesting Facts and Common Errors while taking Input in C++
Lecture 7 Practice Problems
Section 3: Operators
Lecture 8 Operators in C++
Section 4: If-Else concept in C++ (Conditional Decision Control Statement)
Lecture 9 If-Else concept in C++ (Conditional Decision Control Statement)
Lecture 10 Interesting Facts and Important Things to Know about If- else Statement
Lecture 11 If else Practice Problems
Section 5: Loops
Lecture 12 Loops Concepts and its Use in C++
Lecture 13 Loops Practice Problems
Lecture 14 Nested Loops in C++
Lecture 15 Interesting Facts about Loops
Section 6: Switch-Case Statement
Lecture 16 Switch Case in C++
Lecture 17 Interesting facts in Switch Case
Section 7: Advance Data Type- Arrays
Lecture 18 Introduction
Lecture 19 Multi dimentional Arrays
Section 8: Pointers
Lecture 20 Pointers in C++ with Memory Representation
Section 9: Dynamic memory allocation using Pointers
Lecture 21 Dynamic memory allocation using Pointers
Section 10: String - Character Arrays
Lecture 22 String - Concept and examples - Part 1
Lecture 23 String - Concept and examples - Part 2
Section 11: Functions
Lecture 24 Introduction to Functions
Lecture 25 Function Importance with Code and Real world Example
Section 12: Advance Data Type - Structure
Lecture 26 User Define DataType - Structure
Section 13: Function call using reference variable
Lecture 27 Function with Reference variable C++
Section 14: Menu Driven Programming
Lecture 28 How to code Menu Driven Program?
Section 15: Project
Lecture 29 Project 1
Section 16: How to Become a Pro-Coder..?
Lecture 30 How to code like Professionals?
Section 17: Classes and Objects
Lecture 31 Class Concept in C++
Section 18: Files Handling
Lecture 32 Files - Concept and examples
Section 19: Game Development
Lecture 33 stone paper scissors game
Lecture 34 Game 2
Section 20: Data Structure Course Introduction
Lecture 35 Data Structure Course Introduction
Section 21: C++ and C# Master Class course Introduction with WhasApp , PhonePe , Stock Markt
Lecture 36 Introduction to MasterClass
Much useful for SPPU students struggling with Data structure subject without having any knowledge of computer science,This course is perfect start to learn data structure concepts
Code:
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ePLAN Electric P8 - Custom Symbols / Tips And Tricks
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ePLAN Electric P8 - Custom Symbols / Tips And Tricks
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 11 lectures (1h 3m) | Size: 504.1 MB
Symbol libraries management, custom symbols, layer management, funtions to help you work more efficiently in EPLAN etc.
What you'll learn
To work with Layer Management in EPLAN (to see what layers in EPLAN can do for us)
To manage symbols and symbol libraries in EPLAN (create, adjust, new variant, import/export, delete etc.)
To work with the data within EPLAN project more effectively (Navigators, Edit in Table, Edit Properties Externally)
To tag a cable according to special requiremet from some customers (special case)
To graphically represent a phase reversal in the circuit diagram (required for some actuators like pumps)
To graphically represent spare cable cores (2 most common ways in the practice)
Requirements
Eplan Electric P8 software (any version, including Eplan Education)
Description
DISCLAIMER: Please note that this is not an official EPLAN course. For official training and certification as well as for the purchase of EPLAN software contact the manufacturer of the software.
*** UPDATES ***
- October 26, 2019 - Captions in English (EDITED, not auto-generated).
Dear Student,
I welcome you to, according to my opinion, the intermediate course on EPLAN Electric P8.
In this short but intense course we are going to cover the following sections / function in EPLAN:
Symbols and symbol libraries - masterclass - everything you need to know about custom symbols in EPLAN
Layer management in EPLAN - what layers in EPLAN can do for us in the real-world
How to be more effective when working in EPLAN
Bonus lecture - very interesting way to tag cables (a requirement from some customers in the real-world)
This is going to be one of my last courses on EPLAN Electric P8. I might do another one and for that one I will ask for a feedback from you, my students, to tell me which are the things you would like me to cover in the last one.
After EPLAN courses I will concentrate more on electrical design, with examples of projects I have worked on so far.
To help you master electrical design on a professional level so that you can consider a career in this field of electrical engineering.
I hope you will find my course valuable and fun and I wish you a great success in mastering EPLAN and electrical design!
Also please don't forget to rate my course and leave a short review. I thank you for that in advance.
Your Instructor for Electrical Design,
Ivan
Who this course is for
Everyone who wants to work in EPLAN Electric P8
Code:
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A Practical Approach to Timeseries Forecasting using Python
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A Practical Approach to Timeseries Forecasting using Python
Published 09/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 124 lectures (12h 21m) | Size: 5.2 GB
A Complete Course on Time Series Forecasting using Machine Learning and Recursive Neural Networks with Projects
What you'll learn
• Learn the basics of Time Series Analysis and Forecasting.
• Learn basics of Data Analysis Techniques and to Handle Time Series Forecasting.
• Learn to implement the basics of Data Visualization Techniques using Matplotlib
• Learn to Evaluate and Analyze Time Series Forecasting Parameters i.e., Seasonality, Trend, and Stationarity etc.
• Learn to compute and visualize the auto correlation, mean over time, standard deviation and gaussian noise in time series datasets.
• Learn to evaluate applied machine learning in Time Series Forecasting
• Learn to implement Machine Learning Techniques for Time Series Forecasting i.e., Auto Regression, ARIMA, Auto ARIMA, SARIMA, and SARIMAX
• Learn basics of RNN Models i.e., GRU, LSTM, BiLSTM
• Learn to model LSTM, Stacked LSTM, BiLSTM and Stacked BiLSTM models for time series forecasting.
• Learn the impact of Overfitting, Underfitting, Bias and Variance on the performance of RNN Models
• Learn how to implement ML and RNN Models with three state-of-the-art projects.
• And much more.
Requirements
• No prior knowledge of Deep Learning, Data Analysis or Maths is needed. We will start from the basics and gradually build your knowledge in the subject.
• A willingness to learn and practice.
• Only basic Python is required.
Description
Comprehensive Course Description
Have you ever wondered, how weather predictions are made?
Have you ever thought to estimate the global population in 2050!
What if, someone told you that you can predict the expected life of our universe by just sitting next to your laptop in your home.
Its all true! Just because of the Time Series Forecasting pedagogies by using state-of-the-art and robust models of Machine Learning and Deep Learning.
You might have searched for many relevant courses, but this course is different!
This course is a complete package for the beginners to learn time series, data analysis and forecasting methods from scratch. Every module has engaging content, a complete practical approach is used in along with brief theoretical concepts. At the end of every module, we assign you a hand-on exercise or quiz, the solution to the quizzes is also available in the next video.
We will be starting with the theoretical concepts of time series analysis, after a brief overview of its features, examples, mechanism of time series data collection and its scope in the real world, we will learn the basic bench marked steps to compute time series forecasting.
This complete package will enable you to learn the basic to advance data analysis and visualization with respect to time series data by using Numpy, Pandas and Matplotlib. We'll be using Python as a programming language in this course, which is the hottest language nowadays if we talk about machine leaning. Python will be taught from elementary level up to an advanced level so that any machine learning concept can be implemented.
This comprehensive course will be your guide to learning how to use the power of Python to evaluate your time series datasets on the basis of seasonality, trend, noise, autocorrelation, mean overtime, correlation, and on stationarity. Moreover, the impact and role of feature engineering will make you capable of performing exceptional data handling for your forecasting models. Based on this learning you will be able to prepare your time series data for the applied Machine Learning and RNNs Models to test, train and evaluate your forecasted scores.
We'll learn all the basic and necessary concepts for the applied machine learning models such as Auto-Regression, Moving Average, ARIMA, Auto-ARIMA, SARIMA, Auto-SARIMA and SARIMAX in the perspective of the time series forecasting. Moreover, the performance comparison of these models will also be comprehensively discussed.
Machine learning has been ranked as one of the hottest jobs on Glassdoor, and the average salary of a machine learning engineer is over $110,000 in the United States, according to Indeed! Machine Learning is a rewarding career that allows you to solve some of the world's most interesting problems!
In the RNNs Module, we'll be learning a complete mechanism of building GRU, LSTM, Stacked LSTM, BiLSTM and Stacked BiLSTM models along with the practical concepts of the underfitting, overfitting, bias, variance, dropout, role of dense layers, impact of batch sizes, and performance of different activation functions on the RNN models of multiple different layers. Each concept of the "Recursive Neural Networks" (RNNs) will be taught theoretically and will be implemented using Python.
This course is designed for both beginners with some programming experience or even those who know nothing about Data Analysis, ML and RNNs!
This comprehensive course is comparable to other Time Series Courses using Machine Learning and RNNs courses that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost in only one course! With over 12 hours of HD video lectures that are divided into more than 120 videos and detailed code notebooks for every address this is one of the most comprehensive courses for Time Series Forecasting with Machine Learning and RNNs on Udemy!
Why Should You Enroll in This Course?
The course is crafted to help you understand not only the role and impact of timeseries analysis and how to use ML and build RNNs but also how to train them, understand their impact with the key concept of overfitting and underfitting. This straightforward learning by doing course will help you in mastering the concepts and methodology with regards to Python.
This course is
? Easy to understand.
? Expressive and self-explanatory.
? To the point.
? Practical with live coding.
? A complete package with three in depth projects covering complete course contents.
? Thorough, covering the most advanced and recently discovered RNN models by renowned data scientists.
Teaching Is Our Passion
We focus on creating online tutorials that encourage learning by doing. We aim to provide you with more than a superficial look at time series forecasting with the help of RNNs and Machine Learning Algorithms such as ARIMA, SARIMA and SARIMAX etc. For instance, this course has three projects in the final module which will help you to see for yourself via experimentation the practical implementation of RNNs and ML with advance data analysis on the real-world datasets of Birthrates, Stock Exchange and COVID-19. We have worked extra hard to ensure you understand the concepts clearly. We want you to have a sound understanding of the basics before you move onward to the more complex concepts. The course materials that make certain you accomplish all this include high-quality video content, course notes, meaningful course materials, handouts, and evaluation exercises. You can also get in touch with our friendly team in case of any queries.
Course Content
We'll teach you how to program with Python, how to use it for data visualization, data manipulation and RNNs! Here are just a few of the topics that we will be learning
1. Packages Installation
2. Basic Data Manipulation in Time Series using Python
3. Data Processing for Timeseries Forecasting using Python
4. Machine Learning in Time Series Forecasting using Python
5. Recurrent Neural Networks for Time Series using Python
6. Project 1: COVID-19 Prediction using Machine Learning Algorithms
7. Project 2: Microsoft Corporation Stock Prediction using RNNs
8. Project 3: Birthrate Forecasting using RNNs with Advance Data Analysis and much more
Enroll in the course and become a time series forecasting expert today!
After completing this course successfully, you will be able to
? Relate the concepts and theories for time series forecasting and its parameters.
? Understand evaluate the machine learning models.
? Understand the model and implementation of RNN models for the time series forecasting
Who this course is for
? People who want to advance their skills in machine learning and deep learning.
? People who want to master relation of data science with time series analysis.
? People who want to implement time series parameters and evaluate their impact on it.
? People who want to implement machine learning algorithms for time series forecasting.
? Individuals who are passionate about RNNs specially, LSTM, Stacked LSTM, BiLSTM and Stacked BiLSTM Models.
? Machine Learning Practitioners.
? Research Scholars.
? Data Scientists.
Who this course is for
• People who want to advance their skills in machine learning and deep learning.
• People who want to master relation of data science with time series analysis.
• People who want to implement time series parameters and evaluate their impact on it.
• People who want to implement machine learning algorithms for time series forecasting.
• Individuals who are passionate about RNNs specially, LSTM, Stacked LSTM, BiLSTM and Stacked BiLSTM Models.
• Machine Learning Practitioners.
• Research Scholars.
• Data Scientists.
Code:
https://anonymz.com/?https://www.udemy.com/course/a-practical-approach-to-timeseries-forecasting-using-python/
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Eplan Electric P8 - Full course from Beginner to Advance (updated 6/2021)
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Eplan Electric P8 - Full course from Beginner to Advance (updated 6/2021)
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.95 GB | Duration: 6h 12m
What you'll learn
Students will learn about software Eplan, and how to create projects of Electrical circuits and theirs report.
Project - create, copy, delete, rename, backup, restore.
Pages - type, number, structures Structure in EPLAN - how it works and why we need to understand it.
Page / Window macro - how and why to use it.
The Plot frame, Plot form - how we can make it.
Basic EPLAN circuit functions - insert symbol and create new symbol library, adjust, number.
Reports (Title page, cable overview, terminal diagram, parts reports) Export EPLAN Project to PDF.
Create 3D mounting panel and 3D macro.
How to install Eplan software?
How to numerise conductors?
How to create 3D model views?
You can download books and summaries and practice in your home.
How to create PLC controller macro and device?
How to create revision and project options?
How to create PLC box and adress bits?
Requirements
Every Student will be able to complete this course, and after that they will be able to create their own projects on Advanced level.
Description
Welcome in my complete video course about Eplan Student Version 2.73. I would like to recomended you to download all of PDF lectures and books for learning Eplan software.
I would recomended you to do all quizes. The best way to learn Eplan effectively is to do all of exercises and you have to print PDF of all lectures during course. Course is for students of all level of knowledge Eplan software 2.73. Course is designed for students who wants more. Course have PDF books, PDF lectures, and Quizes.
These features enable students to learn more and faster. Eplan software is for Electrical Engineers, Automation Engineers. In this course i will attach schematic circuits form industry of automatisation for practice.
In this course we will learn how to create plot frames for pages, plot form for cover pages, how to create projects, basic projects and template.
In this course you will learn how to create reports and how to modfy reports and translate on other language. In this course you will learn how to create panel layout in encloshures. In this course you will learn how to optimise project and how to send them by email.
In this course you will learn how you can set you own parameters in project and how to make template for this parameters for future project. In this course you will learn how to set workspace, pages and so on.
Who this course is for:
This course is projected for students from Beginner to some Intermediate level.
Code:
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Mindfulness Meditation With Jack Kornfield
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Mindfulness Meditation With Jack Kornfield
Last updated 10/2019
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 5.95 GB | Duration: 4h 13m
Core Practices For Living Wisely
What you'll learn
Sitting meditation
Walking meditation
Handle strong sensations & pain
Eat with mindfulness
Acknowledge thoughts & feelings without getting caught in them
Cultivate compassion
Attune to highest intentions
Requirements
There are no prerequisites for taking this course
Description
Mindfulness Meditation with Jack Kornfield is a complete course in practicing mindfulness by one of the world's leading experts. Decades of neuroscience studies show the power of mindfulness to relieve stress, awaken our positive capacities, and bring balance to our lives. Mindfulness gives us the power to meet any situation wisely, to be fully present, and to operate with compassion. When we can look more clearly into ourselves and our life situation, new possibilities naturally open up for transforming difficulties and emerging stronger and wiser.This comprehensive training in all the fundamental mindfulness practices is entirely accessible to everyone, from beginners to those with prior meditative experience. Rich with stories, practices, teachings and good humor, it features eight complete sessions of guided exercises in sitting meditation including mindfulness of breath, body, feelings and thoughts; walking meditation; mindful eating; compassion practice to free the heart; and connection with our deepest aspirations and intention. These guided segments are a valuable resource that can be used again and again to sustain or refresh your regular, ongoing practice.Here is the benefit: We all know how busy and divisive modern times can be, and how easily they can fuel stress and anxiety. Mindfulness is both healing and liberating. Learning to meet our complex world and our own changing mental states with mindful loving awareness and courage allows us to find spacious, clear and healthy responses to life, rather than be caught in habitual reactions and struggle.Mindfulness quiets the mind and heals the heart. Systematically practicing mindfulness, we can recognize and foster positive states of mind like kindness, generosity, steadiness, and love -- discovering how they are natural to us; and learn to nurture and strengthen them into a new, more gracious life of well-being.
Overview
Section 1: MEDITATION: THE FUNDAMENTALS
Lecture 1 Our Human Predicament
Lecture 2 Course Overview: Living Wisely in Times of Change & Conflict
Lecture 3 The Most Fundamental Practice
Lecture 4 Guided Sitting Meditation Practice
Lecture 5 Distraction and Resistance: Helpful Hints + Q/A
Section 2: WALKING MEDITATION; BEGINNING TO WORK WITH PAIN
Lecture 6 Introduction to Walking Meditation: Instructions
Lecture 7 Walking Meditation Self-Guided Practice (timer segment)
Lecture 8 Bringing Mindfulness To Difficult Sensations
Lecture 9 Skillfully Relating To Vulnerability
Section 3: MEDITATING WITH STRONG SENSATIONS; MINDFUL EATING
Lecture 10 Challenges Around Mindfulness of Breathing: Q/A
Lecture 11 Guided Practice: Sitting With Strong Sensations
Lecture 12 Guided Mindful-Eating Practice
Lecture 13 Reflections on Eating Practice
Section 4: WHAT REALLY MATTERS
Lecture 14 Introduction to Heart Practices
Lecture 15 Guided Contemplative Practice: The Work of the Heart
Lecture 16 Neuroscientific Support For Kindness & Compassion Practice
Section 5: AWARENESS THAT CAN HOLD IT ALL
Lecture 17 Mindfulness of Emotions
Lecture 18 Unpleasant Feelings & Thoughts
Lecture 19 Additional Comments on Mindfulness of Feelings & Thoughts
Lecture 20 Guided Sitting Meditation Practice
Section 6: STEWARDING OUR OWN LIFE
Lecture 21 Progress and Benefits of the Practice
Lecture 22 Attentional Hygiene
Lecture 23 Courage and Understanding
Lecture 24 Not Alone
Lecture 25 Dire Circumstances
Section 7: BRINGING OUR GIFTS TO THE WORLD
Lecture 26 Hope and Peace in These Times
Lecture 27 Connecting Deeply
Lecture 28 Joyfully Tending the Garden of the World
Lecture 29 Guided Exercise: Your Vow
Lecture 30 Guided Practice Exercise: Nobody Else Can Do It For You
Section 8: BONUS: 24-MINUTE, LIGHTLY-GUIDED PRACTICE
Lecture 31 24-Minute, Lightly-Guided Sitting Practice
Section 9: Additional Resources and Acknowledgements
Lecture 32 Resources and Acknowledgments
Everyone who wants to learn to practice mindfulness,Anyone feeling overwhelmed by inner or outer challenges,Everyone seeking greater concentration and clarity
Code:
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Market Research | Complete Marketing Research Course 2022
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Market Research | Complete Marketing Research Course 2022
Published 9/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 5.43 GB | Duration: 12h 3m
Exam DP-100: Designing and Implementing a Data Science Solution on Azure Covered, Learn Azure Machine Learning
What you'll learn
Prepare for DP-100 Exam
Getting Started with Azure ML
Setting up Azure Machine Learning Workspace
Running Experiments and Training Models
Deploying the Models
AzureML Designer: Data Preprocessing
Regression Using AzureML Designer
Classification Using AzureML Designer
AzureML SDK: Setting up Azure ML Workspace
AzureML SDK: Running Experiments and Training Models
Use Automated ML to Create Optimal Models
Tune hyperparameters with Azure Machine Learning
Use model explainers to interpret models
Requirements
Basic Understanding of Machine Learning
A Free or Paid Subscription to Microsoft Azure
Description
Machine Learning and Data Science are one of the hottest tech fields now a days ! There are a lot of opportunities in these fields. Data Science and Machine Learning has applications in almost every field, like transportation, Finance, Banking, Healthcare, Defense, Entertainment, etc.Most of the professionals and students learn Data Science and Machine Learning but specifically they are facing difficulties while working on cloud environment. To solve this problem I have created this course, DP-100. It will help you to apply your data skills in Azure Cloud smoothly.This course will help you to pass the "Exam DP-100: Designing and Implementing a Data Science Solution on Azure". In this course you will understand what to expect on the exam and it includes all the topics that are require to pass the DP-100 Exam.Below are the skills measured in DP-100 Exam,1) Manage Azure resources for machine learning (25-30%)Create an Azure Machine Learning workspaceManage data in an Azure Machine Learning workspaceManage compute for experiments in Azure Machine LearningImplement security and access control in Azure Machine LearningSet up an Azure Machine Learning development environmentSet up an Azure Databricks workspace2) Run experiments and train models (20-25%)Create models by using the Azure Machine Learning designerRun model training scriptsGenerate metrics from an experiment runUse Automated Machine Learning to create optimal modelsTune hyperparameters with Azure Machine Learning3) Deploy and operationalize machine learning solutions (35-40%)Select compute for model deploymentDeploy a model as a serviceManage models in Azure Machine LearningCreate an Azure Machine Learning pipeline for batch inferencingPublish an Azure Machine Learning designer pipeline as a web serviceImplement pipelines by using the Azure Machine Learning SDKApply ML Ops practices4) Implement responsible machine learning (5-10%)Use model explainers to interpret modelsDescribe fairness considerations for modelsDescribe privacy considerations for dataSo what are you waiting for, Enroll Now and understand Azure Machine Learining to advance your career and increase your knowledge!
Overview
Section 1: Getting Started with Azure ML
Lecture 1 Introduction to Azure Machine Learning
Lecture 2 Introduction to Azure Machine Learning Studio
Lecture 3 Azure ML Cheat Sheet
Lecture 4 DP-100 Exam Skills Measured (Exam Curriculum)
Section 2: Setting up Azure Machine Learning Workspace
Lecture 5 Azure ML: Architecture and Concepts
Lecture 6 Creating AzureML Workspace
Lecture 7 Workspace Overview
Lecture 8 AzureML Studio Overview
Lecture 9 Introduction to Azure ML Datasets and Datastores
Lecture 10 Creating a Datastore
Lecture 11 Creating a Dataset
Lecture 12 Exploring AzureML Dataset
Lecture 13 Introduction to Azure ML Compute Resources
Lecture 14 Creating Compute Instance and Compute Cluster
Lecture 15 Deleting the Resources
Section 3: Running Experiments and Training Models
Lecture 16 Azure ML Pipeline
Lecture 17 Creating New Pipeline using AzureML Designer
Lecture 18 Submitting the Designer Pipeline Run
Section 4: Deploying the Models
Lecture 19 Creating Real-Time Inference Pipeline
Lecture 20 Deploying Real-Time Endpoint in AzureML Designer
Lecture 21 Creating Batch Inference Pipeline in AzureML Designer
Lecture 22 Running Batch Inference Pipeline in AzureML Designer
Lecture 23 Deleting the Resources
Section 5: AzureML Designer: Data Preprocessing
Lecture 24 Setting up Workspace and Compute Resources
Lecture 25 Sample Datasets
Lecture 26 Select Columns in Dataset
Lecture 27 Importing External Dataset From Web URL
Lecture 28 Edit Metadata - Column Names
Lecture 29 Edit Metadata - Feature Type and Data Type
Lecture 30 Creating Storage Account, Datastore and Datasets
Lecture 31 Adding Columns From One Dataset to Another One
Lecture 32 Adding Rows From One Dataset to Another One
Lecture 33 Clean Missing Data Module
Lecture 34 Splitting the Dataset
Lecture 35 Normalizing Dataset
Lecture 36 Exporting Data to Blob Storage
Lecture 37 Deleting the Resources
Section 6: Project 1: Regression Using AzureML Designer
Lecture 38 Creating Workspace, Compute Resources, Storage Account, Datastore and Dataset
Lecture 39 Business Problem
Lecture 40 Analyzing the Dataset
Lecture 41 Data Preprocessing
Lecture 42 Training ML Model with Linear Regression (Online Gradient Descent)
Lecture 43 Evaluating the Results
Lecture 44 Training ML Model with Linear Regression (Ordinary least squares)
Lecture 45 Training ML Model with Boosted Decision Tree and Decision Forest Regression
Lecture 46 Finalizing the ML Model
Lecture 47 Creating and Deploying Real-Time Inference Pipeline
Lecture 48 Creating and Deploying Batch Inference Pipeline
Lecture 49 Deleting the Resources
Section 7: Project 2: Classification Using AzureML Designer
Lecture 50 Creating Workspace, Compute Resources, Storage Account, Datastore and Dataset
Lecture 51 Business Problem
Lecture 52 Analyzing the Dataset
Lecture 53 Data Preprocessing
Lecture 54 Training ML Model with Two-Class Logistic Regression
Lecture 55 Training ML Model with Two-Class SVM
Lecture 56 Training ML Model with Two-Class Boosted Decision Tree & Decision Forest
Lecture 57 Finalizing the ML Model
Lecture 58 Creating and Deploying Batch Inference Pipeline
Section 8: AzureML SDK: Setting up Azure ML Workspace
Lecture 59 AzureML SDK Introduction
Lecture 60 Creating Workspace using AzureMl SDK
Lecture 61 Creating a Datastore using AzureMl SDK
Lecture 62 Creating a Dataset using AzureMl SDK
Lecture 63 Accessing the Workspace, Datastore and Dataset with AzureML SDK
Lecture 64 AzureML Dataset and Pandas Dataset Conversion
Lecture 65 Uploading Local Datasets to Storage Account
Section 9: AzureML SDK: Running Experiments and Training Models
Lecture 66 Running Sample Experiment in AzureML Environment
Lecture 67 Logging Values to Experiment in AzureML Environment
Lecture 68 Introduction to Azure ML Environment
Lecture 69 Running Script in AzureML Environment Part 1
Lecture 70 Running Script in AzureML Environment Part 2
Lecture 71 Uploading the output file to Existing run in AzureML Environment
Lecture 72 Logistic Regression in Local Environment Part 1
Lecture 73 Logistic Regression in Local Environment Part 2
Lecture 74 Creating Python Script - Logistic Regression
Lecture 75 Running Python Script for Logistic Regression in AzureML Environment
Lecture 76 log_confusion_matrix Method
Lecture 77 Provisioning Compute Cluster in AzureML SDK
Lecture 78 Automate Model Training - Introduction
Lecture 79 Automate Model Training - Pipeline Run Part 1
Lecture 80 Automate Model Training - Pipeline Run Part 2
Lecture 81 Automate Model Training -Data Processing Script
Lecture 82 Automate Model Training - Model Training Script
Lecture 83 Automate Model Training - Running the Pipeline
Section 10: Use Automated ML to Create Optimal Models
Lecture 84 Introduction to Automated ML
Lecture 85 Automated ML in Azure Machine Learning studio
Lecture 86 Automated ML in Azure Machine Learning SDK
Section 11: Tune hyperparameters with Azure Machine Learning
Lecture 87 What Hyperparameter Tuning Is?
Lecture 88 Define the Hyperparameters Search Space
Lecture 89 Sampling the Hyperparameter Space
Lecture 90 Specify Early Termination Policy
Lecture 91 Configuring the Hyperdrive Run - Part 1
Lecture 92 Configuring the Hyperdrive Run - Part 2
Lecture 93 Creating the Hyperdrive Training Script
Lecture 94 Getting the Best Model and Hyperparameters
Section 12: Use model explainers to interpret models
Lecture 95 Interpretability Techniques in Azure
Lecture 96 Model Explainer on Local Machine
Lecture 97 Model Explainer in AzureML Part 1
Lecture 98 Model Explainer in AzureML Part 2
Anyone who wants to learn Azure Machine Learning,Students and Professionals Who Wants to Pass DP-100 Exam
Code:
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Code:
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https://rapidgator.net/file/4f87930c288aecfbc3fd74756e7015c7/Market_Research_Complete_Marketing_Research_Course_2022.part2.rar
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ePLAN Electric P8 Heavyweight Vol.2/2 - 2D Panel, Parts
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ePLAN Electric P8 Heavyweight Vol.2/2 - 2D Panel, Parts
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 13 lectures (2h 15m) | Size: 1.33 GB
Learn how to properly work with databases (Access), parts and 2D panel layout in Eplan Electric P8
What you'll learn
To get the most of Eplan Electric P8 by using it the proper way (in this course databases, parts, 2D panel layout)
To manage databases (Access) and parts in Eplan
To assign parts to various elements in the schematics - devices, cables, terminals etc.
To import DWG/DXF files to Eplan and to adjust them to your need
To create 2D mounting panel layout properly
To place various elements on the mounting panel
To setup, generate and adjust Enclosure legend and place it on the page of the panel layout.
To solve various issues when working with parts (function definition, too many connections at a connection point etc.)
To generate reports for parts - parts list and summarized parts list in Eplan
Requirements
Basic understanding of electrical symbols and electrical drawings
Eplan Electric P8 software 2.7 HF5 license (but any other version will do the job, including Education)
Description
DISCLAIMER: Please note that this is not an official EPLAN course. For official training and certification as well as for the purchase of EPLAN software contact the manufacturer of the software.
Important: Lecture 2 of the ePLAN Heavyweight Vol. 1 course (free preview) shows you how to download and import parts from Siemens and Phoenix Contact without access to Data Portal.
*** UPDATES ***
- September 11, 2019 - Captions in English (EDITED, not auto-generated).
Dear Student,
In this tutorial we are going to continue to work on our automation project we created in the EPLAN Vol.1 Course. We are going to further expand our project by assigning parts to all the elements in the schematics and then we are going to create 2D mounting panel layouts for all of the cabinets.
Of course, as in the previous course, I am going to share with you a number of useful tips and tricks as you make your progress through the lectures.
If you feel that you are still somewhere on the beginner's level in EPLAN then I recommend that you check my first EPLAN course (Eplan Heavyweight Vol.1) where I show you how to start to work in EPLAN from scratch - i.e. from creating a project all the way up to creating circuit diagrams for PLC and some advanced topis like editing plotframe/form and EPLAN's "Check project" functionality.
I hope you will find this course valuable and I wish you a lot of fun and a lot of success in mastering Eplan Electric P8.
See you in the lectures!
Your Instructor for Electrical Design,
Ivan
Who this course is for
Generally everyone who would like to significantly improve their chance of getting a job anywhere in the world just by knowing how to work properly in Eplan Electric P8
Everyone considering to start to use ePLAN for electrical design
Electricians, technicians and engineers
Code:
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Most Complete Teaching of BGP
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Most Complete Teaching of BGP
Published 09/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 317 lectures (41h 18m) | Size: 15.4 GB
Border Gateway Protocol
What you'll learn
BGP - Fundamentals
eBGP - Neighborship with Connected Interface
eBGP - Neighborship Conditions
eBGP - Neighborship over Multiple Links
BGP - Keep Alive Interval and Holdtime
BGP - Messages and Neighborship States
BGP - Injecting Routes into BGP Table
BGP - Automatic Summarization
BGP - Basic Aggregation
BGP - Atomic Aggregate , Aggregator Attribute
BGP - Advanced Aggregation with AS-SET
BGP - Advanced Aggregation with UnSuppress-Map
BGP - Advanced Aggregation with Suppress-Map
BGP - Advanced Aggregation with Advertise Map
BGP - Origin Code Path Attribute
BGP - Advanced Aggregation with Attribute Map
BGP - Backdoor
BGP - Next-Hop Path Attribute
BGP - Internal BGP[iBGP]
BGP - Peer Group
BGP - iBGP Neighborship with Loopback Interfaces
BGP - Confederation
BGP - Route Reflector Redundancy
BGP - Route Reflection with Multiple Clusters
BGP - Hierarchical Route Reflection
BGP - Default Route Advertisement
BGP - Best Path Selection - Weight
BGP - Best Path Selection - Local Weight
BGP - Best Path Selection - Local Preference
BGP - Best Path Selection - Preference of Locally Injected Path
BGP - Best Path Selection - Accumulated IGP[AIGP]
BGP - Best Path Selection - AS-Path
BGP - Best Path Selection - Origin Code
BGP - Best Path Selection - Multi Exit Discriminator[MED]
BGP - Advanced MED - Always Compare MED
BGP - Advanced MED - Missing-as-worst
BGP - Best Path Selection - Prefer eBGP to iBGP Path
BGP - Best Path Selection - Lowest IGP Metric to Next-Hop
BGP - Best Path Selection - Oldest eBGP Route
BGP - Best Path Selection - Lower BGP Router-ID
BGP - Best Path Selection - Minimum Cluster-List Length
BGP - Best Path Selection - Lowest Neighbor IP
BGP - MultiPath Equal Cost Load Balancing[ECLB]
BGP - MultiPath Unequal Cost Load Balancing[UCLB]
BGP - Route Filtering with ACL
BGP - Route Filtering with IP Prefix-List
BGP - Route Filtering with Route-Map
BGP - Route Filtering with AS-Path ACL
BGP - BGP Regular Expressions - REGEXPs
BGP - Hard Clearing , Soft Clearing
BGP - Outbound Route Filtering[ORF]
BGP - Maximum Prefix
BGP - Remove Private AS Number
BGP - Allow AS
BGP - Dynamic iBGP Peer Group
BGP - Dynamic eBGP Peer Group
BGP - Path Attributes
BGP - Fast Neighbor Loss Detection
BGP - Route-Reflector with Full Mesh Client
BGP - Multi Cluster ID[ MCID ]
BGP - Selective Route Download
BGP - Diverse Path with Shadow Co-located RR
BGP - Diverse Path with Shadow Co-located RR
BGP - Diverse Path with Shadow Session
BGP - Single Homed Connection
BGP - Floating Static Route
BGP - Multiprotocol BGP[ MBGP ]
BGP - TTL Security
BGP - Dynamic Update Peer-Group
BGP - Max AS
BGP - Multi Session Capability
BGP - Route Server
BGP - Route Server Context
Requirements
CCNA
Description
Border Gateway Protocol (BGP) is a standardized exterior gateway protocol designed to exchange routing and reachability information among autonomous systems (AS) on the Internet. BGP is classified as a path-vector routing protocol, and it makes routing decisions based on paths, network policies, or rule-sets configured by a network administrator.
BGP used for routing within an autonomous system is called Interior Border Gateway Protocol, Internal BGP (iBGP). In contrast, the Internet application of the protocol is called Exterior Border Gateway Protocol, External BGP (eBGP).
BGP neighbors, called peers, are established by manual configuration among routers to create a TCP session on port 179. A BGP speaker sends 19-byte keep-alive messages every 30 seconds (protocol default value, tunable) to maintain the connection. Among routing protocols, BGP is unique in using TCP as its transport protocol.
When BGP runs between two peers in the same autonomous system (AS), it is referred to as Internal BGP (iBGP or Interior Border Gateway Protocol). When it runs between different autonomous systems, it is called External BGP (eBGP or Exterior Border Gateway Protocol). Routers on the boundary of one AS exchanging information with another AS are called border or edge routers or simply eBGP peers and are typically connected directly, while iBGP peers can be interconnected through other intermediate routers. Other deployment topologies are also possible, such as running eBGP peering inside a VPN tunnel, allowing two remote sites to exchange routing information in a secure and isolated manner.
Who this course is for
Network Engineers
Homepage
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