Azure Data Engineer Certified: [8 course Bundle] DP-200,201.



Azure Data Engineer Certified: [8 course Bundle] DP-200,201.
Duration: 20h 12m | Video: .MP4 1280x720, 30 fps(r) | Audio: AAC, 44100 Hz, 2ch | Size: 7.58 GB
Genre: eLearning | Language: English

100% Syllabus Covered. DataLakeStorage,Databricks, SQL Database,Stream Analytics, Data Factory,Synapse ,Cosmos DB

What you'll learn
Students will learn Azure Stream Analytics, Azure Data Lake, Azure Data Factory, Azure Cosmos DB, Azure SQL Database, Azure Data Factory, Power BI

Requirements
None


Description
Hi,

If you are aiming for Azure Data Engineer Certification, you need to clear two exams i.e DP-200 & DP-201. This course will provide all the content you require to clear both these exams. And all the concepts are explained using practical lab sessions.

Azure Data Engineer is the most lucrative job profile and those who possess the skill sets require to become Azure Data Engineer are always in great demand and easily can get a 6 figure salary in USD as per the market trend.

But, it can be overwhelming for a beginner to start his/her journey to become a Azure Data Engineer since there are so many different technologies and services offered by Azure.

Hence below I have provided step by step guide of the skill sets require to start your Data Engineer journey and then progress gradually to become an Intermediate level or Expert Azure Data Engineer.

Beginner -> Azure SQL Database, Azure Data Factory, Azure Data Lake, Power BI

Intermediate -> Azure Synapse Analytics, Azure Cosmos DB

Expert -> Azure Databricks, Azure Stream Analytics

In this course, I have covered 100% syllabus required to clear DP-200 and DP-201 exam.

NOTE: ALL THE AZURE DATA SERVICES PRESENT IN THIS COURSE ARE FOR BEGINNERS

Following are the topics currently this course covers:

1. Basics of Cloud Computing

2. Azure SQL Database

3. Azure Data Lake Storage

4. Azure Data Factory V2

5. Implementing Real World Use Cases

6. Power BI

7. Azure Synapse Analytics

8. Azure Cosmos DB

9. Azure Stream Analytics

10. Azure Databricks

Also, all the topics in this course are explained using practical examples and lab sessions which makes it easy to understand and also practice at home for students.

I have also added a section containing "REAL WORLD USE CASES" where some common and important scenarios which you would come across in real life when you work as a Azure Data Engineer in an organization. Do not miss this section as its very important and will also help you in the interview.

All the Best and Happy Learning !!


Who this course is for:
Everyone

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Creating a Real Time Character in Substance Painter



Creating a Real Time Character in Substance Painter
Video: .MP4, 1920x1080 30 fps | Audio: AAC, 44.1 kHz, 2ch | Duration: 2h
Genre: eLearning | Language: English | Size: 5.54 GB

This tutorial is aimed to solve the mystery of texturing realistic human skin in Substance Painter for a real time character!

In this 2 hour, fully narrated tutorial, you will learn the full process of me making this real time bust, from modeling to final rendering in Iray.

You will learn
- How to model ornaments for a medieval armor

- How to create photo realistic and artistic textures for metal armor material

- How to create a believable female face in Zbrush

- How to create high quality displacement for a human face

- How to paint human skin

- How to add believable imperfections to the skin

Note

Basic understanding of software used is recommended.

Contents
- 2 Hour Narrated Videos

- The bust OBJ

- Zbrush File

- Substance Painter File

- 1080p Resolution

- Stream Video Directly

Software Used
- Zbrush

- Mudbox

- Maya

- Mari

- Substance Painter

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Economic principles - a new way of learning



Economic principles - a new way of learning
Duration: 14h 16m | Video: .MP4 1280x720, 30 fps(r) | Audio: AAC, 48000Hz, 2ch | Size: 15.7 GB
Genre: eLearning | Language: English

5 countries: 5 economies: 2 competitions (Includes Economics English course)

What you'll learn
Feel a sense of intellectualism whenever they participate in a discussion about football!
Be able to argue for/against wanting to host the World Cup
See the 2018 World Cup in a whole new (economics) light!
Decluttering

Requirements
An interest in understanding world affairs - especially the economics behind the World Cup 2018!
A realisation that although France won the World Cup there will also be an economic impact in Russia, Croatia, UK - and America!
The role of penalties in international football


Description
Notice: Please do NOT enrol on this course on impulse, thinking you might watch it later. Maybe show an interest on impulse, yes, but before you enrol, watch the Preview videos, read the Course Description and then make a decision. If you then enrol then please start the course as soon as possible. Watch the lectures, look at the workbooks and join in the discussions. Joining my courses is a serious business and I want you to get the most out of your study - but I also want you to enjoy the course.

That is why I am asking that you only enrol because you really want to and that you start the course intending to make full use of all the resources.

You will be very welcome.

Five reasons to TAKE THIS COURSE right now:

You cannot lose - 30 day refund if you really really don't like the course BUT if you DO like it:

a. updated lectures

b. case studies based on very very current news items

c. detailed responses to messages

d. I constantly attempt to have a 'class atmosphere' e.g. throwing questions open to all

e. regular Announcements that are not disguised sales ploys

f. regular FREE access to numerous other courses offered by me

I am a lecturer/teacher in Economics by trade i.e. I teach full-time for a living. I have been Principal, Director of Studies and Head of Economics. (Oxford, UK) I have lectured in University, adult evening classes and also run over 50 seminars in the UK

I am/have been an Examiner for FIVE different examination boards

I have 100+ books on Amazon - many of them about business/economics

The success of my students has been featured on the BBC, Daily Telegraph, The Times and I have been featured on TV in 2 countries.

MOTIVATION

I make courses on Udemy primarily because I enjoy the process of causing learning. Many of my courses are to improve lives. One of the Economics courses is to raise money for charity. (100% of revenue goes to the charity) Fundamentally this course is about helping you.

About this course

This is an economics course about the economic impact of the 2018 World Cup. (Macro economics and micro economics) By basing the course around the World Cup we make the subject , relevant, approachable and Economics actually becomes.. easy!

We look at the impact of the World Cup on various countries but also consider the economic theory behind this impact.

Macro economics concepts include:

The multiplier

Inflation

Economic growth

Unemployment

International trade

Aggregate demand

Injections/leakages

Government economic policy

Balance of payments

Economies:

Russia

France

UK

USA

Croatia

We also apply game theory to penalty shoot-outs. (Football) The course starts off with the 2014 World Cup but quickly moves on to the impact of hosting the football tournament, of qualifying (or not) and actually winning he final.

We also consider controversies and.Pussy Riot!

Although the course is looking at the World Cup, make no mistake, this is all economics, economics, economics!

July 2019: Workbook added on Economics and decluttering

Chapters

Chapter 1 : The economics of decluttering

Chapter 2: Decluttering the company

Chapter 3: How to declutter like an Economist

Chapter 4: Buy less...

Chapter 5: The economics of tidying up

Chapter 6: Declutter your business

Chapter 7: Mistakes people make when decluttering

Chapter 8: The Kondo Effect: the economy-changing magic of tidying up

Topics:

Decluttering

Sunk costs

Costs of clutter

5S lean manufacturing

Lean management

Declutter your mail inbox

Declutter your home office

Wasting time and opportunities

Thinking like an economist

A rich life with less stuff

Minimalism

How much is enough

The art of letting go

Recycling

Status quo bias

Diminishing returns

Decluttering mistakes

Marie Kondo

Minimalism

Consumerism

Minimalism and economics: the endowment effect

30 December 2019

Workbook added on Economics 2020


Who this course is for:
Anyone who follows football and is interested in the economic impact of the 2018 World Cup - in theory and practice
Everyone who has/has not studied economics before - there is so much to learn here!
Someone who watched some/all of the World Cup and wondered 'What if..?'

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Udemy Build Motion Graphics and Animation with Adobe After Effects BOOKWARE-SOFTiMAGE



Udemy Build Motion Graphics and Animation with Adobe After Effects BOOKWARE-SOFTiMAGE | AEP,AI,MP4 | 2.35 GiB

2 857 kb/s 1280x720 | AAC 128 kb/s 2 CH



NFO:
File List:

File: 1. Introduction.mp4
Size: 27335781 bytes (26.07 MiB), duration: 00:01:16, avg.bitrate: 2877 kb/s
Audio: aac, 48000 Hz, stereo (und)
Video: h264, yuv420p, 1280x720, 30.00 fps(r) (und)






Download ( Size: 2.35 GiB ):
Filehosts: Nitroflare, Rapidgator




3 Link/s







3 Link/s

Udemy Adobe Photoshop for the Absolute Beginner Hands On Photoshop BOOKWARE-SOFTiMAGE



Udemy Adobe Photoshop for the Absolute Beginner Hands On Photoshop BOOKWARE-SOFTiMAGE | URL,ABR,CSS,EOT,EXE,GIF,JS,JSX,MP4,PDF,PSD,SVG,WOFF,ZXP | 8.67 GiB

2 094 kb/s 1280x720 | AAC 128 kb/s 2 CH



NFO:
File List:

File: 1. Get the Best Adobe Photoshop Latest Version.mp4
Size: 51889966 bytes (49.49 MiB), duration: 00:03:18, avg.bitrate: 2097 kb/s
Audio: aac, 44100 Hz, stereo (und)
Video: h264, yuv420p, 1280x720, 30.00 fps(r) (und)






Download ( Size: 8.67 GiB ):
Filehosts: Nitroflare, Rapidgator




5 Link/s









5 Link/s

Probability and Statistics 1: The Complete Guide



Probability and Statistics 1: The Complete Guide
Duration: 5h 52m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 7.27 GB
Genre: eLearning | Language: English

Learn everything fast through concise yet contented lectures

What you'll learn
representation and characterisation of data
elementary probability theory
combinatorics
rules for computing probabilities
conditional probability and independence of events
discrete random variables and their probability distributions
expectation value of a discrete random variable
examples of discrete random variables
moment generating functions
continuous random variables and their probability distributions
expectation value of a continuous random variable
examples of continuous random variables
Chebyshev's theorem
bivariate probability distributions
expectations of functions of random variables
covariance
independent random variables
sums of independent random variables
hypothesis testing

Requirements
Basic Calculus, particularly simple differentiation and integration


Description
I know, Probability and Statistics is difficult. But is there a way to make it easy? Of course. I for one managed that.

I know a lot of people struggle with it; a very small group of people are good at it. Back in university, I was in that bigger group, the group that struggled through Probability and Statistics lecture. I needed help; I couldn't understand a thing, but I finally found help and turned my exam result around. I guess since you're looking at this, you need help too.

This 6-hour COMPLETE GUIDE course contains everything you need to know to get started with Probability and Statistics. It's packed with videos that have been categorised into different topics, hence easy for you to learn.

I've included lots of definitions, theorems, quizzes, examples, concise notes for EVERY single section, exercises, and a walkthrough of all the exercise sheets. Most importantly, I've done a BONUS section for you! It includes some additional questions that will strengthen your skill even more.

With this basic Probability and Statistics course, you will have a good core understanding to pursue many more difficult Mathematics topic. In this course, everything has been broken down into a simple structure to make learning and understanding easy for you.

This COMPLETE guide is for those of you are looking to get a full understanding of the basics; the important parts. You've already shown half of your determination by looking at the course, so if this course sounds right for you, boost your eagerness to learn and join me on this journey!

Tips:

1) It will be very useful if you also take notes of your own as you're watching the lectures, it will help you understand everything better and quicker. Just pause if I move on to other topics too fast or if you haven't fully understood the previous sub-topic before you move on to the next parts.

2) Please ask any questions you may have in the Q&A section if you don't understand. It's one thing to not understand it, but it's a whole new experience and a very important thing to do when learning Maths to be able to discuss it with fellow students who are going through the same thing.

3) Use headphones for better sound. (I suggest you turn the volume up)

4) Don't forget you can always slow down or speed up the video!


Who this course is for:
high school students
university students
courses that require probability and statistics (e.g. computer science, and other sciences)

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Flight Mechanics - From Theory to Certification of Aircraft



Flight Mechanics - From Theory to Certification of Aircraft
MP4 | Video: h264, 1280x720 | Audio: AAC, 44100 Hz
Language: English | Size: 5.10 GB | Duration: 8h 55m



What you'll learn
Industry like experience on how to approach flight mechanics of airplanes, from design to certification
Aerodynamics that affect flight mechanics
Create and simulate a complete aircraft on Simulink
Understand how to demonstrate that an aircraft is safe
Stability and Control theory
Requirements
Basic Aerodynamics, Basic Engineering initial curriculum

Description
This course starts with a theoretical overview of 3D equations of motion applied for aircraft simulations. In this initial topic, a classical mechanics overview with a review of aerodynamics will provide the theoretical base for the second section.

On the second section we will cover Matlab & Simulink Intro and start the modelling of a generic aircraft. This final model will be used for trim analysis, simulation and can also be used for control law design, flight control sizing and design and many more.

Finally, in the last section we will cover the process for Part 25 aircraft certification. If you don't know what Part 25 is, don't worry, together we will learn all aspects of current regulations. We will cover all the way from understanding the requirements and how to demonstrate compliance with them up to the flight test campaign and maneuvers used in real aircraft certification.

This course is filled with real examples and is a more hands-on approach of a flight mechanics engineer.


Who this course is for:
Those who are looking to know more regarding Flight Mechanics
People interested in Engineering
Aviation enthusiasts in general
Those who want to work with Aerospace related Projects
Those who want to learn how the industry relates to Flight Mechanics - including certification
Those who I want to see a more applied approach in the topic, different from University
Aerospace Engineers
Mechanical Engineers


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Ramit Sethi - Earnable



Ramit Sethi - Earnable
MP4 + PDF Guides | Video: 1920x1080 | Audio: AAC 44.1Khz 2ch | Duration: 9 hours | Language: English | 8.6 GB

You CAN start your own business. One that pays *you* to live the life of your dreams. I'll show you how to go from "no idea" to profitable business, then expand to ongoing, automatic income with this all-in-one course.

Udemy - Deep Learning with Keras



Udemy - Deep Learning with Keras
Duration: 12h 57m | Video: .MP4 1280x720, 30 fps(r) | Audio: AAC, 44100 Hz, 2ch | Size: 11.6 GB
Genre: eLearning | Language: English

Deep Learning & Keras concepts, model, layers, modules. Build a Neural Network and Image Classification Model with Keras

What you'll learn
Introduction to Deep Learning and Neural Networks
Understand Deep Learning with Keras
Take a big step towards becoming a Deep Learning / Machine Learning engineer
Keras overview, features, benefits
Keras installation
Keras - Models, Layers and Modules
Keras Models - Sequential Model, Functional API
Keras Layers - Dense Layers, Dropout Layers, Convolution Layers, Pooling Layers
Keras Modules
Keras - Model Compilation, Evaluation and Prediction
Loss, Optimizer, Metrics, Compile the Model
Model Training, Model Evaluation, Model Prediction
Life-Cycle for Neural Network Models in Keras
Define Network, Compile Network, Fit Network, Evaluate Network, Make Predictions
Building your first Neural Network with Keras
Building a Multilayer Perceptron neural network
Building Image Classification Model with Keras
Convolutional Neural Network (CNN) & its layers

Requirements
Enthusiasm and determination to make your mark on the world!


Description
Keras is an open-source library of neural network components written in Python. Keras is capable of running atop TensorFlow, Theano, PlaidML and others. The library was developed to be modular and user-friendly. Keras enables fast experimentation through a high level, user-friendly, modular and extensible API. Keras can also be run on both CPU and GPU. Keras was developed and is maintained by Francois Chollet and is part of the TensorFlow core, which makes it TensorFlow preferred high-level API.

Comprised of a library of commonly used machine learning components including objectives, activation functions, and optimizers, Keras' open-source platform also offers support for recurrent and convolutional neural networks. Additionally, Keras offers mobile platform development for users intending to implement deep learning models on smartphones, both iOS and Android.

Keras is essentially an API designed for machine learning and deep learning engineers and follows best practices for reducing cognitive load. Keras offers consistent & simple APIs, minimizes the number of user actions required for common use cases, and provides clear & actionable error messages. It also supports extensive documentation and developer guides.

It is made user-friendly, extensible, and modular for facilitating faster experimentation with deep neural networks. It not only supports Convolutional Networks and Recurrent Networks individually but also their combination

Why do we need Machine Learning libraries such as Keras?

Machine learning uses a variety of math models and calculations to answer specific questions about data. Examples of machine learning in action include detecting spam emails, determining certain objects using computer vision, recognizing speech, recommending products, and even predicting commodities values years in the future.

The calculations implicit in machine learning and deep learning are very complicated to set up to ensure correct output (answers). A variety of machine learning libraries have emerged to help navigate these complexities. With these options, new folks can start getting into data science easily. Some of the most popular machine learning libraries include:

TensorFlow

Keras

sciKit learn

Theano

Microsoft Cognitive Toolkit (CNTK)

Uplatz provides this comprehensive course on Deep Learning with Keras. This Keras course will help you implement deep learning in Python, preprocess your data, model, build, evaluate and optimize neural networks. The Keras training will teach you how to use Keras, a neural network API written in Python. This Keras course will show how the full implementation is done in code using Keras and Python. You will learn how to organize data for training, build and train an artificial neural network from scratch, build and fine-tune convolutional neural networks (CNNs), implement fine-tuning and transfer learning, deploy models using both front-end and back-end deployment techniques.

Deep Learning with Keras - Course Syllabus

1. Introduction to Deep Learning & Keras

What is deep learning?

What is ANN?

Introduction to Keras

a) Overview of Keras

b) Features of Keras

c) Benefits of Keras

Keras Installation

2. Keras - Models, Layers and Modules

Keras Models

a) Sequential Model

b) Functional API

Keras Layers

a) Dense Layers

b) Dropout Layers

c) Convolution Layers

d) Pooling Layers

Keras Modules

3. Keras - Model Compilation, Evaluation and Prediction

Loss

Optimizer

Metrics

Compile the model

Model Training

Model Evaluation

Model Prediction

4. Life-Cycle for Neural Network Models in Keras

Define Network

Compile Network

Fit Network

Evaluate Network

Make Predictions

5. Building our first Neural Network with Keras

(Building a Multilayer Perceptron neural network)

Load Data

Define Keras Model

Compile Keras Model

Fit Keras Model

Evaluate Keras Model

Make Predictions

6. Building Image Classification Model with Keras

What is Image Recognition (Classification)

Convolutional Neural Network (CNN) & its layers

Building Image Classification Model (step by step)

Key Features of Keras

Keras is an API designed for humans

Focus on user experience has always been a major part of Keras

Large adoption in the industry

Highly Flexible

It is a multi backend and supports multi-platform, which helps all the encoders come together for coding

Research community present for Keras works amazingly with the production community

Easy to grasp all concepts

It supports fast prototyping

It seamlessly runs on CPU as well as GPU

It provides the freedom to design any architecture, which then later is utilized as an API for the project

It is really very simple to get started with

Easy production of models actually makes Keras special

Easy to learn and use


Who this course is for:
Deep Learning / Machine Learning Engineers
Machine Learning Researchers - NLP, Python, Deep Learning
Data Scientists and Machine Learning Scientists
Newbies and Beginners aspiring for a career in Machine Learning / Data Science / Deep Learning
Head of Engineering and Technical Leads
Anyone who wants to learn Deep Learning and Machine Learning
Computer Vision Researchers
AI Deep Learning Platform Leads
Senior ML and Deep Learning Scientists
Senior Data Consultants & Analytics Professionals
Product Managers
Artificial Intelligence Program Leads

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Deep Learning with TensorFlow (2021)



Deep Learning with TensorFlow (2021)
Duration: 29h 18m | Video: .MP4 1280x720, 30 fps(r) | Audio: AAC, 44100 Hz, 2ch | Size: 11.2 GB
Genre: eLearning | Language: English

TensorFlow concepts, components, pipeline, ANN, Classification, Regression, Object Identification, CNN, RNN, TensorBoard

What you'll learn
End-to-end knowledge of TensorFlow
TensorFlow concepts, development, coding, applications
TensorFlow components & pipelines
TensorFlow examples
Introduction to Python, Linear Algebra, Matplotlib, NumPy, Pandas
Introduction to Files
Introduction to Machine Learning
TensorFlow Playground & Perceptrons
TensorFlow and Artificial Intelligence
Building Artificial Neural Networks (ANN) with TensorFlow
Types of ANN and Components of Neural Networks
TensorFlow Classification and Linear Regression
TensorFlow vs. PyTorch vs. Theano vs. Keras
Object Identification in TensorFlow
TensorFlow Superkeyword
CNN & RNN, RNN Time Series
TensorBoard - TensorFlow's visualization toolkit

Requirements
Enthusiasm and determination to make your mark on the world!


Description
TensorFlow is an end-to-end open-source machine learning / deep learning platform. It has a comprehensive ecosystem of libraries, tools, and community resources that lets AI/ML engineers, scientists, analysts build and deploy ML-powered deep learning applications. The name TensorFlow is derived from the operations which neural networks perform on multidimensional data arrays or tensors. Deep learning is a subfield of machine learning that is a set of algorithms that is inspired by the structure and function of the brain.

TensorFlow is a machine learning framework that Google created and used to design, build, and train deep learning models. You can use the TensorFlow library do to numerical computations, which in itself doesn't seem all too special, but these computations are done with data flow graphs. In these graphs, nodes represent mathematical operations, while the edges represent the data, which usually are multidimensional data arrays or tensors, that are communicated between these edges.

In simple words, TensorFlow is an open-source and most popular deep learning library for research and production. TensorFlow in Python is a symbolic math library that uses dataflow and differentiable programming to perform various tasks focused on training and inference of deep neural networks. TensorFlow manages to combine a comprehensive and flexible set of technical features with great ease of use.

There have been some remarkable developments lately in the world of artificial intelligence, from much publicized progress with self-driving cars to machines now composing imitations or being really good at video games. Central to these advances are a number of tools around to help derive deep learning and other machine learning models, with Torch, Caffe, and Theano amongst those at the fore. However, since Google Brain went open source in November 2015 with their own framework, TensorFlow, the popularity of this software library has skyrocketed to be the most popular deep learning framework.

TensorFlow enables you to build dataflow graphs and structures to define how data moves through a graph by taking inputs as a multi-dimensional array called Tensor. It allows you to construct a flowchart of operations that can be performed on these inputs, which goes at one end and comes at the other end as output.

Top organizations such as Google, IBM, Netflix, Disney, Twitter, Micron, all use TensorFlow.

Uplatz provides this extensive course on TensorFlow. This TensorFlow course covers TensorFlow basics, components, pipelines to advanced topics like linear regression, classifier, create, train and evaluate a neural network like CNN, RNN, auto encoders etc. with TensorFlow examples.

The TensorFlow training is designed in such a way that you'll be able to easily implement deep learning project on TensorFlow in an easy and efficient way. In this TensorFlow course you will learn the fundamentals of neural networks and how to build deep learning models using TensorFlow. This TensorFlow training provides a practical approach to deep learning for software engineers. You'll get hands-on experience building your own state-of-the-art image classifiers and other deep learning models. You'll also use your TensorFlow models in the real world on mobile devices, in the cloud, and in browsers. Finally, you'll use advanced techniques and algorithms to work with large datasets. You will acquire skills necessary to start creating your own AI applications and models.

You'll master deep learning concepts and models using TensorFlow frameworks and implement deep learning algorithms, preparing you for a career as Deep Learning Engineer. Learn how to build a neural network and how to train, evaluate and optimize it with TensorFlow.

TensorFlow is completely based on Python. This course also provides a sound introduction to Python programming concepts, NumPy, Matplotlib, and Pandas so that you can acquire those skills in this course itself before moving on to learn the TensorFlow concepts. The aim of this TensorFlow tutorial is to describe all TensorFlow objects and method.

This TensorFlow course also includes a comprehensive description of TensorBoard visualization tool. You will gain an understanding of the mechanics of this tool by using it to solve a general numerical problem, quite outside of what machine learning usually involves, before introducing its uses in deep learning with a simple neural network implementation.

TensorFlow Architecture

TensorFlow architecture works in three parts:

Preprocessing the data

Build the model

Train and estimate the model

It is called TensorFlow because it takes input as a multi-dimensional array, also known as tensors. You can construct a sort of flowchart of operations (called a Graph) that you want to perform on that input. The input goes in at one end, and then it flows through this system of multiple operations and comes out the other end as output.

This is why it is called TensorFlow because the tensor goes in it flows through a list of operations, and then it comes out the other side.

TensorFlow - Course Syllabus


Who this course is for:
Machine Learning & Deep Learning Engineers
Data Scientists & Senior Data Scientists
Beginners and newbies aspiring for a career in Machine Learning / Deep Learning
Data Analysts & Advanced Data Analytics Professionals
TensorFlow Engineers
Machine Learning Developers - TensorFlow/Hadoop
Software Developers - AI/ML/Deep Learning
Anyone wishing to learn TensorFlow algorithms and applications
Deep Learning Engineers - Python/TensorFlow
Artificial Intelligence Engineers and Senior ML/DL Engineers
Researchers and PhD students
Data Engineers
AI & RPA Developers - TensorFlow/ML
AI/ML Developers
Machine Learning Leads & Enthusiasts
TensorFlow and Advanced ML Developers

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Brian Moran - The Facebook Ads Academy



Brian Moran - The Facebook Ads Academy
MP4+ MP3 + PDF guides | Video: 960x540 | Audio: AAC 44.1 Khz , 2ch | Duration: 9 hours | Language: English | 5.8 GB

Get the No B.S. secrets behind a $17,000 per day campaign.And tap into an unlimited source of hyper-targeted traffic so you can rapidly scale your sales.
Exclusive Webinar-Only Bonuses!
As promised we are also throwing in over $7,676 in bonuses, 100% FREE when you enroll today.

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Azure Database Administrator Associate



Azure Database Administrator Associate
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 82 Lessons (10h 6m) | Size: 6.2 GB

Use this course to prepare for the DP-300: Administering Relational Databases on Microsoft Azure exam This course prepares students who are already familiar with core Azure services and Azure and SQL database skills used by Azure Database Administrators.

An Azure Database Administrator is responsible for the management, availability, security, performance monitoring, and optimization of modern relational database solutions. They also assist Azure Data Engineers in managing operational aspects of a data platform solution. Azure Database Administrators are familiar with:

Deploying SQL database solutions within Azure
Azure SQL Server, database, and query architecture
Basic usage of Transact-SQL for administrative tasks
Securing Azure SQL resources and data
Interconnecting Azure SQL resources with other Azure services
Monitoring and optimizing Azure SQL Database performance
Maintaining business continuity for Azure SQL resources
Processes for migrating on-premises SQL Server databases to Azure
Other options for managing relational data in Azure
This course also prepares exam candidates to pass the DP-300: Administering Relational Databases on Microsoft Azure exam to attain the Azure Database Administrator Associate certification. Candidates for the exam are presumed to be database administrators and data management specialists that manage on-premises and cloud relational databases built on top of Microsoft SQL Server and Microsoft Azure data services.

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