** Edureka Python Certification Training: https://www.edureka.co/python **
This Edureka video on ‘Robot Framework With Python’ explains the various aspects of robot framework in python with a use case showing web testing using selenium library. Following are the topics discussed in this Robot Framework Tutorial:

What is Robot Framework In Python?
Standard Libraries
Built-in Tools
Test Cases
Keywords
Variables
Organizing Test Cases
Use Case – RobotFramework-SeleniumLibrary

Python Tutorial Playlist: https://goo.gl/WsBpKe
Blog Series: http://bit.ly/2sqmP4s

#PythonEdureka #Edureka #robotframeworkinpython #pythonprojects #pythonprogramming #pythontutorial #PythonTraining

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How it Works?
1. This is a 5 Week Instructor-led Online Course,40 hours of assignment and 20 hours of project work
2. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training, you will be working on a real-time project for which we will provide you a Grade and a Verifiable Certificate!

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About the Course

Edureka’s Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you:

1. Master the Basic and Advanced Concepts of Python
2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs
3. Master the Concepts of Sequences and File operations
4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python
5. Gain expertise in machine learning using Python and build a Real Life Machine Learning application
6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn
7. Master the concepts of MapReduce in Hadoop
8. Learn to write Complex MapReduce programs
9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python
10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics
11. Master the concepts of Web scraping in Python
12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience
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Why learn Python?

Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations.
Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next “Big Thing” and a must for Professionals in the Data Analytics domain.

Who should go for python?

Edureka’s Data Science certification course in Python is a good fit for the below professionals:

· Programmers, Developers, Technical Leads, Architects

· Developers aspiring to be a ‘Machine Learning Engineer’

· Analytics Managers who are leading a team of analysts

· Business Analysts who want to understand Machine Learning (ML) Techniques

· Information Architects who want to gain expertise in Predictive Analytics

· ‘Python’ professionals who want to design automatic predictive models

For more information, Please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll free

This Deep Learning tutorial covers all the essential Deep Learning frameworks that are necessary to build AI models. In this video, you will learn about the development of essential frameworks such as TensorFlow, Keras, PyTorch, Theano, etc. You will also understand the programming languages used to build the frameworks, the different companies that use these frameworks, the characteristics of these Deep Learning frameworks, and type of models that were built using these frameworks. Now, let us get started with understanding the different popular Deep Learning frameworks being used in industries.

Below are the different Deep Learning frameworks we’ll be discussing in this video:
1. TensorFlow (01:28)
2. Keras (02:54)
3. PyTorch (05:02)
4. Theano (06:30)
5. Deep Learning 4 Java (07:55)
6. Caffe (09:51)
7. Chainer (11:29)
8. Microsoft CNTK (13:48)

To learn more about Deep Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1

To access the slides, click here: https://www.slideshare.net/Simplilearn/deep-learning-frameworks-2019-which-deep-learning-framework-to-use-deep-learning-simplilearn/Simplilearn/deep-learning-frameworks-2019-which-deep-learning-framework-to-use-deep-learning-simplilearn

Watch more videos on Deep Learning: https://www.youtube.com/watch?v=FbxTVRfQFuI&list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip

#DeepLearningFrameworks #DeepLearningTutorial #DeepLearning #Datasciencecourse #DataScience #SimplilearnMachineLearning #DeepLearningCourse

Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.

Why Deep Learning?

It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.

And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.

You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:

1. Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning and artificial intelligence

There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:

1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning

Learn more at: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=Deep-Learning-Frameworks-2019-6ryPbOfz03U&utm_medium=Tutorials&utm_source=youtube

For more information about Simplilearn’s courses, visit:
– Facebook: https://www.facebook.com/Simplilearn
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– Website: https://www.simplilearn.com

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Winter Intelligence Oxford http://winterintelligence.org – Organized by the Future of Humanity Institute http://fhi.ox.ac.uk/ – AGI12 – http://agi-conference.org/2012
==The Next Generation of the MicroPsi Framework== Joscha Bach, Humboldt University of Berlin, Germany

( ** AI & Deep Learning with Tensorflow Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** )
This Edureka video on “Deep Learning Frameworks” (https://goo.gl/27nAwR) provides you an insight into the top 8 Deep Learning frameworks you should consider learning

00:38 Chainer
01:40 CNTK
03:12 Caffe
04:46 MXNet
05:55 Deeplearning4j
07:42 Keras
09:21 PyTorch
10:45 TensorFlow
12:10 Conclusion

Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV

Check out our Deep Learning blog series: https://bit.ly/2xVIMe1
Check out our complete Youtube playlist here: https://bit.ly/2OhZEpz

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#PyTorch #TensorFlow #DeepLearning #Python
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Got a question on the topic?
Please share it in the comment section below and our experts will answer it for you.

About the Course
Edureka’s Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.

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Who should go for this course?

The following professionals can go for this course:

1. Developers aspiring to be a ‘Data Scientist’

2. Analytics Managers who are leading a team of analysts

3. Business Analysts who want to understand Deep Learning (ML) Techniques

4. Information Architects who want to gain expertise in Predictive Analytics

5. Professionals who want to captivate and analyze Big Data

6. Analysts wanting to understand Data Science methodologies

However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.

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Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.

Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.

How it Works?

1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate!

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Got a question on the topic? Please share it in the comment section below and our experts will answer it for you.

For more information, please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free).

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