Artificial Intelligence (AI) is currently the hottest buzzword in tech. Here is a video on the role of Artificial Intelligence and its scope in the future. We have put together the best clips on Artificial Intelligence by the most well known leaders and influencers such as Bill Gates, Tim Cook, Elon Musk, Sundar Pichai and Jeff Bezos. The last few years have seen a number of techniques that have previously been in the realm of science fiction slowly transform into reality. We have brought to you the business leaders of today speaking about artificial intelligence, what is fascinating about AI, the latest AI projects and what’s in store for the future of AI. We will also answer the question whether AI will someday overpower us humans. According to the report How AI Boosts Industry Profits and Innovations, AI is predicted to increase economic growth by an average of 1.7 percent across 16 industries by 2035. The report goes on to say that, by 2035, AI technologies could increase labor productivity by 40 percent or more, there by doubling economic growth in 12 developed nations that continue to draw talented and experienced professionals to work in this domain. Let us see what our business leaders have to say about this.
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Simplilearn’s Artificial Intelligence course provides training in the skills required for a career in AI. You will master TensorFlow, Machine Learning and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems without explicit programming.
Why learn Artificial Intelligence?
The current and future demand for AI engineers is staggering. The New York Times reports a candidate shortage for certified AI Engineers, with fewer than 10,000 qualified people in the world to fill these jobs, which according to Paysa earn an average salary of $172,000 per year in the U.S. (or Rs.17 lakhs to Rs. 25 lakhs in India) for engineers with the required skills.
You can gain in-depth knowledge of Artificial Intelligence by taking our Artificial Intelligence certification training course. Those who complete the course will be able to:
1. Master the concepts of supervised and unsupervised learning
2. Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Comprehend the theoretical concepts and how they relate to the practical aspects of machine learning.
6. Be able to model a wide variety of robust machine learning algorithms including deep learning, clustering, and recommendation systems
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It’s hard not to argue that cryptocurrencies represent the future of online transactions. And although slow, merchants and customers are starting to warm up towards cryptocurrencies. There’s still a long way to go before widespread adoption, though! Let’s get into the video and find out why we think cryptocurrencies will rule in 2020!
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Watch more videos on cryptocurrencies and Blockchain: https://www.youtube.com/playlist?list=PLEiEAq2VkUUKmhU6SO2P73pTdMZnHOsDB
#Cryptocurrency #Cryptocurrecncies2020 #CryptocurrencyExplained #Blockchain #Bitcoin #BlockchainTechnology #Simplilearn
Simplilearn’s Blockchain Certification Training has been designed for developers who want to decipher the global craze surrounding Blockchain, Bitcoin and cryptocurrencies. You’ll learn the core structure and technical mechanisms of Bitcoin, Ethereum, Hyperledger and Multichain Blockchain platforms, use the latest tools to build Blockchain applications, set up your own private Blockchain, deploy smart contracts on Ethereum and gain practical experience with real-world projects.
Why learn Blockchain?
Blockchain technology is the brainchild of Satoshi Nakamoto, which enables digital information to be distributed. A network of computing nodes makes up the Blockchain. Durability, robustness, success rate, transparency, incorruptibility are some of the enticing characteristics of Blockchain. By design, Blockchain is a decentralized technology which is used by a global network of the computer to manage Bitcoin transactions easily. Many new business applications will result in the usage of Blockchain such as Crowdfunding, smart contracts, supply chain auditing, Internet of Things(IoT), etc.
This Blockchain Certification course offers a hands-on training covering relevant topics in cryptocurrency and the wider Blockchain space. From a technological standpoint, you will develop a strong grasp of core Blockchain platforms, understand what Bitcoin is and how it works, learn key vocabulary and concepts commonly used when discussing Blockchain and understand why engineers are motivated to create an app with Ethereum. Hands-on exercises and projects will give you practical experience in real-world Blockchain development scenarios.
After completing this course, you will be able to:
1. Apply Bitcoin and Blockchain concepts in business situations
2. Build compelling Blockchain applications using the Ethereum Blockchain
3. Design, test and deploy secure Smart Contracts
4. Use the latest version of Ethereum development tools (Web3 v1.0)
5. Develop Hyperledger Blockchain applications using Composer Framework
6. Model the Blockchain applications using Composer modeling language
7. Develop front-end (client) applications using Composer API
8. Leverage Composer REST Server to design a web-based Blockchain solution
9. Design Hyperledger Fabric Composer Business Network 10.
10. Understand the true purpose and capabilities of Ethereum and Solidity
11. See practical examples of Blockchain and mining
12. Describe the various components of Hyperledger Fabric Technology (Peers, Orderer, MSP, CA)
The Blockchain Certification Training Course is recommended for:
1. Developers
2. Technologists interested in learning Ethereum, Hyperledger and Blockchain
3. Technology architects wanting to expand their skills to Blockchain technology
4. Professionals curious to learn how Blockchain technology can change the way we do business
5. Entrepreneurs with technology background interested in realizing their business ideas on the Blockchain
6. Anyone interested in ERC20 tokens and ICOs
Learn more at: https://www.simplilearn.com/blockchain-certification-training?utm_campaign=Cryptocurrency-Predictions-2020-L8chuTdC65I&utm_medium=Tutorials&utm_source=youtube
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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)
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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
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This Machine Learning basics video will help you understand what is Machine Learning, what are the types of Machine Learning – supervised, unsupervised & reinforcement learning, how Machine Learning works with simple examples, and will also explain how Machine Learning is being used in various industries. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. This is possible as programs learn from previous computations and use “pattern recognition” to produce reliable results. Machine learning is starting to reshape how we live, and it’s time we understood what it is and why it matters. Now, let us deep dive into this short video and understand the basics of Machine Learning.
Below topics are explained in this Machine Learning basics video:
1. What is Machine Learning? ( 00:21 )
2. Types of Machine Learning ( 02:43 )
2. What is Supervised Learning? ( 02:53 )
3. What is Unsupervised Learning? ( 03:46 )
4. What is Reinforcement Learning? ( 04:37 )
5. Machine Learning applications ( 06:25 )
Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1
Download the Machine Learning Career Guide to explore and step into the exciting world of Machine Learning, and follow the path towards your dream career- https://www.simplilearn.com/machine-learning-career-guide-pdf?utm_campaign=Machine-Learning-Basics-ukzFI9rgwfU&utm_medium=Tutorials&utm_source=youtube
Watch more videos on Machine Learning: https://www.youtube.com/watch?v=7JhjINPwfYQ&list=PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy
#MachineLearning #MachineLearningAlgorithms #DataScience #SimplilearnMachineLearning #MachineLearningCourse
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with the knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire a thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Machine-Learning-Basics-ukzFI9rgwfU&utm_medium=Tutorials&utm_source=youtube
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Don’t forget to take the quiz at 04:26
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This video on “What is Deep Learning” provides a fun and simple introduction to its concepts. We learn about where Deep Learning is implemented and move on to how it is different from machine learning and artificial intelligence. We will also look at what neural networks are and how they are trained to recognize digits written by hand. We further look at some popular applications of Deep Learning. So, let’s dive into the world of Deep Learning with this video.
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Watch more videos on Deep Learning: https://www.youtube.com/watch?v=FbxTVRfQFuI&list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip
#DeepLearning #WhatIsDeepLearning #DeepLearningTutorial #DeepLearningCourse #DeepLearningExplained #Simplilearn
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. 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=What-Is-Deep-Learning-6M5VXKLf4D4&utm_medium=Tutorials&utm_source=youtube
For more information about Simplilearn’s courses, visit:
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Natural language processing (NLP) is a field of computer science, artificial intelligence and computational linguistics concerned with the interactions between computers and human (natural) languages, and, in particular, concerned with programming computers to fruitfully process large natural language corpora.
Python for Data Science Certification Training Course: https://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training?utm_campaign=Data-Science-NLP-6WpnxmmkYys&utm_medium=Tutorials&utm_source=youtube
Download the Machine Learning Career Guide to explore and step into the exciting world of Machine Learning, and follow the path towards your dream career- https://www.simplilearn.com/machine-learning-career-guide-pdf?utm_campaign=Data-Science-NLP-6WpnxmmkYys&utm_medium=Tutorials&utm_source=youtube
The Data Science with Python course is designed to impart an in-depth knowledge of the various libraries and packages required to perform data analysis, data visualization, web scraping, machine learning, and natural language processing using Python. The course is packed with real-life projects, assignment, demos, and case studies to give a hands-on and practical experience to the participants.
Mastering Python and using its packages: The course covers PROC SQL, SAS Macros, and various statistical procedures like PROC UNIVARIATE, PROC MEANS, PROC FREQ, and PROC CORP. You will learn how to use SAS for data exploration and data optimization.
Mastering advanced analytics techniques: The course also covers advanced analytics techniques like clustering, decision tree, and regression. The course covers time series, it’s modeling, and implementation using SAS.
As a part of the course, you are provided with 4 real-life industry projects on customer segmentation, macro calls, attrition analysis, and retail analysis.
Who should take this course?
There is a booming demand for skilled data scientists across all industries that make this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals:
1. Analytics professionals who want to work with Python
2. Software professionals looking for a career switch in the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in Analytics and Data Science
5. Experienced professionals who would like to harness data science in their fields
6. Anyone with a genuine interest in the field of Data Science
For more updates on courses and tips follow us on:
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This video on “How to become a Artificial Intelligence Engineer” will explain the skills that one should master to become successful in his Artificial Intelligence career. This video will also explain about the job opportunities in AI industry along with the Artificial Intelligence course offered by Simplilearn and how one can get benefited from these courses. Artificial Intelligence industry is growing at a rapid speed. we can’t even imagine what will develop, but we do know we already have a shortage of trained AI and machine learning professionals, and that gap will only grow until we get people trained and placed in the millions of AI jobs. If you want to be one of those professionals, get certified, because the sooner you get your training started, the sooner you will be working in this exciting and rapidly changing field. Now let us get started and understand the steps to become an AI engineer.
We will be talking about the below topics in this video:
1. What is Artificial Intelligence
2. Who is an Artificial Intelligence engineer
3. Responsibilities of an AI engineer
4. Skills to become an AI engineer
– Programming skills
– Liner algebra, probability and statistics
– Knowledge on Spark and Big Data technologies
– Algorithms and frameworks
– Communication and problem solving skills
5. AI engineer’s salary
6. Career and roles in Artificial Intelligence
7. Simplilearn AI engineer course
To learn more about Artificial Intelligence, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1
Download the Artificial Intelligence Career Guide and take a sneak peek into the world that awaits you: https://www.simplilearn.com/artificial-intelligence-career-guide-pdf?utm_campaign=How-To-Become-An-Artificial-Intelligence-Engineer-uBV0w8Qwhv4&utm_medium=Tutorials&utm_source=youtube
To access slides, click here: https://www.slideshare.net/Simplilearn/how-to-become-an-artificial-intelligence-engineer-ai-engineer-career-path-and-skills-simplilearn/Simplilearn/how-to-become-an-artificial-intelligence-engineer-ai-engineer-career-path-and-skills-simplilearn
Watch more videos on Artificial Intelligence: https://www.youtube.com/playlist?list=PLEiEAq2VkUULg2pAmFCfrpSXPHmNP6Map
#AIEngineerCareerPath #AritificialIntelligence #HowToBecomeAnAIEngineer #AIEngineer#AritificialIntelligenceCourse #AITutorial #AITutorialForBeginners #AI #Simplilearn
About Simplilearn Artificial Intelligence course:
Simplilearns’ Introduction to Artificial Intelligence course is designed to help learners decode the mystery of artificial intelligence and its business applications. The course provides an overview of AI concepts and workflows, machine learning and deep learning, and performance metrics. You’ll learn the difference between supervised, unsupervised and reinforcement learning; be exposed to use cases, and see how clustering and classification algorithms help identify AI business applications.
What are the career benefits of this Introduction to AI course?
Artificial intelligence has become a powerful driving force in a wide range of industries, helping people and businesses create exciting, innovative products and services, enable more informed business decisions, and achieve key performance goals.
What are the course objectives?
The Introduction to Artificial Intelligence course will give you a look at the booming field of AI and show you how AI can help drive business value. The course covers basic concepts, terminologies, scope and stages of artificial intelligence and their effect on real-world business processes. By the end of the course, you will be able to clearly define various supervised and unsupervised AI algorithms, apply machine learning workflow to solve business problems and measure ROI based on performance metrics.
What skills will you learn from this Introduction to Artificial Intelligence course?
Upon completion of this course, you will understand:
1. The meaning, purpose, scope, stages, applications and effects of AI
2. Fundamental concepts of machine learning and deep learning
3. The difference between supervised, semi-supervised and unsupervised learning
4. Machine Learning workflow and how to implement the steps effectively
Learn more at: https://www.simplilearn.com/artificial-intelligence-masters-program-training-course?utm_campaign=How-To-Become-An-Artificial-Intelligence-Engineer-uBV0w8Qwhv4&utm_medium=Tutorials&utm_source=youtube
For more updates on courses and tips follow us on:
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Get the Android app: http://bit.ly/1WlVo4u
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This video on Deep Learning with Python will help you understand what is deep learning, applications of deep learning, what is a neural network, biological versus artificial neural networks, introduction to TensorFlow, activation function, cost function, how neural networks work, and what gradient descent is. Deep learning is a technology that is used to achieve machine learning through neural networks. We will also look into how neural networks can help achieve the capability of a machine to mimic human behavior. We’ll also implement a neural network manually. Finally, we’ll code a neural network in Python using TensorFlow.
Below topics are explained in this Deep Learning with Python tutorial:
1. What is Deep Learning (01:56)
2. Biological versus Artificial Intelligence (02:45)
3. What is a Neural Network (04:09)
4. Activation function (08:49)
5. Cost function (14:08)
6. How do Neural Networks work (16:05)
7. How do Neural Networks learn (18:58)
8. Implementing the Neural Network (20:26)
9. Gradient descent (23:21)
10. Deep Learning platforms (24:48)
11. Introduction to TensoFlow (26:00)
12. Implementation in TensorFlow (28:56)
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-with-python-deep-learning-and-neural-networks-deep-learning-tutorial-simplilearn/Simplilearn/deep-learning-with-python-deep-learning-and-neural-networks-deep-learning-tutorial-simplilearn
Watch more videos on Deep Learning: https://www.youtube.com/watch?v=FbxTVRfQFuI&list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip
#DeepLearningWithPython #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.
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-with-Python-fcD6YeEYKNg&utm_medium=Tutorials&utm_source=youtube
For more information about Simplilearn’s courses, visit:
– Facebook: https://www.facebook.com/Simplilearn
– Twitter: https://twitter.com/simplilearn
– LinkedIn: https://www.linkedin.com/company/simplilearn/
– Website: https://www.simplilearn.com
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This Deep Learning tutorial is designed for beginners who want to learn Deep Learning from scratch. We will look at where Deep Learning is applied and what exactly this term means. We’ll see how Deep Learning, Machine Learning, and AI are different and why Deep Learning even came into the picture. We will then proceed to look at Neural Networks, which are the core of Deep Learning. Before we move into the working of Neural Networks, we’ll cover activation and cost functions. The video will also introduce you to the most popular Deep Learning platforms. We wrap it up with a demo in TensorFlow to predict if a person receives a salary above or below 50k. Now, let us get started and understand Deep Learning in detail.
Below topics are explained in this Deep Learning tutorial:
1. Applications of Deep Learning
2. What is Deep Learning
3. Why is Deep Learning important
4. What are Neural Networks
5. Activation function
6. Cost function
7. How do Neural Networks work
8. Deep Learning platforms
9. Introduction to TensorFlow
10. Use case implementation using TensorFlow
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Watch more videos on Deep Learning: https://www.youtube.com/watch?v=FbxTVRfQFuI&list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip
#DeepLearningTutorial #DeepLearning #DeepLearningAndNeuralNetworks #WhatIsDeepLearning#DeepLearningCourse #Simplilearn
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. 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-Tutorial-Cq_P8kJgjvI&utm_medium=Tutorials&utm_source=youtube
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This Artificial Intelligence video will introduce you to the AI world and give you a short glimpse about AI history. By the end of this video, you will learn what is Artificial intelligence, types of Artificial Intelligence, applications of Artificial Intelligence and future of Artificial Intelligence.
The topics covered in this video are as follows:
1. Brief History of Artificial Intelligence (00:58)
2. What is Artificial intelligence (03:15)
3. Types of Artificial Intelligence (04:05)
4. Applications of Artificial Intelligence(07:01)
5. Future of Artificial Intelligence(08:35)
You can also go through the Slides here: https://goo.gl/qBDF8c
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To learn more about Artificial Intelligence, subscribe to our YouTube channel: youtube.com/c/SimplilearnOfficial
#ArtificialIntelligence #Simplilearn #AI #AIIn10Minutes #MachineLearning
Simplilearn’s Artificial Intelligence course provides training in the skills required for a career in AI. You will master TensorFlow, Machine Learning and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems without explicit programming.
Why learn Artificial Intelligence?
The current and future demand for AI engineers is staggering. The New York Times reports a candidate shortage for certified AI Engineers, with fewer than 10,000 qualified people in the world to fill these jobs, which according to Paysa earn an average salary of $172,000 per year in the U.S. (or Rs.17 lakhs to Rs. 25 lakhs in India) for engineers with the required skills.
You can gain in-depth knowledge of Artificial Intelligence by taking our Artificial Intelligence certification training course. Those who complete the course will be able to:
Master the concepts of supervised and unsupervised learning
Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach which includes working on 28 projects and one capstone project.
Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning.
Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
Comprehend the theoretical concepts and how they relate to the practical aspects of machine learning.
Be able to model a wide variety of robust machine learning algorithms including deep learning, clustering, and recommendation systems
Learn more at: https://www.simplilearn.com/artificial-intelligence-masters-program-training-course
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This Machine Learning vs Deep Learning vs Artificial Intelligence video will help you understand the differences between ML, DL and AI, and how they are related to each other. The tutorial video will also cover what Machine Learning, Deep Learning and Artificial Intelligence entail, how they work with the help of examples, and whether they really are all that different.
This Machine Learning Vs Deep Learning Vs Artificial Intelligence video will explain the topics listed below:
1. Artificial Intelligence example ( 00:29 )
2. Machine Learning example ( 01:29 )
3. Deep Learning example ( 01:44 )
4. Human vs Artificial Intelligence ( 03:34 )
5. How Machine Learning works ( 06:11 )
6. How Deep Learning works ( 07:09 )
7. AI vs Machine Learning vs Deep Learning ( 12:33 )
8. AI with Machine Learning and Deep Learning ( 13:05 )
9. Real-life examples ( 15:29 )
10. Types of Artificial Intelligence ( 17:50 )
11. Types of Machine Learning ( 20:32 )
12. Comparing Machine Learning and Deep Learning ( 22:46 )
13. A glimpse into the future ( 25:46 )
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Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=What-is-Machine-Learning-7JhjINPwfYQ&utm_medium=Tutorials&utm_source=youtube
To gain in-depth knowledge of Machine Learning, Deep learning and Artificial Intelligence, Check out our Artificial Intelligence Engineer Program: https://www.simplilearn.com/artificial-intelligence-masters-program-training-course?utm_campaign=Machine-Learning-Vs-Deep-Learning-Vs-Artificial-Intelligence-9dFhZFUkzuQ&utm_medium=Tutorials&utm_source=youtube
You can also go through the Slides here: https://goo.gl/cdQ7uy
#SimplilearnMachineLearning #SimplilearnAI #SimplilearnDeepLearning #Artificialintelligence #MachineLearningTutorial
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About Simplilearn Artificial Intelligence Engineer course:
What are the learning objectives of this Artificial Intelligence Course?
By the end of this Artificial Intelligence Course, you will be able to accomplish the following:
1. Design intelligent agents to solve real-world problems which are search, games, machine learning, logic constraint satisfaction problems, knowledge-based systems, probabilistic models, agent decision making
2. Master TensorFlow by understanding the concepts of TensorFlow, the main functions, operations and the execution pipeline
3. Acquire a deep intuition of Machine Learning models by mastering the mathematical and heuristic aspects of Machine Learning
4. Implement Deep Learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
5. Comprehend and correlate between theoretical concepts and practical aspects of Machine Learning
6. Master and comprehend advanced topics like convolutional neural networks, recurrent neural networks, training deep networks, high-level interfaces
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What skills will you learn with our Masters in Artificial Intelligence Program?
1. Learn about major applications of Artificial Intelligence across various use cases in various fields like customer service, financial services, healthcare, etc
2. Implement classical Artificial Intelligence techniques such as search algorithms, neural networks, tracking
3. Ability to apply Artificial Intelligence techniques for problem-solving and explain the limitations of current Artificial Intelligence techniques
4. Formalise a given problem in the language/framework of different AI methods such as a search problem, as a constraint satisfaction problem, as a planning problem, etc
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