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AI vs Machine Learning vs Deep Learning | Machine Learning Training with Python | Edureka

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AI vs Machine Learning vs Deep Learning | Machine Learning Training with Python | Edureka

???? NIT Warangal Post Graduate Program on AI and Machine Learning:
This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: ) on AI vs Machine Learning vs Deep Learning talks about the differences and relationship between AL, Machine Learning and Deep Learning. Below are the topics covered in this tutorial:

1. AI vs Machine Learning vs Deep Learning
2. What is Artificial Intelligence?
3. Example of Artificial Intelligence
4. What is Machine Learning?
5. Example of Machine Learning
6. What is Deep Learning?
7. Example of Deep Learning
8. Machine Learning vs Deep Learning

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Machine Learning Tutorial Blog list:

------------Edureka Training and Certifications-----------

???? Machine Learning Course using Python:

???? Machine Learning Engineer Masters Program:

????Python Masters Program:

???? Deep Learning using TensorFlow:

???? PG in Artificial Intelligence and Machine Learning with NIT Warangal :

???? Post Graduate Certification in Data Science with IIT Guwahati -
(450+ Hrs || 9 Months || 20+ Projects & 100+ Case studies)

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#edureka #AIvsMLvsDL #PythonTutorial #PythonMachineLearning #PythonTraining

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 24x7 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!

- - - - - - - - - - - - - - - - -

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
- - - - - - - - - - - - - - - - - - -

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.

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.

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

Customer Review

Sairaam Varadarajan, Data Evangelist at Medtronic, Tempe, Arizona: I took Big Data and Hadoop / Python course and I am planning to take Apache Mahout thus becoming the customer of Edureka!. Instructors are knowledge... able and interactive in teaching. The sessions are well structured with a proper content in helping us to dive into Big Data / Python. Most of the online courses are free, edureka charges a minimal amount. Its acceptable for their hard-work in tailoring - All new advanced courses and its specific usage in industry. I am confident that, no other website which have tailored the courses like Edureka. It will help for an immediate take-off in Data Science and Hadoop working.
x

Deep Learning Vs Machine Learning | AI Vs Machine Learning Vs Deep Learning

Deep Learning Vs Machine Learning | AI Vs Machine Learning Vs Deep Learning

Hello and welcome to Acadgild’s tutorial on data science.
In this video, we explain the difference between three key concepts artificial intelligence vs machine learning vs deep learning – to understand how they relate to the field of data science.

First up, artificial intelligence or AI! What is it?
Artificial intelligence is simply any code, technique or algorithm that enables machines to mimic, develop and demonstrate human cognition or behavior.

We are in, what many refer to as, the era of “weak AI”. The technology is still in its infancy and is expected to make machines capable of doing anything and everything humans do, in the era of “strong AI”.
To transition from weak AI to strong AI, machines need to learn the ways of humans. The techniques and processes, which help machines in this endeavor are broadly categorized under machine learning.
Machines learn in predominantly two ways. Their learning is either supervised or unsupervised.
In supervised learning, machines learn to predict outcomes with help from data scientists.
In unsupervised learning, machines learn to predict outcomes on the go by recognizing patterns in input data.

When machines can draw meaningful inferences from large volumes of data sets, they demonstrate the ability to learn deeply.
Deep learning requires artificial neural networks (ANNs), which are like the biological neural networks in humans. These networks contain nodes in different layers that are connected and communicate with each other to make sense of voluminous input data.

Deep learning is a subset of machine learning, which in turn, is a subset of artificial intelligence.
The three technologies help scientists and analysts interpret tons of data and are hence crucial for the field of data science.
To learn more about these technologies, subscribe to Acadgild’s blog and Youtube channel. To become an expert, join one of our courses.
Thank you for watching and happy learning!

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AI VS ML VS DL VS Data Science

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AI Show - Deep Learning vs. Machine Learning

This episode helps you compare deep learning vs. machine learning. You'll learn how the two concepts compare and how they fit into the broader category of artificial intelligence. During this demo we will also describes how deep learning can be applied to real-world scenarios such as fraud detection, voice and facial recognition, sentiment analytics, and time series forecasting.

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Introduction to Deep Learning: Machine Learning vs Deep Learning

Learn about the differences between deep learning and machine learning in this MATLAB® Tech Talk.
- MATLAB for Deep Learning:

Walk through several examples, and learn how to decide which method to use.
Learn more about Deep Learning:
Download a trial:

The video outlines the specific workflow for solving a machine learning problem.

The video also outlines the differing requirements for machine learning and deep learning. You’ll learn about the key questions to ask before deciding between machine learning and deep learning.

The choice between machine learning or deep learning depends on your data and the problem you’re trying to solve. MATLAB can help you with both of these techniques – either separately or as a combined approach.

Artificial Intelligence Vs Machine Learning Vs Data science Vs Deep learning | Applied AI Course

For More information Please visit

#ArtificialIntelligence,#MachineLearning,#DeepLearning,#DataScience,#NLP,#AI,#ML

Live - 1 | Artificial Intelligence vs Machine Learning vs Deep Learning | AI vs ML vs DL | Edureka

????Edureka NIT Warangal Post Graduate Program on AI and Machine Learning:
This Edureka Machine Learning tutorial on AI vs Machine Learning vs Deep Learning talks about the differences and relationship between AI, Machine Learning and Deep Learning.

????Subscribe to our channel to get video updates. Hit the subscribe button above:

------------Edureka Training and Certifications-----------

???? Machine Learning Course using Python:

???? Machine Learning Engineer Masters Program:

???? Deep Learning using TensorFlow:

???? PG in Artificial Intelligence and Machine Learning with NIT Warangal :

???? Post Graduate Certification in Data Science with IIT Guwahati -
(450+ Hrs || 9 Months || 20+ Projects & 100+ Case studies)

- - - - - - - - - - - - - - - - -

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#edureka #AIvsMLvsDL #ArtificialIntelligence #MachineLearning #DeepLearning
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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 24x7 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!

- - - - - - - - - - - - - - - - -

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
- - - - - - - - - - - - - - - - - - -

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.

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.

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

Customer Review

Sairaam Varadarajan, Data Evangelist at Medtronic, Tempe, Arizona: I took Big Data and Hadoop / Python course and I am planning to take Apache Mahout thus becoming the customer of Edureka!. Instructors are knowledge... able and interactive in teaching. The sessions are well structured with a proper content in helping us to dive into Big Data / Python. Most of the online courses are free, edureka charges a minimal amount. Its acceptable for their hard-work in tailoring - All new advanced courses and its specific usage in industry. I am confident that, no other website which have tailored the courses like Edureka. It will help for an immediate take-off in Data Science and Hadoop working.

The Difference Between A.I. and Machine Learning and Deep Learning

There's a discussion going on about the topic we are covering today: what’s the difference between AI and machine learning and deep learning. (Get our free list of the worlds best AI newsletters right here ????

Very frequently, press coverage and even practitioners of analytics use the terms Artificial Intelligence and Machine Learning interchangeably. Disregarding the difference between AI and machine learning and deep learning.

However, these three concepts do not represent the same. In this video, we are going to break this down for you, giving you examples of use cases making the difference between ai and machine learning and deep learning more clear.

Any device that perceives its environment and takes actions to maximize its chances of success, can be said to have some kind of artificial intelligence, more frequently referred to as A.I.

More specifically, when a machine has cognitive capabilities, such as problem solving and learning by example it is usually associated with A.I.

Artificial Intelligence has three different levels:

Narrow AI: when a computer can perform one task much better than a human; this is where we stand nowadays.

2. General AI: when a machine can successfully perform any given intellectual task that a human being can too

3. Strong AI: when machines can beat humans in many of tasks.

Machine Learning is a subset of AI.
This is what most applications of AI in business rely on currently. Want to know more about how businesses are applying AI? Watch this video, in which we cover a list of them:

And finally, as a subset of machine learning, there’s Deep Learning. It is called “deep” because it makes use of deep artificial neural networks.


Also discussed in this video:

Difference between ai and machine learning
Difference between ai and machine learning and deep learning
Artificial intelligence
Machine learning
Deep learning
Difference AI ML
Difference AI machine learning
Difference ai machine learning deep learning
AI
ML

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Artificial Intelligence (AI) vs Machine Learning vs Deep Learning vs Data Science

Artificial intelligence is a field where set of techniques are used to make computers as smart as humans. There are certain tasks where human outperform computers such as image recognition, cognitive thinking, creativity, driving cars etc. Machine learning is a sub domain of artificial intelligence where set of statistical and neural network based algorithms are used for training a computer in doing a smart task. Deep learning is all about neural networks. Deep learning is considered to be a sub field of machine learning. Pytorch and Tensorflow are two popular frameworks that can be used in doing deep learning.
Data science on the other hand a field where data is used to generate business insights. It can use machine learning techniques but data science can be done without using machine learning as well. One can use Microsoft excel to draw insights from data. Visualization and BI tools such as tableau and power BI can be used to plot powerful business reports that can give lots of insights about a business.

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Data Science vs Machine Learning – What’s The Difference? | Data Science Course | Edureka

** Python Data Science Training: **
In this video on Data Science vs Machine Learning, we’ll be discussing the importance of Data Science and Machine Learning and we’ll compare them based on a few key parameters. The following topics are covered in this session:

(00:47)What Is Data Science?
(02:32)What Is Machine Learning?
(04:06)Fields Of Data Science
(05:32)Use Case

Python Training Playlist:
Python Blog Series:

PG in Artificial Intelligence and Machine Learning with NIT Warangal :

Post Graduate Certification in Data Science with IIT Guwahati -
(450+ Hrs || 9 Months || 20+ Projects & 100+ Case studies)

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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 24x7 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!

- - - - - - - - - - - - - - - - -

About the Course

Edureka’s Data Science Course on Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. Throughout the Data Science Certification Course, you’ll be solving real life case studies on Media, Healthcare, Social Media, Aviation, HR.

During our Python Certification Training, our instructors will help you to:

1. Master the basic and advanced concepts of Python

2. Gain insight into the 'Roles' played by a Machine Learning Engineer

3. Automate data analysis using python

4. Gain expertise in machine learning using Python and build a Real Life Machine Learning application

5. Understand the supervised and unsupervised learning and concepts of Scikit-Learn

6. Explain Time Series and it’s related concepts

7. Perform Text Mining and Sentimental analysis

8. Gain expertise to handle business in the future, living the present

9. Work on a Real Life Project on Big Data Analytics using Python and gain Hands-on Project Experience

- - - - - - - - - - - - - - - - - - -

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 Dot 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.

For online Data Science training, please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.
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Machine Learning vs Deep Learning vs Artificial Intelligence | ML vs DL vs AI | Simplilearn

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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#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

- - - - - -

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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Best Laptop for Machine Learning and Deep Learning | Machine Learning Training | Edureka

(Data Science Certification: )
This Edureka video on Best Laptops for Machine Learning will provide you the detail and comprehensive knowledge about the best laptops that you can use for machine learning.

Below is the Link to Laptops

TensorBook:
MacBook:
Asus ROG Strix GL702VS:
ASUS ROG Zephyrus S:
Dell XPS 15 9560:
Razer Blade 15:
MSI GS65:
Acer Predator (Helios 300 and Triton 700):

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#Edureka #EdurekaBestlaptop #EdurekaDataScience
#MachineLearning #MachineLearning #DataScience

How it Works?
1. This is a 30-hour Instructor-led Online Course.

2. We have a 24x7 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!

- - - - - - - - - - - - - - - - -

About the Course

Edureka's Data Science Training lets you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using R. Data Science Training encompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introduction to Deep Learning. Throughout this Data Science Course, you will implement real-life use-cases on Media, Healthcare, Social Media, Aviation and HR

- - - - - - - - - - - - - - - - - - -

Who should go for this course?

The market for Data Analytics is growing across the world and this strong growth pattern translates into a great opportunity for all the IT Professionals. Our Data Science Training helps you to grab this opportunity and accelerate your career by applying the techniques on different types of Data. It is best suited for:
Developers aspiring to be a 'Data Scientist'
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
'R' professionals who wish to work Big Data
Analysts wanting to understand Data Science methodologies

-------------------------------------

Why learn Data Science?
Data science is an evolutionary step in interdisciplinary fields like the business analysis that incorporate computer science, modelling, statistics and analytics. To take complete benefit of these opportunities, you need a structured training with an updated curriculum as per current industry requirements and best practices.
Besides strong theoretical understanding, you need to work on various real-life projects using different tools from multiple disciplines to gather a data set, process and derive insights from the data set, extract meaningful data from the set, and interpret it for decision-making purposes.
Additionally, you need the advice of an expert who is currently working in the industry tackling real-life data-related challenges.
.

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If you are looking for live online training, write back to us at sales@edureka.in or call us at US: + 18338555775 (Toll-Free) or India: +91 9606058406 for more information

AI vs Machine Learning vs Deep Learning | Machine Learning vs Artificial Intelligence | AI vs ML

????Intellipaat Artificial Intelligence Master's course:
In this video you will learn about the difference between ai vs machine learning vs deep learning also known as ai vs ml vs dl. Most of the people have this doubt about the differences between machine learning vs artificial intelligence, ai vs dl, deep learning vs machine learning, ai vs machine learning so we have come up with this video tutorial for you to learn and become expert in these technologies. I bet you won't get a comprehensive detailed video between deep learning vs machine learning vs artificial intelligence on YouTube.
#AIvsMachineLearningvsDeepLearning #MachineLearningvsDeepLearningvsArtificialIntelligence #Intellipaat #MLvsDLvsAI #MachineLearningvsArtificialIntelligence #MachineLearningvsAi

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???? Interested to learn AI still more? Please check similar what is AI blog here:

????Following topics are covered in this video:
AI vs ML vs DL - 0:54
Machine Learning & It's Types - 4:29
Types of Supervised Learning - 5:19
Classification Algorithm - Decision Tree - 6:54
Use Cases of Supervised Learning - 8:06
Unsupervised Learning - 9:55
Unsupervised Algorithm - K-means Clustering - 10:32
Use Cases of Unsupervised Learning - 11:25
Reinforcement Learning - 12:10
Use Cases of Reinforcement Learning - 13:06
Limitations of Machine Learning - 15:19
Automatic Feature Extraction with Deep Learning - 16:04
Deep Learning with Artificial Neural Networks - 16:35
Perceptron - How does it works? - 17:38
Why do we need weights? - 18:31
Perceptron Training Algorithm - 19:09
Deep Learning Application - 19:54
Quiz 1 - 20:44
Quiz 2 - 21:03

In our Artificial Intelligence Master's course, you will learn about Ai, Machine Learning, Deep learning, tensorflow and this will help you become a successful AI architect in future. You can get more details about our course at -

All Intellipaat trainings are provided by Industry experts and is completely aligned with industry standards and certification bodies.

If you’ve enjoyed this artificial intelligence vs machine learning vs deep learning video, Like us and Subscribe to our channel for more informative tutorials.

Got any questions about artificial intelligence, machine learning and deep learning? Ask us in the comment section below.
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6. Life time free Course Upgrade
------------------------------
Why Artificial Intelligence is important?

Artificial Intelligence is taking over each and every industry domain. Machine Learning and especially Deep Learning are the most important aspects of Artificial Intelligence that are being deployed everywhere from search engines to online movie recommendations. Taking the Intellipaat deep learning training & Artificial Intelligence Course can help professionals to build a solid career in a rising technology domain and get the best jobs in top organizations.

Why machine learning is important?

Machine learning might just be one of the most important fields of science that we are just moving towards. It differs from other science in the sense that this is one of the one domains where the input and output are not directly correlated and neither do we provide the input for every task that the machine will perform. It is more about mimicking how humans think and solving real world problems like humans without actually the intervention of humans. It focuses on developing computer programs that can be taught to grown and change when exposed to data.

Why should you opt for an Artificial Intelligence career?

If you want to fast-track your career then you should strongly consider Artificial Intelligence. The reason for this is that it is one of the fastest growing technology. There is a huge demand for professionals in Artificial Intelligence. The salaries for A.I. Professionals is fantastic.There is a huge growth opportunity in this domain as well. Hence this Intellipaat Artificial Intelligence tutorial is your stepping stone to a successful career!
------------------------------

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Keras vs Tensorflow vs PyTorch | Deep Learning Frameworks Comparison | Edureka

** AI & Deep Learning with Tensorflow Training: **
This Edureka video on Keras vs TensorFlow vs PyTorch will provide you with a crisp comparison among the top three deep learning frameworks. It provides a detailed and comprehensive knowledge about Keras, TensorFlow and PyTorch and which one to use for what purposes. Following topics will be covered in this video:
1:06 - Introduction to keras, Tensorflow, Pytorch
2:13 - Parameters of Comparison
2:18 - Level of API
3:06 - Speed
3:28 - Architecture
4:03 - Ease of Code
4:27 - Debugging
4:59 - Community Support
5:19 - Datasets
5:37 - Popularity
6:14 - Suitable use cases

Subscribe to our channel to get video updates. Hit the subscribe button above

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Post Graduate Certification in Data Science with IIT Guwahati -
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#keras #tensorflow #pytorch #deeplearning #machinelearning #frameworks
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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 24x7 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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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.

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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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Machine Learning Basics | What Is Machine Learning? | Introduction To Machine Learning | Simplilearn

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:

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-

Watch more videos on Machine Learning:

#MachineLearning #WhatIsMachineLearning #MachineLearningTutorial #MachineLearningBasics #MachineLearningTutorialForBeginners #Simplilearn

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

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TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Learn how to use TensorFlow 2.0 in this full tutorial course for beginners. This course is designed for Python programmers looking to enhance their knowledge and skills in machine learning and artificial intelligence.

Throughout the 8 modules in this course you will learn about fundamental concepts and methods in ML & AI like core learning algorithms, deep learning with neural networks, computer vision with convolutional neural networks, natural language processing with recurrent neural networks, and reinforcement learning.

Each of these modules include in-depth explanations and a variety of different coding examples. After completing this course you will have a thorough knowledge of the core techniques in machine learning and AI and have the skills necessary to apply these techniques to your own data-sets and unique problems.


⭐️ Google Colaboratory Notebooks ⭐️

???? Module 2: Introduction to TensorFlow -
???? Module 3: Core Learning Algorithms -
???? Module 4: Neural Networks with TensorFlow -
???? Module 5: Deep Computer Vision -
???? Module 6: Natural Language Processing with RNNs -
???? Module 7: Reinforcement Learning -


⭐️ Course Contents ⭐️

⌨️ Module 1: Machine Learning Fundamentals (00:03:25)
⌨️ Module 2: Introduction to TensorFlow (00:30:08)
⌨️ Module 3: Core Learning Algorithms (01:00:00)
⌨️ Module 4: Neural Networks with TensorFlow (02:45:39)
⌨️ Module 5: Deep Computer Vision - Convolutional Neural Networks (03:43:10)
⌨️ Module 6: Natural Language Processing with RNNs (04:40:44)
⌨️ Module 7: Reinforcement Learning with Q-Learning (06:08:00)
⌨️ Module 8: Conclusion and Next Steps (06:48:24)


⭐️ About the Author ⭐️

The author of this course is Tim Ruscica, otherwise known as “Tech With Tim” from his educational programming YouTube channel. Tim has a passion for teaching and loves to teach about the world of machine learning and artificial intelligence. Learn more about Tim from the links below:
???? YouTube:
???? LinkedIn:

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Learn to code for free and get a developer job:

Read hundreds of articles on programming:

And subscribe for new videos on technology every day:

Using Python to build an AI to play and win SNES StreetFighter II with machine learning

Hear the story of how we used Python and machine learning to build an artificial intelligence that plays Super StreetFighter II on the Super NES. We'll cover how Python provided the key glue between the SNES emulator and AI, and how the AI was built with gym, keras-rl and tensorflow. We'll show examples of game play and training, and talk about which bot beat which bot in the bot-v-bot tournament we ran.

Talk given by Adam Fletcher and Jonathan Mortensen at PyCon 2018.

Thanks to PyCon for giving us permission to post this talk. freeCodeCamp is not associated with this talk. We're just excited to bring more exposure to to it!

--

Learn to code for free and get a developer job:

Read hundreds of articles on programming:

And subscribe for new videos on technology every day:

Supervised vs Unsupervised vs Reinforcement Learning | Data Science Certification Training | Edureka

** Python Data Science Training: **
In this video on Supervised vs Unsupervised vs Reinforcement learning, we’ll be discussing the types of machine learning and we’ll differentiate them based on a few key parameters. The following topics are covered in this session:

1. Introduction to Machine Learning

2. Types of Machine Learning

3. Supervised vs Unsupervised vs Reinforcement learning

4. Use Cases

Python Training Playlist:
Python Blog Series:

PG in Artificial Intelligence and Machine Learning with NIT Warangal :

Post Graduate Certification in Data Science with IIT Guwahati -
(450+ Hrs || 9 Months || 20+ Projects & 100+ Case studies)

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Subscribe to our channel to get video updates. Hit the subscribe button above:

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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 24x7 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 Data Science Course on Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. Throughout the Data Science Certification Course, you’ll be solving real life case studies on Media, Healthcare, Social Media, Aviation, HR.



During our Python Certification Training, our instructors will help you to:



1. Master the basic and advanced concepts of Python

2. Gain insight into the 'Roles' played by a Machine Learning Engineer

3. Automate data analysis using python

4. Gain expertise in machine learning using Python and build a Real Life Machine Learning application

5. Understand the supervised and unsupervised learning and concepts of Scikit-Learn

6. Explain Time Series and it’s related concepts

7. Perform Text Mining and Sentimental analysis

8. Gain expertise to handle business in the future, living the present

9. 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.

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

Artificial Intelligence vs Machine Learning vs Deep Learning vs Data Science in Hindi | ML #02.02

Course name: “Machine Learning – Beginner to Professional Hands-on Python Course in Hindi”
‘Artificial Intelligence vs Machine Learning vs Deep Learning vs Data Science in Hindi’
In this tutorial we explain the Difference between AI, ML, DL & Data Science in Hindi and describe these questions:
1) What is Artificial Intelligence?
2) What is Weak AI & Strong AI?
3) What is Machine Learning?
4) What is Deep Learning?
5) Difference between Machine Learning & Deep Learning?
6) AI vs ML vs DL vs Data Science.
7) What is Data Science?
8) Flow Chart of Data Science.
9) Role of Machine Learning in Data Science.
10) Machine Learning Flow Chart.

What is Machine Learning in Hindi tutorial link:


For more information:
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#AIvsMLvsDLvsDSinHindi #AIvsMachineLearningvsDeepLearning #AIvsMlvsDl
# MachineLearningvsDeepLearninginHindi #DeepLearningvsMachineLearning

Is this the BEST BOOK on Machine Learning? Hands On Machine Learning Review

Hands On Machine Learning with Scikit Learn and Tensorflow published by O'Reilly and written by Aurelien Geron could just be the best practical book on machine learning. In this review I explain why.

►Subscribe to my YouTube Channel

WANT TO LEARN PYTHON - HERE'S MY COURSE
You can buy the book at my Amazon store




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