This is a series where I walk through the engineering steps and challenges on how to build an Artificial intelligence voice assistant, similar to google home or Amazon Alexa, with Python and PyTorch on a Raspberry Pi. I leverage the latest machine and deep learning techniques to achieve this.

In this video, I show how you can build a wake word detector (keyword spotting) using recurrent neural networks specifically LSTMs.

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Discord Server: Join a community of A.I. Hackers
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Parts:
raspberry pi 4 model b – https://www.amazon.com/gp/product/B07TC2BK1X/ref=ppx_yo_dt_b_search_asin_title?ie=UTF8&psc=1

ReSpeaker 2 mic array hat – https://www.amazon.com/gp/product/B07D5X7N6W/ref=ppx_yo_dt_b_search_asin_title?ie=UTF8&psc=1

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micro sd – https://www.amazon.com/gp/product/B07KY36H93/ref=ppx_yo_dt_b_search_asin_title?ie=UTF8&psc=1

Build A Virtual Assistant Using Python

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How can artificial intelligence (AI) improve beer?

Rob McInerney, the founder & CEO of Intelligent Layer and co-founder of IntelligentX Brewing Company, explains the use of AI in improving everyday products.

Learn more about Rob McInerney at http://www.tedxgoodenoughcollege.com/portfolio/rob-mcinerney-using-artificial-intelligence-to-make-everyday-products-better/

Dr Rob McInerney is the founder & CEO of Intelligent Layer and co-founder of IntelligentX Brewing Company. He completed his PhD in Machine Learning at the University of Oxford, where his research focused on how intelligent machines should learn from experience through a process of reinforcement learning.
In 2015, Rob founded Intelligent Layer to re-envisage the relationship between human beings and intelligent technology, underpinned by his belief that AI can help us navigate a world that is changing faster. Last year, he created IntelligentX Brewing Company, a self-evolving beer brand that uses AI to optimise beer recipes using customer feedback, which Popular Science voted โ€œthe third greatest software innovation of 2016โ€. Intelligent Layer recently graduated from Techstars in New York City, one of the worldโ€™s top accelerator programmes.
Rob regularly contributes to topics around AI and machine learning and has been featured in Time, Wired, Forbes, The Guardian & The Huffington Post

This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx

Text summarization is the process of creating a short, accurate, and fluent summary of a longer text document. It is the process of distilling the most important information from a source text. Automatic text summarization is a common problem in machine learning and natural language processing (NLP). Automatic text summarization methods are greatly needed to address the ever-growing amount of text data available online to both better help discover relevant information and to consume relevant information faster.

๐Ÿ”Š Watch till last for a detailed description
01:21 What is text summarization?
05:19 Installing the packages
15:10 Sentence tokenization

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๐Ÿ“Š ๐Ÿ“ˆ Data Visualization in Python Masterclass: Beginners to Pro
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NLP: Complete Text Processing with Spacy, NLTK, Scikit-Learn,
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๐Ÿ“ˆ ๐Ÿ“˜ 2021 Python for Linear Regression in Machine Learning
Linear & Non-Linear Regression, Lasso & Ridge Regression, SHAP, LIME, Yellowbrick, Feature Selection & Outliers Removal. You will learn how to build a Linear Regression model from scratch.
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Learn Latest R 4.x Programming. You Will Learn List, DataFrame, Vectors, Matrix, DateTime, DataFrames in R, GGPlot2, Tidyverse, Machine Learning, Deep Learning, NLP, and much more.
Course Link: http://bit.ly/r4-ml
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Can an algorithm help you improve your penalty kick or tennis serve? In this episode of Making with Machine Learning, Dale Markowitz chats with Machine Learning Engineer Zack Akil to learn about how Google Cloudโ€™s ML services, like Cloud AutoML vision and the Video Intelligence API, can be used to analyze, assess, and improve your game.

0:00 – Introduction
0:40 – Overview
2:05 – What was measured?
3:37 – What powered the demo?
4:12 – Problems/Challenges
4:45 – Training Auto ML Vision model
5:50 – Using it for tennis serve

Blog Post โ†’ https://goo.gle/3etkKdM
Code โ†’ https://goo.gle/3h4hIOZ
Watch more episodes of Making with Machine Learning โ†’ https://goo.gle/2YysJRY

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Product: Video Intelligence API, Cloud AutoML Vision; fullname: Dale Markowitz;

๐Ÿ”ฅEdureka and NIT Warangal Post Graduate Program on AI and Machine Learning: https://www.edureka.co/post-graduate/machine-learning-and-ai
This Edureka Session explores and analyses the spread and impact of the novel coronavirus pandemic which has taken the world by storm with its rapid growth. In this session, we shall develop a machine learning model in Python to analyze what has been its impact so far and analyze the outbreak of COVID 19 across various regions, visualize them using charts and tables, and predict the number of upcoming confirmed cases.
Finally, weโ€™ll conclude with a few safety measures that you can take to save yourself and your loved ones from getting adversely affected in the hour of crisis.
02: 53 Introduction to COVID 19ย 
05:49 Case Study: the outbreak of COVID 19
57:20 Conclusion

๐Ÿ”ธDatasets and code: https://bit.ly/3tFxZQa

๐Ÿ”ธMachine Learning Tutorial Playlist: https://goo.gl/UxjTxm

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(450+ Hrs || 9 Months || 20+ Projects & 100+ Case studies)

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#edureka #MLedureka #covidPrediction #FutureOfAIML #covid19outbreak #covid19cases #machineLearningusingPython
——————————————————————————————–
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!
——————————————————————————————–
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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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.

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โ–บ A digital artist is teaching machines how to interpret paintings as real life people and results are amazing. Nathan Shipley is a San Francisco based artist who uses latest digital technology and in particular Artificial Intelligence to create some pretty cool artistic stuff. In 2018 he used Deepfake technology to bring to life Salvador Dali for the Dali Museum in Florida, who was able to talk and interact with visitors. In his latest project, using machine learning and generative art, he is exploring how artificial intelligence recreates historical figures from paintings. Please check video where some of his work is presented. And a warning again, please don’t freak out, I’ve added some subtle animation to give them additional layer of realism. Enjoy.

Please follow Nathan’s work here:
https://www.instagram.com/nathan_shipley_vfx/
https://twitter.com/CitizenPlain
http://www.nathanshipley.com/

Featured in this video:
King Henry VII, King Henry VIII, Anne Boleyn, King Edward VI, Queen Mary I (Mary Tudor), Elizabeth I (young, middle and old age), Mona Lisa and Rembrandt.
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Link: https://filmmusic.io/song/6017-background-groove
License: http://creativecommons.org/licenses/by/4.0/
โ–บ Adventure by Alexander Nakarada
Link: https://filmmusic.io/song/6092-adventure
License: http://creativecommons.org/licenses/by/4.0/
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Link: https://filmmusic.io/song/5590-raindrops-
License: http://creativecommons.org/licenses/by/4.0/
โ–บ https://www.scottbuckley.com.au/library/
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Check more videos like this @Mystery Scoop

Build A Smart AI Chat Bot Using Python & Machine Learning

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Promising new research on kidneys:
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Mayo Clinic Website on Chronic Kidney Disease:
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WRT-1025: Using AI/ML Design Patterns for Digital Twins and Model-Centric Engineering – Dr. Mark Austin, University of Maryland

Presented on November 18, 2020 at the 12th Annual SERC Sponsor Research Review. Through various keynotes and breakout sessions, the SSRR focuses on the latest research results from SERC researchers aligned with the emerging and critical research needs of sponsors.

EVENT PAGE: https://sercuarc.org/research-reviews/2020-serc-research-review

Stock Predictions Using Machine Learning Algorithms
#Python #Stocks #MachineLearning

Disclaimer: The material in this video is purely for educational purposes and should not be taken as professional investment advice. Invest at your own discretion.

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Hi. In this lecture will transform tokens into features. And the best way to do that is Bag of Words. Let’s count occurrences of a particular token in our text. The motivation is the following. We’re actually looking for marker words like excellent or disappointed, and we want to detect those words, and make decisions based on absence or presence of that particular word, and how it might work. Let’s take an example of three reviews like a good movie, not a good movie, did not like. Let’s take all the possible words or tokens that we have in our documents. And for each such token, let’s introduce a new feature or column that will correspond to that particular word. So, that is a pretty huge metrics of numbers, and how we translate our text into a vector in that metrics or row in that metrics. So, let’s take for example good movie review. We have the word good, which is present in our text. So we put one in the column that corresponds to that word, then comes word movie, and we put one in the second column just to show that that word is actually seen in our text. We don’t have any other words, so all the rest are zeroes. And that is a really long vector which is sparse in a sense that it has a lot of zeroes. And for not a good movie, it will have four ones, and all the rest of zeroes and so forth. This process is called text vectorization, because we actually replace the text with a huge vector of numbers, and each dimension of that vector corresponds to a certain token in our database. You can actually see that it has some problems. The first one is that we lose word order, because we can actually shuffle over words, and the representation on the right will stay the same. And that’s why it’s called bag of words, because it’s a bag they’re not ordered, and so they can come up in any order. And different problem is that counters are not normalized. Let’s solve these two problems, and let’s start with preserving some ordering. So how can we do that? Actually you can easily come to an idea that you should look at token pairs, triplets, or different combinations. These approach is also called as extracting n-grams. One gram stands for tokens, two gram stands for a token pair and so forth. So let’s look how it might work. We have the same three reviews, and now we don’t only have columns that correspond to tokens, but we have also columns that correspond to let’s say token pairs. And our good movie review now translates into vector, which has one in a column corresponding to that token pair good movie, for movie for good and so forth. So, this way, we preserve some local word order, and we hope that that will help us to analyze this text better. The problems are obvious though. This representation can have too many features, because let’s say you have 100,000 words in your database, and if you try to take the pairs of those words, then you can actually come up with a huge number that can exponentially grow with the number of consecutive words that you want to analyze. So that is a problem. And to overcome that problem, we can actually remove some n-grams. Let’s remove n-grams from features based on their occurrence frequency in documents of our corpus. You can actually see that for high frequency n-grams, as well as for low frequency n-grams, we can show why we don’t need those n-grams. For high frequency, if you take a text and take high frequency n-grams that is seen in almost all of the documents, and for English language that would be articles, and preposition, and stuff like that. Because they’re just there for grammatical structure and they don’t have much meaning. These are called stop-words, they won’t help us to discriminate texts, and we can pretty easily remove them. Another story is low frequency n-grams, and if you look at low frequency n-grams, you actually find typos because people type with mistakes, or rare n-grams that’s usually not seen in any other reviews. And both of them are bad for our model, because if we don’t remove these tokens, then very likely we will overfeed, because that would be a very good feature for our future classifier that can just see that, okay, we have a review that has a typo, and we had only like two of those reviews, which had those typo, and it’s pretty clear whether it’s positive or negative. So, it can learn some independences that are actually not there and we don’t really need them. And the last one is medium frequency n-grams, and those are really good n-grams, because they contain n-grams that are not stop-words, that are not typos and we actually look at them. And, the problem is there’re a lot of medium frequency n-grams. And it proved to be useful to look at n-gram frequency in our corpus for filtering out bad n-grams. What if we can use the same frequency for ranking of medium frequency n-grams?

WRT-1025: Using AI/ML Design Patterns for Digital Twins and Model-Centric Engineering – Dr. Mark Austin, University of Maryland

Presented on November 18, 2020 at the 12th Annual SERC Sponsor Research Review. Through various keynotes and breakout sessions, the SSRR focuses on the latest research results from SERC researchers aligned with the emerging and critical research needs of sponsors.

EVENT PAGE: https://sercuarc.org/research-reviews/2020-serc-research-review

PyPower Projects – Experience The Power Of Python

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PyPower is an initiative with the vision to integrate every TechGeek to an integrated platform in order to devour the essence of Python.
We will be coming up with various extraordinary real-life projects as well as awesome technical programs easily implemented through Python.
Will present a video related to this every week along with its explanation, working, programming screencast and code.
Open the essence of images by OpenCV.
Embed numeric operations in Python using NumPy.
Come in flow of machine learning with TensorFlow.
Dive deep into ocean of deep learning with Keras.
Plot anything anytime anywhere using Matplotlib.
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Let your Shell strengthen to its Apex and be the Impulse of your work.

Stay Tuned.

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Here we go over a Python Project using OpenCV and simple Machine Learning
Google Colab Link : https://colab.research.google.com/drive/1DOvXJZRkjfKfF9oUpCZXSU3hPG3g1h-l?usp=sharing
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Detecting and starting treatment of autism spectrum disorder (ASD) at an age of 18 to 24 months can increase a childโ€™s IQ by up to 17 pointsโ€”in some cases moving them into the โ€œaverageโ€ child IQ range of 90-110 (or above it)โ€”and improving the child’s quality of life significantly. Researchers at Duke University are using Machine Learning on AWS to create a faster, less expensive, more reliable, and more accessible system to screen children early for ASD.

To learn more about research projects like this that are enabled by AWS, see the AWS Machine Learning Research Awards website โ€“ https://amzn.to/2RR78PM

Viz.ai, an Israeli-based technology provider, uses cloud based Artificial Intelligence to revolutionize stroke care for doctors and patients. Join builder Adrian De Luca for this special edition of This is My Architecture as he dives deep into the solutions architecture of this ground breaking application that dramatically reduces systemic delays that stand between patients and life-saving treatments. Explore how the company integrates Computed Tomography (CT) images with Amazon S3 & Amazon Simple Queue Service (SQS), assembles and conducts inference of millions of files with Amazon EC2 & Amazon RDS, and improves stroke detection through an automated machine learning training workflow with AWS Lambda, AWS Batch & Amazon Fargate.

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In this tutorial, you will learn how to create a Machine Learning Linear Regression Model using Python. You will be analyzing a house price predication dataset for finding out the price of a house on different parameters. You will do Exploratory Data Analysis, split the training and testing data, Model Evaluation and Predictions.

Blog and Dataset – https://studygyaan.com/data-science-ml/linear-regression-machine-learning-project-for-house-price-prediction

Coronavirus (Covid-19) has become the most buzzed topic these days. Its outbreak has taken the world by storm. In this video, we’ll see what Coronavirus is, how did it emerge, and what are its symptoms. Then, we will see what has been its impact so far and analyze the outbreak of Coronavirus across various regions, visualize them using charts and graphs, and predict the number of upcoming confirmed cases using the Linear Regression model in Python. Finally, weโ€™ll look at the various safety measures that you can take to save yourself from getting attacked by Coronavirus.

Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1

To access the slides, click here: https://www.slideshare.net/Simplilearn/coronavirus-outbreak-prediction-using-machine-learning-covid19-outbreak-prediction-simplilearn/Simplilearn/coronavirus-outbreak-prediction-using-machine-learning-covid19-outbreak-prediction-simplilearn

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=Coronavirus-outbreak-prediction-sHWKN5dakPw&utm_medium=Tutorials&utm_source=youtube

You can also go through the Slides here: https://goo.gl/m5Txob

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We’ve partnered with Purdue University and collaborated with IBM to offer you the unique Post Graduate Program in AI and Machine Learning. Learn more about it here – https://www.simplilearn.com/ai-and-machine-learning-post-graduate-certificate-program-purdue?utm_campaign=Machine-Learning-Tutorial-DWsJc1xnOZo&utm_medium=Tutorials&utm_source=youtube

About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all peopleโ€™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 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 thorough knowledge of the mathematical and heuristic aspects of Machine Learning.

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=Coronavirus-outbreak-prediction-sHWKN5dakPw&utm_medium=Tutorials&utm_source=youtube

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Cloud Tensor Processing Units (TPUs ) enable machine learning engineers and researchers to accelerate TensorFlow workloads with Google-designed supercomputers on Google Cloud Platform. This talk will include the latest Cloud TPU performance numbers and survey the many different ways you can use a Cloud TPU today – for image classification, object detection, machine translation, language modeling, sentiment analysis, speech recognition, and more. You’ll also get a sneak peak at the road ahead.

Rate this session by signing-in on the I/O website here โ†’ https://goo.gl/5HcnkN

Watch more GCP sessions from I/O ’18 here โ†’ https://goo.gl/qw2mR1
See all the sessions from Google I/O ’18 here โ†’ https://goo.gl/q1Tr8x

Subscribe to the Google Cloud Platform channel โ†’ https://goo.gl/S0AS51

#io18 #GoogleIO #GoogleIO2018

Banjo founder Damien Patton explains how he decided to focus on building the world’s first “crystal ball” after identifying a suspect during the 2013 Boston Marathon bombing.

#ArtificialIntelligence #DamienPatton #Startup

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AWS provides a number of different machine learning products (https://amzn.to/2IbHEb3) that you or your business can leverage. Learn about the three layers of the machine learning stack. Experts who are comfortable building and training their own machine learning models can take advantage of AWSโ€™s Framework & Interfaces layer. Developers and data scientists can leverage AWSโ€™s ML Platforms layer using Amazon SageMaker to build, train, and deploy machine learning models without having specialized expertise in ML or having to think about the infrastructure like you would at the Framework & Interface layer. Developers who want to make calls to APIs to add machine learning services to their applications without building and training their own models can take advantage of the Application Services layer. Simply call APIs to perform image processing, voice recognition, video processing, speech synthesis, or other machine learning services. Start taking advantage of machine learning on AWS today like the NFL, Netflix, Zillow, and other large businesses have!

Download:
The circuit diagram and Project programming can be downloaded by clicking on the link below

Arduino Image Processing based Entrance lock Control System

Download Libraries:
https://www.electroniclinic.com/arduino-libraries-download-and-projects-they-are-used-in-project-codes/

Image Processing based Eyepupil Tracking:
https://youtu.be/xQrTNoSgNDQ

Human machine tracking using image processing:
https://youtu.be/HnKRy26NXMU

Watch other tutorials:

9: Image processing based entrance control system
https://youtu.be/TYllIMfJ3Eg

8: GSM and GPS based car accident location monitoring
https://youtu.be/tumEQioxT6I

7: GSM based GAS leakage detection and sms alert
https://youtu.be/Ar4LowNT_HI

6: Wireless Tongue controlled wheelchair
https://youtu.be/WNCn062YzXc

5: Human Posture Monitoring System
https://youtu.be/6bxZyTi6m-4

4: RFID based bike anti theft system
https://youtu.be/iGtu6TQ-_ao

3: RFID based students attendance system
https://youtu.be/gc4LLN1vftk

2: Piezo Electric generator
https://youtu.be/n6FFnJVq5cQ

1: iot car parking monitoring system
https://youtu.be/tjLAjGi6O5Q

Support me on Patreon and get access to hundreds of projects:
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Project Description:
********************

This is a very detailed tutorial on how to make image processing based human recognition system for entrance controlling. In this project we will be using Arduino uno for controlling the electronic door lock, The Arduino will receive command from the vb.net application when a human will be detected. We will be using xml file for human face detection. This xml file will be used in vb.net application to track a human face. The application designed in vb.net visual basic make use of the emguCv.

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download haarcascades:
https://github.com/opencv/opencv/tree/master/data/haarcascades

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DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I will receive a small commission. This helps support the channel and allows me to continue to make videos like this. Thank you for the support!

About the Electronic Clinic:
Electronic Clinic is the only channel on YouTube that covers all the engineering fields. Electronic Clinic helps the students and workers to learn electronics designing and programming. Electronic Clinic has tutorials on
Gsm based projects ” gsm security system, gsm messages sending and receiving, gsm based controlling, gsm based request data”

wireless projects using bluetooth, radio frequency ” rf ” , ir remote based or infrared remote based.
electronics projects
wheelchair projects
robots
image processing
security systems
pcb designing
schematics designing
Solidworks projects
final year engineering projects and ideas
electronic door locks projects
automatic watering systems.
computer desktop applications designing.
email systems.
and much more.

For more Projects and tutorials visit my Website:
www.electroniclinic.com

Follow me on Facebook:
https://web.facebook.com/groups/190031841821771/

email: engrfahad@electroniclinic.com

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#CarControlAndAnti-theft
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Hello! Today I will show you how to make image recognition bots as fast as possible using Python. I will cover the basics of Pyautogui, Python, win32api and by the end, you should be able to make a bot for pretty much any game.

Here are the commands to run and code to paste: https://github.com/KianBrose/Image-Recognition-Botting-Tutorial/blob/master/README.txt

All code can be found here: https://github.com/KianBrose/Image-Recognition-Botting-Tutorial

If this video helped you please consider subscribing and leaving a like, it helps a ton!

If you have any errors/suggestions please let me know!

Discord server: https://discord.com/invite/8NcumxN

Do you fear artificial intelligence in healthcare?

We spoke to Dr Shada Alsalamah of MIT and King Saud University that you shouldn’t always be afraid of AI and machine learning when it reduces time to process the data that would normally take much longer using human expertise. With the lack of data scientists, we are also relying on AI to speed up decision-making processes that are used by hospitals which are leading to decreased patient waiting times and saving people’s lives.

Shot in Adelaide, Australia at the APAC Blockchain Conference.

Watch the full interview here: https://www.youtube.com/watch?v=q8J3RxF5g3Y&t=158s

Speakers:
– Lucy Lin, Founder/CMO of Forestlyn, www.forestlyn.com
– Dr Shada Alsalamah, Professor at King Saud University (ksu.edu.sa) & Visiting Scholar at MIT, www.mit.edu

๐Ÿ‘‰ Be sure to SUBSCRIBE to our channel @Forestlyn Marketing , LIKE and SHARE this video for more innovation and emerging technologies insights.

About Forestlyn:
Forestlyn is a technology and blockchain marketing consultancy. With a deep understanding of the emerging technologies space, we work in partnership with innovative organisations and founders to provide strategic marketing delivering revenue and growth. Talk to us today to find out more about how we can help you. For more information: https://www.forestlyn.com/

Learn about the limitations of RNNs, how LSTMs work, and Gated Recurrent Units (GRUs).

Github repo: https://github.com/lukas/ml-class
See all classes: http://wandb.com/classes
Weights & Biases: http://wandb.com

In this video, we will learn about Automatic text generation using Tensorflow, Keras, and LSTM. Automatic text generation is the generation of natural language texts by computer. It has applications in automatic documentation systems, automatic letter writing, automatic report generation, etc. In this project, we are going to generate words given a set of input words. We are going to train the LSTM model using William Shakespeareโ€™s writings.

Long Short-Term Memory (LSTM) networks are a modified version of recurrent neural networks, which makes it easier to remember past data in memory. Generally, LSTM is composed of a cell (the memory part of the LSTM unit) and three โ€œregulatorsโ€, usually called gates, of the flow of information inside the LSTM unit: an input gate, an output gate and a forget gate. Intuitively, the cell is responsible for keeping track of the dependencies between the elements in the input sequence. The input gate controls the extent to which a new value flows into the cell, the forget gate controls the extent to which a value remains in the cell and the output gate controls the extent to which the value in the cell is used to compute the output activation of the LSTM unit. The activation function of the LSTM gates is often the logistic sigmoid function. There are connections into and out of the LSTM gates, a few of which are recurrent. The weights of these connections, which need to be learned during training, determine how the gates operate.

๐Ÿ”Š Watch till last for a detailed description
01:20 Text generation using TensorFlow, Keras, and LSTM
05:28 Get started with code
27:53 Build LSTM model and prepare x and y
42:25 LSTM model

๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡
โœจ Kite is a free AI-powered coding assistant that will help you code faster and smarter. The Kite plugin integrates with all the top editors and IDEs to give you smart completions and documentation while youโ€™re typing. I’ve been using Kite for 6 months and I love it! Get your FREE coding assistant today!!
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๐Ÿ“Š ๐Ÿ“ˆ Data Visualization in Python Masterclass: Beginners to Pro
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๐Ÿ“˜ ๐Ÿ“™ Natural Language Processing (NLP) in Python for Beginners
NLP: Complete Text Processing with Spacy, NLTK, Scikit-Learn,
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Make your own speech recognition app using MIT app inventor and control gadgets, electrical appliances, and robots. This app will allow you to control any gadgets I’ve made a working video of this app controlling electrical devices using arduino and HC-05 bluetooth module,. check out the video here https://www.youtube.com/watch?v=EJ0WEkG6zuY

circuit diagram and arduino program can find in the link http://mitappsinventor.blogspot.in/p/blog-page.html

In our conversations with developers, business, and industry leaders all over the world, we hear four patterns emerging on how to apply AI to businesses and business scenarios that provide a useful frame for the conversation. Hear Corporate Vice President Steve Guggenheimer discuss these patterns.

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For more about Microsoft, our technology, and our mission, visit https://aka.ms/microsoftstories

The battle for the 2020 US Presidential election has begun. The Republican nominee Donald Trump and the Democratic nominee Joe Biden are fighting for a place in the White House. In this video, we will analyze the US elections using Twitter Sentiment Analysis in Python. You will understand how to extract tweets, store it in a CSV file, examine the tweets’ polarity, and visualize the results. Finally, you will build a word cloud based on the tweets for Trump and Biden.

โœ…Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH

โฉ Check out the Machine Learning tutorial videos: https://bit.ly/3fFR4f4

#USElectionPrediction2020 #USElectionPredictionLatest #ElectionPredictionUsingMachineLearning #MachineLearningCourse #ElectionPredictionUsingDataScience #Simplilearn

About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all peopleโ€™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 knowledge and hands-on skills required for certification and job competency in Machine Learning.

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 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

๐Ÿ‘‰Learn more at: https://bit.ly/3fouyY0

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๐Ÿ”ฅNIT Warangal Post Graduate Program in AI & Machine Learning with Edureka: https://www.edureka.co/nitw-ai-ml-pgp
This Edureka “Stock Prediction using Machine Learning” takes you through the basic process of predicting the trends of stock prices using machine learning architecture of LSTM while also making use of prominent Python Libraries such as Tensorflow, Keras, etc. Topics covered in the tutorial are as follow:
01:30 Introduction to Stock Prediction
03:00 LSTM Architecture
05:35 Stock Prediction Model
26:35 Conclusion

๐Ÿ”ธDataset & code: https://bit.ly/2x6BPe0

๐Ÿ”นCheck out our machine learning Playlist: https://bit.ly/3byztDp
๐Ÿ”นAnd our Blog series: https://bit.ly/34YXH7n
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———————————————-
Why Machine Learning & AI?

Because of the increasing need for intelligent and accurate decision making, there is an exponential growth in the adoption of AI and ML technologies. Hence these are poised to remain the most important technologies in the years to come.
———————————————–
PG Program in Machine Learning & AI

1. Ranked 4th among NITs by NIRF
2. Ranked among Top 50 Institutes in India
3. Designated as Institute of National Importance
———————————————–
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1. Mentorship from NITW faculty
2. Placement Assistance
3. Alumni Status
4. Industry Networking
———————————————–
Industry Projects

1. Building a Conversational ChatBot
2. Predictive Model for Auto Insurance
3. E-commerce Website – Sales Prediction
———————————————–
Mentors & Instructors

Dr. RBV Subramaanyam
Professor NITW

Dr. DVLN Somayajulu
Professor NITW

Dr. P. Radha Krishna
Professor NITW

Dr. V. Ravindranath
Professor JNTU Kakinada
——————————————–
Is this program for me?

If youโ€™re passionate about AI & ML and want to pursue a career in this field, this program is for you. Whether youโ€™re a fresher or a professional, this program is designed to equip you with the skills you need to rise to the top in a career in AI & ML.

Is there any eligibility criteria for this program?

A potential candidate must have one of the following prerequisites: Degrees like BCA, MCA, and B.Tech or Programming experience Should have studied PCM in 10+2

Will I get any certificate at the end of the course?

Yes, you will receive a Post-Graduate industry-recognized certificate from E & ICT Academy, NIT Warangal upon successful completion of the course.
——————————————–
For more information, Please write back to us at sales@edureka.in or call us at IND: +91-9606058418 / US: 18338555775 (toll-free).

We’ve developed a new framework for reinforcement learning, a subset of machine learning. This video shows the framework applied to an autonomous RC car that learns to drift around a truck.

Graph enhancements to AI and ML are changing the landscape of intelligent applications. In this webinar, weโ€™ll focus on using graph feature engineering to improve the accuracy, precision, and recall of machine learning models. Youโ€™ll learn how graph algorithms can provide more predictive features as well as aid in feature selection to reduce overfitting. Weโ€™ll illustrate a link prediction workflow using Spark and Neo4j to predict collaboration and discuss our missteps and tips to get to measurable improvements.

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Why should you watch this machine learning video?

Machine learning is one of the fastest growing arms of the domain of artificial intelligence. It has far reaching consequences and in the next couple of years we will be seeing every industry deploying the principles of artificial intelligence, machine learning and deep learning technologies at scale.

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.
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