*** Natural Language Processing Course: https://www.edureka.co/python-natural-language-processing-course *** This session on Context Free Grammar will give you a detailed and comprehensive knowledge of context-free grammar and how it is used in Natual Language Processing. It also focuses on Syntax trees and Techniques like Chinking and Chunking. ———————————— About this course : Edurekaโ€™s Natural Language Processing with Python course will take you through the essentials of text processing all the way up to classifying texts using Machine Learning algorithms. You will learn various concepts such as Tokenization, Stemming, Lemmatization, POS tagging, Named Entity Recognition, Syntax Tree Parsing and so on using Pythonโ€™s most famous NLTK package. Once you delve into NLP, you will learn to build your own text classifier using the Naรฏve Bayes algorithm. ———————————— Meetup: http://meetu.ps/c/4glvl/JzH2K/f Instagram: https://www.instagram.com/edureka_learning Slideshare: https://www.slideshare.net/EdurekaIN/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka For more information, please write back to us at sales@edureka.co or call us at: IND: 9606058406 / US: 18338555775 (toll free)
This video contains a stepwise implementation of python code for object detection based on the OpenCV library. The following are the list of contents you will find inside the video. 1) basic understanding of object detection and image classification 2) installation of necessary libraries 2) line by line implementation for object detection using OpenCV a) Single Image b) Video.mp4 c) Live Webcam List of labels to download https://github.com/pjreddie/darknet/blob/master/data/coco.names Configuration file https://gist.github.com/dkurt/54a8e8b51beb3bd3f770b79e56927bd7
Welcome to this new video series in which we will be using Natural Language Processing or it’s called NLP in short. to analyse emotions and sentiments of given text. After completing this videos series – 1) You will be able to analyse different emotions present in an essay like sadness, happiness, jealousy etc 2) You will be able to find out the dominant emotion in the text 3) You will be able to plot those emotions on a graph 4) And you will also be able to tell whether the whole text is a positive or negative emotion 5) And finally you will also be able scrap tweets with a hashtag and find out the public opinion on that hashtag. For example you can search for #donaldtrump and find out whether that emotion is associated with a positive or a negative sentiment. First we will be doing all the natural language processing and sentiment analysis on our own without the use of a library or a package. So that you guys properly understand the concepts of NLP and then we can go on to use NLTK library to shorten our work. Source Code – https://github.com/attreyabhatt/Sentiment-Analysis Next video – Installing Python and Pycharm https://youtu.be/Ul0ZgDoamco Full playlist – https://www.youtube.com/playlist?list=PLhTjy8cBISEoOtB5_nwykvB9wfEDscuEo Subscribe – https://www.youtube.com/channel/UCirPbvoHzD78Lnyll6YYUpg?sub_confirmation=1 Website – www.buildwithpython.com Instagram – http://instagram.com/buildwithpython #python #nltk #nlp
This six-part video series goes through an end-to-end Natural Language Processing (NLP) project in Python to compare stand up comedy routines. – Natural Language Processing (Part 1): Introduction to NLP & Data Science – Natural Language Processing (Part 2): Data Cleaning & Text Pre-Processing in Python – Natural Language Processing (Part 3): Exploratory Data Analysis & Word Clouds in Python – Natural Language Processing (Part 4): Sentiment Analysis with TextBlob in Python – Natural Language Processing (Part 5): Topic Modeling with Latent Dirichlet Allocation in Python – Natural Language Processing (Part 6): Text Generation with Markov Chains in Python All of the supporting Python code can be found here: https://github.com/adashofdata/nlp-in-python-tutorial
๐Ÿ”ฅEdureka Python Training: https://www.edureka.co/python-programming-certification-training/ This Edureka video on ” Python NLTK ” will walk you through the best way to clean, analyze and categorize your text data. The following are covered in this Python NLTK Tutorial video: 00:00:00 Introduction 00:00:42 What is NLP? 00:03:15 What is NLTK? 00:25:42 Naive Bayes Algorithm & Sentiment Analysis Checkout Edureka’s Python Tutorial Playlist: https://goo.gl/WsBpKe Checkout Edureka’s Python Tutorial Blog Series: http://bit.ly/2sqmP4s ๐Ÿ”ดDo subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV Edureka Community: https://bit.ly/EdurekaCommunity Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Telegram: https://t.me/edurekaupdates SlideShare: https://www.slideshare.net/EdurekaIN Meetup: https://www.meetup.com/edureka/ #Edureka #PythonEdureka #PythonNLTK #NaturalLanguageToolKit #howtocodeinpython #pythonprojects #pythonprogramming #pythontutorial #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 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 [More]
Presented by: Keith Galli In the past year, massive developments have been made in the natural language processing field. Improvements in areas such as question answering, machine translation, and sentiment analysis have opened up doors to utilize NLP more effectively than ever before. In this tutorial we will perform a brief overview of the field of NLP and look at the Python libraries that allow us to utilize different techniques and models. We will start with simple, traditional approaches to NLP that will provide us baseline for our models. As we progress in the tutorial we will look at some more advanced concepts that can give quick boosts to model performance. We will end by introducing state-of-the-art language models and how we can incorporate them into applications that we build. Tutorial resources:https://github.com/keithgalli/pycon2020
๐Ÿ”ฅEdureka PG Diploma in Artificial Intelligence & ML from E & ICT Academy NIT Warangal(Use Code: YOUTUBE20): https://www.edureka.co/executive-programs/machine-learning-and-ai This Edureka video on ‘Emotion Detection using OpenCV & Python’ will give you an overview of Emotion Detection using OpenCV & Python and will help you understand various important concepts that concern Emotion Detection using OpenCV & Python Following pointers are covered in this Emotion Detection using OpenCV & Python: 00:00:00 Agenda 00:01:54 Introduction to Deep Learning 00:04:14 What is Image Processing? 00:04:58 Libraries used in Project 00:07:30 Steps to execute the Project 00:08:47 Implementation ———————————— Github link for codes: https://github.com/dhruvpandey662/Emotion-detection dataset link: https://www.dropbox.com/s/w3zlhing4dkgeyb/train.zip?dl=0 ———————————— ๐Ÿ”นCheck Edureka’s Deep Learning & TensorFlow Tutorial playlist here: https://goo.gl/cck4hE ๐Ÿ”นCheck Edureka’s Deep Learning & TensorFlow Tutorial Blog Series: http://bit.ly/2sqmP4s ๐Ÿ”ดSubscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ SlideShare: https://www.slideshare.net/EdurekaIN Castbox: https://castbox.fm/networks/505?country=in Meetup: https://www.meetup.com/edureka/ ———๐„๐๐ฎ๐ซ๐ž๐ค๐š ๐Ž๐ง๐ฅ๐ข๐ง๐ž ๐“๐ซ๐š๐ข๐ง๐ข๐ง๐  ๐š๐ง๐ ๐‚๐ž๐ซ๐ญ๐ข๐Ÿ๐ข๐œ๐š๐ญ๐ข๐จ๐ง——— ๐Ÿ”ต Data Science Online Training: https://bit.ly/2NCT239 ๐ŸŸฃ Python Online Training: https://bit.ly/2CQYGN7 ๐Ÿ”ต AWS Online Training: https://bit.ly/2ZnbW3s ๐ŸŸฃ RPA Online Training: https://bit.ly/2Zd0ac0 ๐Ÿ”ต DevOps Online Training: https://bit.ly/2BPwXf0 ๐ŸŸฃ Big Data Online Training: https://bit.ly/3g8zksu ๐Ÿ”ต Java Online Training: https://bit.ly/31rxJcY ———๐„๐๐ฎ๐ซ๐ž๐ค๐š ๐Œ๐š๐ฌ๐ญ๐ž๐ซ๐ฌ ๐๐ซ๐จ๐ ๐ซ๐š๐ฆ๐ฌ——— ๐ŸŸฃMachine Learning Engineer Masters Program: https://bit.ly/388NXJi ๐Ÿ”ตDevOps Engineer Masters Program: https://bit.ly/2B9tZCp ๐ŸŸฃCloud Architect Masters Program: https://bit.ly/3i9z0eJ ๐Ÿ”ตData Scientist Masters Program: https://bit.ly/2YHaolS ๐ŸŸฃBig Data Architect Masters Program: https://bit.ly/31qrOVv ๐Ÿ”ตBusiness Intelligence Masters Program: https://bit.ly/2BPLtn2 —————–๐„๐๐ฎ๐ซ๐ž๐ค๐š ๐GD ๐‚๐จ๐ฎ๐ซ๐ฌ๐ž๐ฌ————— ๐Ÿ”ตArtificial and Machine Learning PGD: https://bit.ly/2Ziy7b1 #edureka #edurekadeeplearning #deeplearning #EmotionDetectionusingOpenCV&Python #RealTimeEmotionDetection #machinelearningpretrainedmodels #deeplearningtutorial #edurekatraining ——————————————————————– Why Machine Learning & [More]
In this Python Tutorial we build a simple chatbot using PyTorch and Deep Learning. I will also provide an introduction to some basic Natural Language Processing (NLP) techniques. 1) Theory + NLP concepts (Stemming, Tokenization, bag of words) 2) Create training data 3) PyTorch model and training 4) Save/load model and implement the chat Resource: This tutorial was inspired and adapted from the following article: “Contextual Chatbots with Tensorflow”: https://chatbotsmagazine.com/contextual-chat-bots-with-tensorflow-4391749d0077 ๐Ÿช Code faster with Kite, AI-powered autocomplete that integrates into VS Code: https://www.kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=pythonengineer&utm_content=description-only * โœ… Write cleaner code with Sourcery, instant refactoring suggestions in VS Code & PyCharm: https://sourcery.ai/?utm_source=youtube&utm_campaign=pythonengineer * ๐Ÿ“š Get my FREE NumPy Handbook: https://www.python-engineer.com/numpybook ๐Ÿ““ Notebooks available on Patreon: https://www.patreon.com/patrickloeber โญ Join Our Discord : https://discord.gg/FHMg9tKFSN If you enjoyed this video, please subscribe to the channel! NLTK: https://www.nltk.org You can find the code on GitHub: https://github.com/python-engineer/pytorch-chatbot PyTorch Beginner Course: https://www.youtube.com/playlist?list=PLqnslRFeH2UrcDBWF5mfPGpqQDSta6VK4 Please checkout my website to see all tutorials: https://www.python-engineer.com You can find me here: Twitter: https://twitter.com/python_engineer GitHub: https://github.com/python-engineer Icons: https://fontawesome.com/icons/comments https://fontawesome.com/icons/robot #PyTorch #NLP #DeepLearning ———————————————————————————————————- * This is a sponsored or an affiliate link. By clicking on it you will not have any additional costs, instead you will support me and my project. Thank you so much for the support! ๐Ÿ™
In this video, we will learn How to extract text from a pdf file in python NLP. Natural Language Processing (NLP) is the field of Artificial Intelligence, where we analyse text using machine learning models. Text Classification, Spam Filters, Voice text messaging, Sentiment analysis, Spell or grammar check, Chatbot, Search Suggestion, Search Autocorrect, Automatic Review, Analysis system, Machine translation are the applications of NLP. This notebook demonstrates the extraction of text from PDF files using python packages. Extracting text from PDFs is an easy but useful task as it is needed to do further analysis of the text. We are going to use PyPDF2 for extracting text. You can download it by running the command given below. We have used the file NLP .pdf in this notebook. The open() function opens a file and returns it as a file object. rb opens the file for reading in binary mode. ๐Ÿ”Š Watch till last for a detailed description 02:43 Importing the libraries 06:21 Reading and extracting the data 09:17 Append write or merge PDFs 13:20 Analysing the output ๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡ โœ๏ธ๐Ÿ†๐Ÿ…๐ŸŽ๐ŸŽŠ๐ŸŽ‰โœŒ๏ธ๐Ÿ‘Œโญโญโญโญโญ ENROLL in My Highest Rated Udemy Courses to ๐Ÿ”‘ Unlock Data Science Interviews ๐Ÿ”Ž and Tests ๐Ÿ“š ๐Ÿ“— NLP: Natural Language Processing ML Model Deployment at AWS Build & Deploy ML NLP Models with Real-world use Cases. Multi-Label & Multi-Class Text Classification using BERT. Course Link: https://bit.ly/bert_nlp ๐Ÿ“Š ๐Ÿ“ˆ Data Visualization in Python Masterclass: Beginners to Pro Visualization in matplotlib, Seaborn, Plotly & Cufflinks, EDA on Boston Housing, Titanic, IPL, FIFA, [More]
In this spaCy tutorial, you will learn all about natural language processing and how to apply it to real-world problems using the Python spaCy library. ๐Ÿ’ป Course website with code: http://spacy.pythonhumanities.com/ โœ๏ธ Course developed by Dr. William Mattingly. Check out his channel: https://www.youtube.com/pythontutorialsfordigitalhumanities โญ๏ธ Course Contents โญ๏ธ โŒจ๏ธ (0:00:00) Course Introduction โŒจ๏ธ (0:03:56) Intro to NLP โŒจ๏ธ (0:11:53) How to Install spaCy โŒจ๏ธ (0:17:33) SpaCy Containers โŒจ๏ธ (0:21:36) Linguistic Annotations โŒจ๏ธ (0:45:03) Named Entity Recognition โŒจ๏ธ (0:50:08) Word Vectors โŒจ๏ธ (1:05:22) Pipelines โŒจ๏ธ (1:16:44) EntityRuler โŒจ๏ธ (1:35:44) Matcher โŒจ๏ธ (2:09:38) Custom Components โŒจ๏ธ (2:16:46) RegEx (Basics) โŒจ๏ธ (2:19:59) RegEx (Multi-Word Tokens) โŒจ๏ธ (2:38:23) Applied SpaCy Financial NER ๐ŸŽ‰ Thanks to our Champion and Sponsor supporters: ๐Ÿ‘พ Wong Voon jinq ๐Ÿ‘พ hexploitation ๐Ÿ‘พ Katia Moran ๐Ÿ‘พ BlckPhantom ๐Ÿ‘พ Nick Raker ๐Ÿ‘พ Otis Morgan ๐Ÿ‘พ DeezMaster ๐Ÿ‘พ AppWrite — Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news And subscribe for new videos on technology every day: https://youtube.com/subscription_center?add_user=freecodecamp
* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training * This Edureka video on “Artificial Intelligence With Python” will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples. Python Course: https://www.youtube.com/watch?v=vaysJAMDaZw Statistics and Probability Tutorial: https://www.youtube.com/watch?v=XcLO4f1i4Yo Check out the entire Machine Learning Playlist: https://bit.ly/2NG9tK4 Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV #edureka #aiEdureka #artificialIntelligence #artificialIntelligenceTutorial #artificialIntelligenceWithPython #artificialIntelligenceEngineer Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Slideshare: https://www.slideshare.net/EdurekaIN/ ————————————- About the Masters Program Edurekaโ€™s Machine Learning Engineer Masters Program makes you proficient in techniques like Supervised Learning, Unsupervised Learning and Natural Language Processing. It includes training on the latest advancements and technical approaches in Artificial Intelligence & Machine Learning such as Deep Learning, Graphical Models and Reinforcement Learning. The Master’s Program Covers Topics LIke: Python Programming PySpark HDFS Spark SQL Machine Learning Techniques and Artificial Intelligence Types Tokenization Named Entity Recognition Lemmatization Supervised Algorithms Unsupervised Algorithms Tensor Flow Deep learning Keras Neural Networks Bayesian and Markovโ€™s Models Inference Decision Making Bandit Algorithms Bellman Equation Policy Gradient Methods. ———————- Prerequisites There are no prerequisites for enrolment to the Masters Program. However, as a goodwill gesture, Edureka offers a complimentary self-paced course in your LMS on SQL Essentials to brush up on your SQL Skills. This program is designed and developed for an aspirant planning to build a career in Machine Learning or an experienced professional working in the IT industry. ————————————– Please write back [More]
This video is about building a Heart Disease Prediction system using Machine Learning with Python. This is one of the important Machine Learning Projects. Machine Learning Projects Playlist: https://youtube.com/playlist?list=PLfFghEzKVmjvuSA67LszN1dZ-Dd_pkus6 Hello everyone! I am setting up a donation campaign for my YouTube Channel. If you like my videos and wish to support me financially, you can donate through the following means: From India ๐Ÿ‘‰ UPI ID : siddhardhselvam2317@oksbi Outside of India? ๐Ÿ‘‰ Paypal id: siddhardhselvam2317@gmail.com (No donation is small. Every penny counts) Thanks in advance! Hi guys! I am Siddhardhan. I work in the field of Data Science and Machine Learning. It all started with my curiosity to learn about Artificial Intelligence and the ability of AI to solve several Real Life Problems. I worked on several Machine Learning & Deep Learning projects involving Computer Vision. I am on this journey to empower as many students & working professionals as possible with the knowledge of Machine Learning and Artificial Intelligence. Let’s build a Community of Machine Learning experts! Kindly Subscribe here๐Ÿ‘‰ https://tinyurl.com/md0gjbis I am making a “Hands-on Machine Learning Course with Python” in YouTube. I’ll be posting 3 videos per week: Monday Evening; Wednesday Evening; Friday Evening. Dataset file: https://www.kaggle.com/ronitf/heart-disease-uci Colab File Link: https://colab.research.google.com/drive/1FYGPRSEGvd0urNlZmRJHx-gq6ANn3IpX?usp=sharing Download the Course Curriculum File from here: https://drive.google.com/file/d/17i0c6SmncNuwSgr9W1MRRk3YYdEOP9Gd/view?usp=sharing LinkedIn: https://www.linkedin.com/in/siddhardhan-s-741652207 Telegram Group: https://t.me/siddhardhan Facebook group: https://www.facebook.com/groups/490857825649006/?ref=share #machinelearningcourse #machinelearningprojects
In this video, we are building a system that can predict whether a person has diabetes or not with the help of Machine Learning. This project is done in Python. In this project, we use Support Vector Machine model for the prediction. Hi guys! I am Siddhardhan. I work in the field of Data Science and Machine Learning. It all started with my curiosity to learn about Artificial Intelligence and the ability of AI to solve several Real Life Problems. I worked on several Machine Learning & Deep Learning projects involving Computer Vision. I am on this journey to empower as many students & working professionals as possible with the knowledge of Machine Learning and Artificial Intelligence. Hello everyone! I am setting up a donation campaign for my YouTube Channel. If you like my videos and wish to support me financially, you can donate through the following means: From India ๐Ÿ‘‰ UPI ID : siddhardhselvam2317@oksbi Outside of India? ๐Ÿ‘‰ Paypal id: siddhardhselvam2317@gmail.com (No donation is small. Every penny counts) Thanks in advance! Let’s build a Community of Machine Learning experts! Kindly Subscribe here๐Ÿ‘‰ https://tinyurl.com/md0gjbis I am making a “Hands-on Machine Learning Course with Python” in YouTube. I’ll be posting 3 videos per week. 2 videos on Machine Learning basics (Monday & Wednesday Evening). 1 video on a Machine Learning project (Friday Evening). Dataset File link: https://www.dropbox.com/s/uh7o7uyeghqkhoy/diabetes.csv?dl=0 Colab File Link: https://colab.research.google.com/drive/1oxnhMTlomJ4HVhPuowpPFyMt1mwuOuQo?usp=sharing Download the Course Curriculum File from here: https://drive.google.com/file/d/17i0c6SmncNuwSgr9W1MRRk3YYdEOP9Gd/view?usp=sharing LinkedIn: https://www.linkedin.com/in/siddhardhan-s-741652207 Telegram Group: https://t.me/siddhardhan Facebook group: https://www.facebook.com/groups/490857825649006/?ref=share Machine Learning Project Python [More]
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code used in this video – https://gist.github.com/pknowledge/dc4ba582623cc3682a62d7d7a69f7887 In this video I will show How To Convert Text to Speech in Python. we are going to use gtts python package to TEXT TO SPEECH IN PYTHON. gTTS stands for Google Text-to-Speech. gTTs is a Python library and Command line tool to interface with Google Translate’s text-to-speech API. it Writes text to spoken mp3 data to a file or stdout. So we are going to learn How to Use the Speech Recognition Module GTTS usinng Python 3. text to speech conversion using python is goinng to be few lines of code. #python #texttospeech #gtts โ˜…โ˜…โ˜…Top Online Courses From ProgrammingKnowledge โ˜…โ˜…โ˜… Python Programming Course โžก๏ธ http://bit.ly/2vsuMaS โšซ๏ธ http://bit.ly/2GOaeQB Java Programming Course โžก๏ธ http://bit.ly/2GEfQMf โšซ๏ธ http://bit.ly/2Vvjy4a Bash Shell Scripting Course โžก๏ธ http://bit.ly/2DBVF0C โšซ๏ธ http://bit.ly/2UM06vF Linux Command Line Tutorials โžก๏ธ http://bit.ly/2IXuil0 โšซ๏ธ http://bit.ly/2IXukt8 C Programming Course โžก๏ธ http://bit.ly/2GQCiD1 โšซ๏ธ http://bit.ly/2ZGN6ej C++ Programming Course โžก๏ธ http://bit.ly/2V4oEVJ โšซ๏ธ http://bit.ly/2XMvqMs PHP Programming Course โžก๏ธ http://bit.ly/2XP71WH โšซ๏ธ http://bit.ly/2vs3od6 Android Development Course โžก๏ธ http://bit.ly/2UHih5H โšซ๏ธ http://bit.ly/2IMhVci C# Programming Course โžก๏ธ http://bit.ly/2Vr7HEl โšซ๏ธ http://bit.ly/2W6RXTU JavaFx Programming Course โžก๏ธ http://bit.ly/2XMvZWA โšซ๏ธ http://bit.ly/2V2CoAi NodeJs Programming Course โžก๏ธ http://bit.ly/2GPg7gA โšซ๏ธ http://bit.ly/2GQYTQ2 Jenkins Course For Developers and DevOps โžก๏ธ http://bit.ly/2Wd4l4W โšซ๏ธ http://bit.ly/2J1B1ug Scala Programming Tutorial Course โžก๏ธ http://bit.ly/2PysyA4 โšซ๏ธ http://bit.ly/2PCaVj2 Bootstrap Responsive Web Design Tutorial โžก๏ธ http://bit.ly/2DFQ2yC โšซ๏ธ http://bit.ly/2VoJWwH MongoDB Tutorial Course โžก๏ธ http://bit.ly/2LaCJfP โšซ๏ธ http://bit.ly/2WaI7Ap QT C++ GUI Tutorial For Beginners โžก๏ธ http://bit.ly/2vwqHSZ โ˜…โ˜…โ˜… Online Courses to learn โ˜…โ˜…โ˜… Data Science – http://bit.ly/2BB3PV8 | http://bit.ly/2IOrpni Machine Learning – http://bit.ly/2J2xex1 Artificial Intelligence – http://bit.ly/2AeIHUR | [More]
In this video we will build a speech assistant app using the speech regonition library and Google’s text-to-speech API. Download Kite free: https://kite.com/download/?utm_medium=referral&utm_source=youtube&utm_campaign=TechGuyWeb&utm_content=speech-assistant-tutorial Code: https://github.com/bradtraversy/alexis_speech_assistant ๐Ÿ’– Become a Patron: Show support & get perks! http://www.patreon.com/traversymedia Website & Udemy Course Links: https://www.traversymedia.com Follow Traversy Media: https://www.twitter.com/traversymedia https://www.instagram.com/traversymedia https://www.facebook.com/traversymedia
Secretly aspire to be a fortune teller to impress your friends? What to build a fun python project? What if you could predict the iPhone price? Yes, even for the latest iPhone 12. #python #project #tutorial You can do this and that too with just 6 simple lines of python code. WHAT IS THE VIDEO ABOUT? โ€ข Predict iPhone (especially iPhone 12) price and show off your skills with just 6 lines of Python code Complete Code (give us a star): https://github.com/ProgrammingHero1/predict-iphone-price #python #machine #learning #machinelearning #iphone #iphone12 #apple #pythonhack #funpython #beginners #iphoneprice #iphonefuture Now, if you’re new to the programming world and don’t know what to do, go check out our app and build your own game immediately while learning. Android App: https://bit.ly/AndroidProgHero iPhone Version: https://bit.ly/iOSProgHero CHECK OUT If you hate to study, let’s hear it. Turn your books into audiobooks today: https://bit.ly/FreeAudiobookWithPython ENJOYED THE VIDEO? Save yourself from our Grandma โ โ€” she’ll come to your house to steal your old iPhone charger and sell it to Tim Cook if you don’t click on the Like button and also turn the Subscribe button from red to white. If you like and subscribe, she will be ready to make love with you. ๐Ÿ˜‰ OUR SOCIAL MEDIA Watch us on Facebook: https://bit.ly/FBProgHero Peep us on Instagram: https://bit.ly/IGProgHero Fly with us on Twitter: https://bit.ly/TWProgHero Board with us on Pinterest: https://bit.ly/PTProgHero Don’t SHARE this with your friends. They’ll know your secret. We’re always with you. Feel free to mail us anytime you need [More]
In this video, make sure you define the X’s like so. I flipped the last two lines by mistake: X = np.array(df.drop([‘label’],1)) X = preprocessing.scale(X) X_lately = X[-forecast_out:] X = X[:-forecast_out:] To forecast out, we need some data. We decided that we’re forecasting out 10% of the data, thus we will want to, or at least *can* generate forecasts for each of the final 10% of the dataset. So when can we do this? When would we identify that data? We could call it now, but consider the data we’re trying to forecast is not scaled like the training data was. Okay, so then what? Do we just do preprocessing.scale() against the last 10%? The scale method scales based on all of the known data that is fed into it. Ideally, you would scale both the training, testing, AND forecast/predicting data all together. Is this always possible or reasonable? No. If you can do it, you should, however. In our case, right now, we can do it. Our data is small enough and the processing time is low enough, so we’ll preprocess and scale the data all at once. In many cases, you wont be able to do this. Imagine if you were using gigabytes of data to train a classifier. It may take days to train your classifier, you wouldn’t want to be doing this every…single…time you wanted to make a prediction. Thus, you may need to either NOT scale anything, or you may scale the data separately. As [More]
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This video contains stepwise implementation for training dataset of “Face Emotion Recognition or Facial Expression Recognition” using Transfer Learning in Tensorflow-Keras API (00:00:00) concepts (00:23:01) installation (00:30:52) implementation (1:15:08) Live Webcame demo
In this video, I have created an AI desktop virtual assistant using python along with several related techniques to write effective python programs. This video is a part of my python for absolute beginners playlist – https://www.youtube.com/playlist?list=PLK8cqdr55Tss0puRoHDBagvj7Qjin9axl
PyData LA 2018 I will present some way in which tensor methods can be combined with deep learning, and demonstrate through Jupyter notebooks on how easy it is specify tensorized neural networks. — www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Build A Virtual Assistant Using Python โญPlease Subscribe !โญ โญSupport the channel and/or get the code by becoming a supporter on Patreon: https://www.patreon.com/computerscience โญWebsites: โ–บ http://everythingcomputerscience.com/ โญHelpful Programming Books โ–บ Python (Hands-Machine-Learning-Scikit-Learn-TensorFlow): https://amzn.to/2AD1axD โ–บ Learning Python: https://amzn.to/3dQGrEB โ–บHead First Python: https://amzn.to/3fUxDiO โ–บ C-Programming : https://amzn.to/2X0N6Wa โ–บ Head First Java: https://amzn.to/2LxMlhT #VirtualAssistant #Python
Learn Artificial Intelligence from leading experts and attain a Dual Certificate in AI and Machine Learning from world-renowned universities. Take the step towards your professional growth by obtaining expertise in the real-world application of the latest technological tools of AI. Over 500+ Hiring Partners & 8000+ career transitions over varied domains. Know More: https://glacad.me/3qSjmt0 For data sets, code files and projects associated with course please enroll for free at: https://www.greatlearning.in/academy/learn-for-free/courses/machine-learning-with-python Machine learning is changing the world that we live in. Top companies such as Facebook, Google, Microsoft and Amazon are looking for machine learning engineers and the average salary of a machine learning engineer is around 120k$ dollars. Visit Great Learning Academy, to get access to 80+ free courses with 1000+ hours of content on Data Science, Data Analytics, Artificial Intelligence, Big Data, Cloud, Management, Cybersecurity and many more. These are supplemented with free projects, assignments, datasets, quizzes. You can earn a certificate of completion at the end of the course for free. https://glacad.me/3uBbddU Get the free Great Learning App for a seamless experience, enroll for free courses and watch them offline by downloading them. https://glacad.me/3cSKlNl This full course on Machine Learning with Python will be taught by Dr Abhinanda Sarkar. Dr Sarkar is the Academic Director at Great Learning for Data Science and Machine Learning Programs. He is ranked amongst the Top 3 Most Prominent Analytics & Data Science Academicians in India. He has taught applied mathematics at the Massachusetts Institute of Technology (MIT) as well as been visiting [More]
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. โญ๏ธ Google Colaboratory Notebooks โญ๏ธ ๐Ÿ“• Module 2: Introduction to TensorFlow – https://colab.research.google.com/drive/1F_EWVKa8rbMXi3_fG0w7AtcscFq7Hi7B#forceEdit=true&sandboxMode=true ๐Ÿ“— Module 3: Core Learning Algorithms – https://colab.research.google.com/drive/15Cyy2H7nT40sGR7TBN5wBvgTd57mVKay#forceEdit=true&sandboxMode=true ๐Ÿ“˜ Module 4: Neural Networks with TensorFlow – https://colab.research.google.com/drive/1m2cg3D1x3j5vrFc-Cu0gMvc48gWyCOuG#forceEdit=true&sandboxMode=true ๐Ÿ“™ Module 5: Deep Computer Vision – https://colab.research.google.com/drive/1ZZXnCjFEOkp_KdNcNabd14yok0BAIuwS#forceEdit=true&sandboxMode=true ๐Ÿ“” Module 6: Natural Language Processing with RNNs – https://colab.research.google.com/drive/1ysEKrw_LE2jMndo1snrZUh5w87LQsCxk#forceEdit=true&sandboxMode=true ๐Ÿ“’ Module 7: Reinforcement Learning – https://colab.research.google.com/drive/1IlrlS3bB8t1Gd5Pogol4MIwUxlAjhWOQ#forceEdit=true&sandboxMode=true โญ๏ธ Course Contents โญ๏ธ โŒจ๏ธ (00:03:25) Module 1: Machine Learning Fundamentals โŒจ๏ธ (00:30:08) Module 2: Introduction to TensorFlow โŒจ๏ธ (01:00:00) Module 3: Core Learning Algorithms โŒจ๏ธ (02:45:39) Module 4: Neural Networks with TensorFlow โŒจ๏ธ (03:43:10) Module 5: Deep Computer Vision – Convolutional Neural Networks โŒจ๏ธ (04:40:44) Module 6: Natural Language Processing with RNNs โŒจ๏ธ (06:08:00) Module 7: Reinforcement Learning with Q-Learning โŒจ๏ธ (06:48:24) Module 8: Conclusion and Next Steps [More]