Description I used the Doc2Vec framework to analyze user comments on German online news articles and uncovered some interesting relations among the data. Furthermore, I fed the resulting Doc2Vec document embeddings as inputs to a supervised machine learning classifier. Can we determine for a particular user comment from which news site it originated? Abstract Doc2Vec is a nice neural network framework for text analysis. The machine learning technique computes so called document and word embeddings, i.e. vector representations of documents and words. These representations can be used to uncover semantic relations. For instance, Doc2Vec may learn that the word “King” is similar to “Queen” but less so to “Database”. I used the Doc2Vec framework to analyze user comments on German online news articles and uncovered some interesting relations among the data. Furthermore, I fed the resulting Doc2Vec document embeddings as inputs to a supervised machine learning classifier. Accordingly, given a particular comment, can we determine from which news site it originated? Are there patterns among user comments? Can we identify stereotypical comments for different news sites? Besides presenting the results of my experiments, I will give a short introduction to Doc2Vec. 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, [More]
⭐️ Content Description ⭐️ In this video, I have explained about speech emotion recognition analysis using python. This is a classification project in deep learning. I have build a LSTM neural network to build a classifier. GitHub Code Repo: http://bit.ly/dlcoderepo Dataset link: https://www.kaggle.com/ejlok1/toronto-emotional-speech-set-tess 🔔 Subscribe: http://bit.ly/hackersrealm 🗓️ 1:1 Consultation with Me: https://calendly.com/hackersrealm/consult 📷 Instagram: https://www.instagram.com/aswintechguy 🔣 Linkedin: https://www.linkedin.com/in/aswintechguy 🎯 GitHub: https://github.com/aswintechguy 🎬 Share: https://youtu.be/-VQL8ynOdVg ⚡️ Data Structures & Algorithms tutorial playlist: http://bit.ly/dsatutorial 😎 Hackerrank problem solving solutions playlist: http://bit.ly/hackerrankplaylist 🤖 ML projects tutorial playlist: http://bit.ly/mlprojectsplaylist 🐍 Python tutorial playlist: http://bit.ly/python3playlist 💻 Machine learning concepts playlist: http://bit.ly/mlconcepts ✍🏼 NLP concepts playlist: http://bit.ly/nlpconcepts 🕸️ Web scraping tutorial playlist: http://bit.ly/webscrapingplaylist Make a small donation to support the channel 🙏🙏🙏:- 🆙 UPI ID: hackersrealm@apl 💲 PayPal: https://paypal.me/hackersrealm 🕒 Timeline 00:00 Introduction to Speech Emotion Recognition 03:51 Import Modules 06:20 Load the Speech Emotion Dataset 12:34 Exploratory Data Analysis 25:20 Feature Extraction using MFCC 38:20 Creating LSTM Model 45:37 Plot the Model Results 49:15 End #speechemotionrecognition #machinelearning #hackersrealm #deeplearning #classification #lstm #datascience #model #project #artificialintelligence #beginner #analysis #python #tutorial #aswin #ai #dataanalytics #data #bigdata #programming #datascientist #technology #coding #datavisualization #computerscience #pythonprogramming #analytics #tech #dataanalysis #iot #programmer #statistics #developer #ml #business #innovation #coder #dataanalyst
Dataset: https://github.com/laxmimerit/All-CSV-ML-Data-Files-Download In this video, we will learn about spam text message classification using NLP. Natural Language Processing (NLP) is the field of Artificial Intelligence, where we analyze 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. Tokenization is breaking the raw text into small chunks. Tokenization breaks the raw text into words, sentences called tokens. These tokens help in understanding the context or developing the model for the NLP. The tokenization helps in interpreting the meaning of the text by analyzing the sequence of the words. 🔊 Watch till last for a detailed description 03:33 What is NLP? 09:38 Natural Language generation 12:42 Installing packages 21:11 Bag of words 27:07 Get started with Code 32:05 Balance the data 37:16 Exploratory data analysis 49:08 Pipeline and random forest 58:41 Support vector machine 👇👇👇👇👇👇👇👇👇👇👇👇👇👇 ✍️🏆🏅🎁🎊🎉✌️👌⭐⭐⭐⭐⭐ ENROLL in My Highest Rated Udemy Courses to 🔑 Unlock Data Science Interviews 🔎 and Tests 📚 📗 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 📊 📈 Data Visualization in Python Masterclass: Beginners to Pro Visualization in matplotlib, Seaborn, Plotly & Cufflinks, EDA on Boston Housing, Titanic, IPL, FIFA, Covid-19 Data. Course Link: https://bit.ly/udemy95off_kgptalkie 📘 📙 Natural Language Processing (NLP) in Python for Beginners NLP: Complete Text Processing with [More]
In this lesson, we will learn how to perform image classification using Convolutional Neural Network (CNN) in MATLAB. Convolutional Neural Network (CNN) is a powerful machine learning technique from the deep learning domain. A collection of diverge image is required to train CNNs. The larger the collection the richer the features that CNN learns. These features often outperform features such as HOG, LBP or SURF. Training a CNN with large collection of diverse images is not an easy task. However, there is an easy way. We can use pertained CNN to leverage the power of CNN. It saves a huge amount of time and effort when we use pretrained CNN as feature extractor. In this lesson, I used ‘ResNet-50’ as pretrained CNN and Caltech101 image dataset. Image classification using convolutional neural network is a very exciting topic. Once you will have learned how to classify images using CNN, you can do what ever you want. For example – you can train classifier to identify brain tumor, cancer cell and skin diseases. Object recognition is another excellent field where you can use the method shown in this lesson. Image classification using CNN in MATLAB is not a straightforward approach. However, the strategy used in this lecture has made it simple. Each function used here, the role and outcome of each line and explanation with example where needed have made this lesson the best lesson on image classification using convolutional neural network in MATLAB. After completing this lesson, you will learn: 1. [More]
***AI and Deep Learning using TensorFlow: https://edureka.co/ai-deep-learning-with-tensorflow *** This Edureka Live video on “Tensorflow Image Classification” will provide you with a comprehensive and detailed knowledge of Image classification and how it can be implemented using Tensorflow. It covers the following topics: 1:04 What is TensorFlow 1:35 Applications of TensorFlow 2:32 Image Classification 3:15 Fashion MNIST 16:42 CIFAR-10 ———————————————————– Machine Learning Podcast – http://bit.ly/2IGLYCc Complete Youtube Playlist here: https://bit.ly/2OhZEpz Deep Learning Blog Series: https://bit.ly/2xVIMe1 Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV 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 ———————————————————– #edureka #edurekadeeplearning #tensorflow #imageclassification #deeplearning #tensorflowtutorial About the course: Edureka’s Deep Learning in TensorFlow with Python Certification Training is curated by industry professionals as per the industry requirements & demands. You will master the concepts such as SoftMax function, Autoencoder Neural Networks, Restricted Boltzmann Machine (RBM) and work with libraries like Keras & TFLearn. The course has been specially curated by industry experts with real-time case studies. —————————————————— Objectives: Deep Learning in TensorFlow with Python Training is designed by industry experts to make you a Certified Deep Learning Engineer. The Deep Learning in TensorFlow course offers: In-depth knowledge of Deep Neural Networks Comprehensive knowledge of various Neural Network architectures such as Convolutional Neural Network, Recurrent Neural Network, Autoencoders Implementation of Collaborative Filtering with RBM The exposure to real-life industry-based projects which will be executed using TensorFlow library Rigorous involvement of an SME throughout the AI & Deep Learning Training to learn industry [More]
In this tutorial, we will cover Natural Language Processing for Text Classification with NLTK & Scikit-learn. Remember the last Natural Language Processing project we did? (http://bit.ly/2Ittrop) We will be using all that information to create a Spam filter. This tutorial will also cover Feature Engineering and ensemble NLP in text classification. This project will use Jupiter Notebook running Python 2.7. Let’s get started! You will find the source code to this project here: https://github.com/eduonix/nlptextclassification 👉Enjoy Extra 50% off on the Below E-Degrees with certification – (APPLY COPOUN – COL50) 🔹AI & ML E-degree- http://bit.ly/2mEUCYC 🔹MERN Stack Developer E-Degree Program – http://bit.ly/2pFSz7J 🔹DevOps E-degree – http://bit.ly/2J6Gf7u 🔹Cloud Computing E-Degree – https://bit.ly/2Hyv5dO 🔹Cybersecurity E-Degree – https://bit.ly/2Hyv5dO 🔹IoT E-degree – The Novice to Expert Program in IOT – https://bit.ly/3dTtSJP 🔹Advance Artificial Intelligence & Machine Learning E-Degree – https://bit.ly/336NwOU ★★★The Best courses & Bundles to do with Eduonix with Flat 50% OFF ★★★ ( APPLY COUPON – COL50) 1.Learn Machine Learning By Building Projects – http://bit.ly/2MxMSSl 2.The Complete Web Development Course – Build 15 Projects – http://bit.ly/32Ah9oW 3.The Full Stack Web Development – http://bit.ly/2MZDBRV 4.Projects In Laravel : Learn Laravel Building 10 Projects – http://bit.ly/2MAiHtH 5.Mathematical Foundation For Machine Learning and AI – http://bit.ly/2N23Eb1 1.Mighty Digital Marketing Bundle – https://bit.ly/2X3xK3U 2.AI and Machine Learning Guru – https://bit.ly/3okSbFG 3.Game Development Masterpack – https://bit.ly/3mdTSTk 4. Mighty Web Development Bundle 2.0 – https://bit.ly/3ouO3TA ✔ Get Exclusive Flat 30% off on Our Lifetime membership – https://bit.ly/3dO6oGc ( APPLY COUPON : YTLIFE30) #machinelearning #machinelearningprojects #eduonix Thank you for watching! [More]
Now that we understand some of the basics of of natural language processing with the Python NLTK module, we’re ready to try out text classification. This is where we attempt to identify a body of text with some sort of label. To start, we’re going to use some sort of binary label. Examples of this could be identifying text as spam or not, or, like what we’ll be doing, positive sentiment or negative sentiment. Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Hi! My name is Andre and this week, we will focus on text classification problem. Although, the methods that we will overview can be applied to text regression as well, but that will be easier to keep in mind text classification problem. And for the example of such problem, we can take sentiment analysis. That is the problem when you have a text of review as an input, and as an output, you have to produce the class of sentiment. For example, it could be two classes like positive and negative. It could be more fine grained like positive, somewhat positive, neutral, somewhat negative, and negative, and so forth. And the example of positive review is the following. “The hotel is really beautiful. Very nice and helpful service at the front desk.” So we read that and we understand that is a positive review. As for the negative review, “We had problems to get the Wi-Fi working. The pool area was occupied with young party animals, so the area wasn’t fun for us.” So, it’s easy for us to read this text and to understand whether it has positive or negative sentiment but for computer that is much more difficult. And we’ll first start with text preprocessing. And the first thing we have to ask ourselves, is what is text? You can think of text as a sequence, and it can be a sequence of different things. It can be a sequence of characters, that is a very low level [More]
Can AI be used to detect various diseases from a simple body scan? Yes! Normally, doctors train for years to do this and the error rate is still relatively high. From mammograms to cat scans, AI can diagnose a disease better than any human can if given the right training dataset. This will drastically reduce patient death, save medical practices a lot of money, and aid doctors in the patient care process. Everyone will win and its important to remember that AI won’t replace doctors, it will become the most powerful tool they’ve ever used. And once enough AI startups start impacting the field of healthcare, it will become as common a tool as the stethoscope has been. Code for this video: https://github.com/llSourcell/AI_in_Medicine_Clinical_Imaging_Classification Please Subscribe! And like. And comment. That’s what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval Curriculum: https://github.com/llSourcell/AI_For_Business_Curriculum More learning resources: https://www.youtube.com/watch?v=3LkbUxqGTfo https://www.youtube.com/watch?v=S4GvBCMfRew https://www.youtube.com/watch?v=LxHHsujnF9c https://www.youtube.com/watch?v=ZPXCF5e1_HI https://www.youtube.com/watch?v=QfNvhPx5Px8&t=202s Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Sign up for the next course at The School of AI: https://www.theschool.ai https://github.com/gregwchase/dsi-capstone And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Provides steps for applying Image classification & recognition with easy to follow example. R file: https://goo.gl/fCYm19 Data: https://goo.gl/To15db Machine Learning videos: https://goo.gl/WHHqWP To install EBimage package, you can run following 2 lines; install.packages(“BiocManager”) BiocManager::install(“EBImage”) Uses TensorFlow (by Google) as backend. Includes, – load keras and EBImage packages – read images – explore images and image data – resize and reshape images – one hot encoding – sequential model – compile model – fit model – evaluate model – prediction – confusion matrix Image Classification & Recognition with Keras is an important tool related to analyzing big data or working in data science field. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Lecture 2 formalizes the problem of image classification. We discuss the inherent difficulties of image classification, and introduce data-driven approaches. We discuss two simple data-driven image classification algorithms: K-Nearest Neighbors and Linear Classifiers, and introduce the concepts of hyperparameters and cross-validation. Keywords: Image classification, K-Nearest Neighbor, distance metrics, hyperparameters, cross-validation, linear classifiers Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture2.pdf ————————————————————————————– Convolutional Neural Networks for Visual Recognition Instructors: Fei-Fei Li: http://vision.stanford.edu/feifeili/ Justin Johnson: http://cs.stanford.edu/people/jcjohns/ Serena Yeung: http://ai.stanford.edu/~syyeung/ Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. Website: http://cs231n.stanford.edu/ For additional learning opportunities please visit: http://online.stanford.edu/