github link: https://github.com/krishnaik06/Gender-Recognition-and-Age-Estimator weights: https://drive.google.com/file/d/12Ub2ZUtiYXL1QKUPlAy6oOG4Qhn0GM0H Please donate if you want to support the channel through GPay UPID, Gpay: krishnaik06@okicici Discord Server Link: https://discord.gg/tvAJuuy Telegram link: https://t.me/joinchat/N77M7xRvYUd403DgfE4TWw Please join as a member in my channel to get additional benefits like materials in Data Science, live streaming for Members and many more https://www.youtube.com/channel/UCNU_lfiiWBdtULKOw6X0Dig/join Please do subscribe my other channel too https://www.youtube.com/channel/UCjWY5hREA6FFYrthD0rZNIw Connect with me here: Twitter: https://twitter.com/Krishnaik06 Facebook: https://www.facebook.com/krishnaik06 instagram: https://www.instagram.com/krishnaik06
Today’s modern-day machine learning data centers require complex computations and fast, efficient data delivery. The NVIDIA Scalable Hierarchical Aggregation and Reduction Protocol (SHARP) takes advantage of the in-network computing capabilities in the NVIDIA Quantum switch, dramatically improving the performance of distributed machine learning workloads. https://developer.nvidia.com/networking/hpc-x #infiniBand #ISC21 #Networking
It’s time to reveal the magician’s secrets behind deploying machine learning models! In this tutorial, I go through an example machine learning deployment scenario using Google Cloud and an image recognition app called Food Vision 🍔👁. Get all the code on GitHub – https://github.com/mrdbourke/cs329s-ml-deployment-tutorial Slides – https://github.com/mrdbourke/cs329s-ml-deployment-tutorial/blob/main/CS329s-deploying-ml-models-tutorial.pdf Full CS329s syllabus – https://stanford-cs329s.github.io/index.html Learn ML (my beginner-friendly ML course) – https://dbourke.link/mlcourse Connect elsewhere: Web – https://www.mrdbourke.com Get email updates on my work – https://www.mrdbourke.com/newsletter Timestamps: 0:00 – Intro/hello 1:42 – Presentation start (what we’re going to cover) 6:00 – Food Vision 🍔👁 (the app we’re building) recipe 11:16 – The end goal we’re working towards (data flywheel) 13:07 – The data flywheel: the holy grail of ML apps 14:57 – Tesla’s data flywheel 17:02 – Food Vision’s data flywheel 18:24 – Deploying a model on the cloud outline 21:14 – Steps we’re going to go through to deploy our app 27:06 – Question: “How do you identify hard samples in your data?” 37:53 – Creating a bucket on Google Storage 45:51 – Uploading to Google Storage from Google Colab 48:02 – Deploying a model to AI Platform 52:50 – Creating an AI Platform Prediction version 58:10 – Creating a Service Account to access our model on Google Cloud 1:02:32 – Authenticating our app with our private Service Account key 1:09:19 – What happens when we run make gcloud-deploy 1:11:27 – Problems you’ll run into when deploying your models 1:20:12 – Extensions you could perform on this tutorial 1:20:49 – Part 2 [More]
🙋‍♂️ We’re launching an exclusive part-time career-oriented certification program called the Zero to Data Science Bootcamp with a limited batch of participants. Learn more and enroll here: https://www.jovian.ai/zero-to-data-science-bootcamp 🔗 Resources used • Notebook created in the workshop: https://jovian.ai/aakashns-6l3/deep-learning-project-live • Guidelines and datasets for deep learning projects: https://jovian.ai/learn/deep-learning-with-pytorch-zero-to-gans/assignment/course-project 💻 In this live hands-on workshop, we’ll build a deep learning project from scratch in 2.5 – 3 hours. You can follow along to build your own project. Take our Free Certification Course “Deep Learning with PyTorch: Zero to GANs” to learn the required skills: http://zerotogans.com Here’s an outline of the workshop: 📄 Find an interesting unstructured dataset online (images, text, audio, etc.) ❓ Identify the type of problem: regression, classification, generative modeling, etc. 🤔 Identify the type of neural network you need: fully connected, convolutional, recurrent, etc. 🛠 Prepare the dataset for training (set up batches, apply augmentations & transforms) 🔃 Define a network architecture and set up a training loop ⚡ Train the model and evaluate its performance using a validation/test set 🧪 Experiment with different network architectures, hyperparameters & regularization techniques 📰 Document and publish your work in a Jupyter notebook or blog post 📒 Datasets from the workshop: Chest X-Ray Images (Pneumonia) – https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia Fruits 360 – https://www.kaggle.com/moltean/fruits Flowers Recognition – https://www.kaggle.com/alxmamaev/flowers-recognition Malaria Cell Images Dataset – https://www.kaggle.com/iarunava/cell-images-for-detecting-malaria Intel Image Classification – https://www.kaggle.com/puneet6060/intel-image-classification Best Artworks of All Time – https://www.kaggle.com/ikarus777/best-artworks-of-all-time CelebFaces Attributes (CelebA) Dataset – https://www.kaggle.com/jessicali9530/celeba-dataset Open Datasets – https://github.com/JovianML/opendatasets ⚙ Check out these projects for inspiration: • Blindness [More]
In this keynote, we announced the launch of Databricks Machine Learning, the first enterprise ML solution that is data-native, collaborative, and supports the full ML lifecycle. This launch introduces a new purpose-built product surface Databricks ML provides a solution for the full ML lifecycle by supporting any data type at any scale, enabling users to train ML models with the ML framework of their choice and managing the model deployment lifecycle – from large scale batch scoring to low latency online serving. Additionally, we announced two machine learning capabilities. First, Databricks Feature Store, the first feature store codesigned with a data and MLOps platform. Second, Databricks AutoML, a ‘glass box’ approach to autoML that accelerates model development without sacrificing control and transparency. Finally, this keynote covers and end-to-end demo of Databricks Machine Learning. Register for free to see the rest of the keynotes and exciting announcements live, plus over 200+ sessions. Learn from the creators and top contributors of technologies like PyTorch, TensorFlow, MLflow, Delta Lake, Apache Spark, Hugging Face, DBT and more. https://databricks.com/dataaisummit/north-america-2021 Connect with us: Website: https://databricks.com Facebook: https://www.facebook.com/databricksinc Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc/
Dr. Dennis Ong is a Technology Evangelist. Dr. Ong is Distinguished Architect and Managing Principal at Verizon. Previously, he served as Chief Architect and Director at Nokia, Alcatel-Lucent, Lucent, and AT&T. He and his teams have launched many innovative and award-winning solutions combining the strengths of high tech companies and multitude of start-up companies. At Verizon, he is working with startups to utilize IoT and Machine Learning to create Smart City solutions to address the most challenging problems facing cities and municipalities. At Nokia, he led a recently acquired start-up to develop the IoT IMPACT platform which received the “”Best IoT Innovation for Mobile Networks”” award at the 2017 Mobile World Congress. At Alcatel-Lucent, he and his team collaborated with three start-ups, based in India, Israel, and Silicon Valley, in creating a highly scalable video optimization and analytics platform that served tens of millions of mobile subscribers worldwide. At Lucent, he launched the first packet-based cellular small cell solution with a start-up in Boston. Originally from Hong Kong, Dennis received Ph.D. in Electrical Engineering from the Ohio State University as University Fellow and MBA with honors from the University of Chicago. He was an adjunct faculty at the Ohio State University. Dennis and his wife, Timmy, enjoy coaching Christian marriage retreats. They are proud parents of three children – Joshua, Jeremiah, and Hannah. Dennis is an active learner and an avid swimmer. Dr. Dennis Ong is Distinguished Architect and Managing Principal at Verizon. Previously, he served as Chief Architect and [More]
We’ve learned how to train different machine learning models and make predictions, but how do we actually choose which model is “best”? We’ll cover the train/test split process for model evaluation, which allows you to avoid “overfitting” by estimating how well a model is likely to perform on new data. We’ll use that same process to locate optimal tuning parameters for a KNN model, and then we’ll re-train our model so that it’s ready to make real predictions. Download the notebook: https://github.com/justmarkham/scikit-learn-videos Quora explanation of overfitting: http://www.quora.com/What-is-an-intuitive-explanation-of-overfitting/answer/Jessica-Su Estimating prediction error: https://www.youtube.com/watch?v=_2ij6eaaSl0&t=2m34s Understanding the Bias-Variance Tradeoff: http://scott.fortmann-roe.com/docs/BiasVariance.html Guiding questions for that article: https://github.com/justmarkham/DAT8/blob/master/homework/09_bias_variance.md Visualizing bias and variance: http://work.caltech.edu/library/081.html WANT TO GET BETTER AT MACHINE LEARNING? HERE ARE YOUR NEXT STEPS: 1) WATCH my scikit-learn video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A 2) SUBSCRIBE for more videos: https://www.youtube.com/dataschool?sub_confirmation=1 3) JOIN “Data School Insiders” to access bonus content: https://www.patreon.com/dataschool 4) ENROLL in my Machine Learning course: https://www.dataschool.io/learn/ 5) LET’S CONNECT! – Newsletter: https://www.dataschool.io/subscribe/ – Twitter: https://twitter.com/justmarkham – Facebook: https://www.facebook.com/DataScienceSchool/ – LinkedIn: https://www.linkedin.com/in/justmarkham/
MIT RES.LL-005 Mathematics of Big Data and Machine Learning, IAP 2020 Instructor: Jeremy Kepner, Vijay Gadepally View the complete course: https://ocw.mit.edu/RES-LL-005IAP20 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP62uI_DWNdWoIMsgPcLGOx-V This lecture provided an overview on artificial intelligence and took a deep dive on machine learning, including supervised learning, unsupervised learning, and reinforcement learning. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
** Machine Learning Training with Python: https://www.edureka.co/machine-learning-certification-training ** This Edureka video on ‘Mathematics for Machine Learning’ teaches you all the math needed to get started with mastering Machine Learning. It teaches you all the necessary topics and concepts of Linear Algebra, Multivariate Calculus, Statistics, and Probability and also dives into the actual implementation of these topics. Blog Link: https://bit.ly/2PX5lIp Check out our playlist for more videos: http://bit.ly/2taym8X —————————————————————————— Subscribe to our channel to get video updates: https://bit.ly/2PYu1jD Hit the subscribe button above. 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/ SlideShare: https://www.slideshare.net/EdurekaIN/ #Edureka #MachineLearning #MathematicsForMachineLearning # —————————————————————————— How does it work? 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 with a Grade and a Verifiable Certificate! —————————————————————————— About the Course Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. Machine Learning training will provide a deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in Python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You [More]
🔥 Get the pdf of this course: https://bit.ly/getpdf_covidprediction 🔥 Great Learning brings you this live session on ‘Predicting COVID-19 With Machine Learning’.In this session, we will take a COVID-19 dataset and understand how the disease has spread across different countries. We will perform some data manipulation and data visualization operations on top of the dataset. We will also be implementing a linear regression algorithm to understand the number of active and recovered cases 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/3duVMLE The data-set used is from ‘Our World in Data’. You can download the dataset from this link:https://github.com/owid/covid-19-data/tree/master/public/data Get the free Great Learning App for a seamless experience, enrol for free courses and watch them offline by downloading them. https://glacad.me/3cSKlNl About Great Learning: – Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more. – For more interesting tutorials, don’t forget to subscribe to our channel: https://bit.ly/2s92TDX – Learn More at https://www.greatlearning.in/ For more updates [More]
Can you predict the Bitcoin Price with Machine Learning? It seems like it’s possible! Using an LSTM algorithm, I showcase how you can use machine learning to predict prices of cryptocurrencies. Machine Learning can most definitely be used as a support in your bitcoin investing, and as a predictor of the price of cryptocurrencies. Find the code at: https://github.com/OscarAlsingCommunity/Predict-Cryptocurrency-Price-With-Machine-Learning Resources: http://colah.github.io/posts/2015-08-Understanding-LSTMs/ https://github.com/NourozR/Stock-Price-Prediction-LSTM https://www.kaggle.com/pablocastilla/predict-stock-prices-with-lstm/ PEACE! ———————————————————————————————- JOIN NO PMO NATION 👬: ———————————————————————————————- 👬 Instagram: https://www.instagram.com/nopmonation/ ———————————————————————————————- JOIN THE ARMY OF HAPPIER AND STRONGER PEOPLE 👬: ———————————————————————————————- 🎓 SUBSCRIBE ON YOUTUBE: https://goo.gl/JDWLKZ 🎓 JOIN US ON SLACK: https://goo.gl/srBTka 🎓 JOIN MY EXCLUSIVE MAILING LIST: http://eepurl.com/di4dNj ———————————————————————————————- POPULAR EDUCATION SERIES 💝: ———————————————————————————————- 🎓 MASTER NOFAP: https://goo.gl/z6E6HU 🎓 BECOME HAPPIER: https://goo.gl/DZ4cps 🎓 ATTRACT WOMEN: https://goo.gl/MKxdeS 🎓 MACHINE LEARNING: https://goo.gl/hULpKQ 🎓 ARTIFICIAL INTELLIGENCE: https://goo.gl/pzCWpU ———————————————————————————————- HOW TO ASK OSCAR QUESTIONS 🎤: ———————————————————————————————- 👬 MESSAGE ME ON INSTAGRAM: https://www.instagram.com/oscaralsing/ 👬 ASK ME ON SLACK: https://goo.gl/srBTka Linkedin: https://www.linkedin.com/in/oscaralsing/ Facebook: https://www.facebook.com/oscaralsingcom Website: http://www.oscaralsing.com ———————————————————————————————- PRODUCTS I LOVE ❤️: ———————————————————————————————- LIFE-CHANGING BOOKS: https://goo.gl/MMH4XG MY CAMERA/PROGRAMMING GEAR: https://goo.gl/WPCkZr ———————————————————————————————- ABOUT OSCAR 💝: ———————————————————————————————- Oscar is a leader, educator and programmer specialised in Artificial Intelligence and Machine Learning who strives to build a world where all leadership spawns from an intrinsic compassion for others. He is heavily interest in mindfulness and meditation and is a daily Brazilian Jiu-Jitsu practitioner. Furthermore, he Loves lifting heavy things and reads a lot of books and believes in a world where compassion and mutual understanding and respect permeate all of our actions. 🎉 Leader of [More]
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? • 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 ⁠— 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. 😉 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]
#ArtificalIntelligence #MachineLearning #collegesuggest #KnowYourCourse Welcome to College Suggest! Let’s take a look at one of the most innovative and widely popular courses available today – CSE with a specialization in Artificial Intelligence and Machine Learning, which offers great opportunities due to plenty of demand for skilled professionals. 00:00 Intro 01:10 What exactly is artificial intelligence? 1:54 What to study? 02:57 Core components of AI and ML 03:34 Where to study? 04:54 Why Artificial Intelligence? 06:02 Job roles 06:59 Salary trends 07:38 Top Recruiters 08:22 Salary 09:16 Tips 09:58 How to stay updated? 10:12 Reasons to study AI & ML
Sentiment analysis is an active research field where researchers aim to automatically determine the polarity of text [1], either as a binary problem or as a multi-class problem where multiple levels of positiveness and negativeness are reported. Recently, there is an increasing interest in going beyond sentiment, and analyzing emotions such as happiness, fear, anger, surprise, sadness and others. Emotion detection has many use cases for both enterprises and consumers. The best-known examples are customer service performance monitoring [2], and social media analysis [3]. In this talk, we present a new algorithm based on deep learning, which not only outperforms state-of-the-art method [4] in emotion detection from text, but also automatically decides on length of emotionally-intensive text blocks in a document. Our talk presents the problem by examples, with business motivations related to the Microsoft Cognitive Services suite. We present a technique to capture both semantic and syntactic relationships in sentences using word embeddings and Long Short-Term Memory (LSTM) based modeling. Our algorithm exploits lexical information of emotions to enrich the data representation. We present empirical results based on ISAER and SemEval-2007 datasets [5,6]. We then motivate the problem of detecting emotionally-intensive text blocks of various sizes, along with an entropy-based technique to solve it by determining the granularity on which the emotions model is applied. We conclude with a live demonstration of the algorithm on diverse types of data: interviews, customer service, and social media.
Artificial intelligence and machine learning are changing the world. Today, business leaders and developers have open-source AI/ML learning systems at their disposal to build intelligent environments. The question is, which one suits your needs the best? We’ve got the top 10 Open-Source AI/ML Learning Systems for you to consider right here.
Deep learning is a key technology driving the current artificial intelligence (AI) megatrend. You may have heard of some mainstream applications of deep learning, but how many of them would you consider applying to your engineering and science applications? MATLAB and Simulink developers have purpose-built the MATLAB deep learning functionality for engineering and science workflows. We understand that success goes beyond just developing a deep learning model. Ultimately, models need to be incorporated into an entire system design workflow to deliver a product or a service to the market. The aim of the session is to provide an overview of how MATLAB enables you to take advantage of disruptive technologies like deep learning. We will: • Show where deep learning is being applied in engineering and science, and how it is driving MATLAB’s development. • Demonstrate a workflow for how you can research, develop and deploy your own deep learning application. • Outline what MATLAB and Simulink engineers can do to help support you achieve success with deep learning. Demo files (note: this is a large download at 433 MB): https://www.mathworks.com/content/dam/mathworks/mathworks-dot-com/company/events/post-event-email/3228951-Presentation.zip Check out these other great resources: * See if your school has a MATLAB campus license: https://bit.ly/33hvREb * Get a free product trial: https://bit.ly/2SeH5mA * MATLAB EXPO 2020 On Demand: https://bit.ly/3n8KgKL * Join the Simulink Student Challenge: https://bit.ly/30iLVUb * Learn more about MATLAB: https://bit.ly/3l5xkDR * Learn more about Simulink: https://bit.ly/36lYuSw * See what’s new in MATLAB and Simulink: https://bit.ly/33iHRp0
🔥Edureka AWS Training: https://www.edureka.co/aws-certification-training This Edureka video on “Deploy an ML Model using Amazon Sagemaker” discusses what is Amazon Sagemaker and how you can build, train and deploy your Machine Learning Models in Amazon Sagemaker. These are the topics covered in the AWS Machine Learning Tutorial video: 00:00:00 Introduction 00:01:14 What is Amazon Sagemaker? 00:04:21 Create your AWS Account 00:06:46 Create your First Notebook Instance 00:17:39 Train your Model on AWS 00:24:37 Deploy your Model on AWS 00:26:33 Evaluate your Model on AWS 00:29:03 AWS SageMaker Case Study: Grammarly 🔹Check Edureka’s complete DevOps playlist here: http://goo.gl/O2vo13 🔹Check Edureka’s Blog playlist here: https://bit.ly/3gfNuZr ——————————————————————————————– 🔴Subscribe 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/ #Edureka #DeployAnMlModelUsingAmazonSagemaker #AWSTutorial #AWSCertification #AWSTraining #AWSMachineLearning #AWSMLDeployment #MachineLearningOnCloud #CloudComputing #AWS ——————————————————————————————– How it Works? 1. This is a 5 Week Instructor led Online Course. 2. Course consists of 30 hours of online classes, 30 hours of assignment, 20 hours of project 3. 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. 4. You will get Lifetime Access to the recordings in the LMS. 5. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! – – – – – – – – – – – – – – About [More]
Machine Learning courses and articles from coursera, edX, Udacity, dataCamp to udemy. Here are the resources link collection: —- R Language —- 1. https://cognitiveclass.ai/courses/machine-learning-r/ 2. https://www.datacamp.com/community/tutorials/machine-learning-in-r —- Python Language —- 1. https://www.coursera.org/learn/machine-learning-with-python 2. https://www.coursera.org/learn/python-machine-learning —- Java Language —- 1. https://skymind.ai/wiki/java-ai 2. EdX : Course Lists https://www.edx.org/learn/java 3. Coursera: Course List https://www.coursera.org/courses?query=java —- Machine Learning fundamentals —- 1. EdX: https://www.edx.org/course/machine-learning-fundamentals 2. Udacity: https://in.udacity.com/course/intro-to-machine-learning–ud120-india 3. Udemy: https://www.udemy.com/machine-learning-for-beginners/ 4. Coursera: https://www.coursera.org/learn/ml-foundations 5. Google https://developers.google.com/machine-learning/crash-course/prereqs-and-prework —- ML Tools and packages —- 1. NumPy, SciPy, matplotlib a. EdX https://www.edx.org/course/python-data-science-uc-san-diegox-dse200x 2. TensorFlow a. Coursera : https://www.coursera.org/learn/intro-tensorflow b. EdX : https://www.edx.org/course/deep-learning-with-tensorflow 3.Scikit Learn a. https://www.datacamp.com/courses/supervised-learning-with-scikit-learn b. Udemy https://www.udemy.com/machine-learning-with-scikit-learn/ c. https://www.dataschool.io/machine-learning-with-scikit-learn/ 4. Pandas a. https://www.udemy.com/data-analysis-with-pandas/ —- Nano Degrees —- 1. IBM : https://imarticus.org/machine-learning-prodegree 2. Coursera https://www.coursera.org/specializations/data-science-python 3. EdX Microsoft : https://www.edx.org/microsoft-professional-program-artificial-intelligence 4. Nanodegree https://in.udacity.com/course/python-foundation-nanodegree–nd002-inpy —- Machine Learning Maths —- 1. https://towardsdatascience.com/the-mathematics-of-machine-learning-894f046c568 2. EdX https://www.edx.org/course/essential-math-machine-learning-python 3. Coursera https://www.coursera.org/specializations/mathematics-machine-learning Udemy 4. https://www.udemy.com/calculus1/ 5. https://www.udemy.com/statshelp/ 6. https://www.udemy.com/integralcalc-algebra I am sure this will help you and please share this video — Follow me — : Twitter – https://twitter.com/bitfumes Facebook – https://www.facebook.com/Bitfumes/ Instagram – https://www.instagram.com/bitfumes/ (ask me questions!) — QUESTIONS? — Leave a comment below and I or someone else can help you. For quick questions you may also want to ask me on Twitter, I respond almost immediately. Email me support@bitfumes.com Thanks for all your support!
Intellipaat Artificial Intelligence Course:- https://intellipaat.com/ai-deep-learning-course-with-tensorflow/ Artificial Intelligence Webinar video is an introduction to what is Ai?, what is Deep Learning?, Industries getting disrupted by AI & Deep Learning, Machine Learning vs AI, Robotics, Tensorflow, Career in AI & Future of AI in this Artificial Intelligence Tutorial in detail. Interested to learn Deep Learning & Machine Learning still more? Please check similar Artificial Intelligence Tutorial and other Artificial Intelligence Course Blogs here:- https://goo.gl/rFFw9L Watch complete Artificial Intelligence, Deep Learning & Machine Learning tutorials here:- https://goo.gl/gyf2g3 This Artificial Intelligence Tutorial conference video helps you to learn following topics: 12:48 – What is Ai? 20:18 – Artificial Intelligence history 24:10 – How A.I. Works? 27:17 – What is Deep Learning? 31:37 – Industries getting disrupted by A.I. 35:30 – Applications of Artificial Intelligence 44:55 – Future of AI 53:33 – Job Trends in Artificial Intelligence Are you looking for something more? Enroll in our Artificial Intelligence Course and become a certified A.I. Professional (https://goo.gl/RdA17B). It is a 32 hrs instructor led AI for everyone training provided by Intellipaat which is completely aligned with industry standards and certification bodies. If you’ve enjoyed this Deep Learning, Machine Learning and Robotics tutorial, Like us and Subscribe to our channel for more similar Robotics, Machine Learning vs AI videos and free tutorials. Got any questions about Artificial Intelligence Course & Future of AI? Ask us in the comment section below. —————————- Intellipaat Edge 1. 24*7 Life time Access & Support 2. Flexible Class Schedule 3. Job Assistance [More]
Download the audio at InfoQ: http://bit.ly/2FxY1NG Maciej Ceglowski wonders what tech companies can do to reduce the amount of data collected, closing the path to mass surveillance and bringing some morality in using ML with this data. This presentation was recorded at ETE 2017. For more awesome presentations on innovator and early adopter topics check InfoQ’s selection of talks from conferences worldwide http://bit.ly/2tm9loz
You are a HUGE football fan. Every week you pick winners in an NFL pick-em’ league. Somehow, all that fan experience doesn’t translate into consistently winning your league. Perhaps you need a more systematic approach that takes some of the emotion out of it. Where to start? Betting spreads provide a consistent and robust mechanism for encapsulating the variables and predicting outcomes of NFL games. In a weekly confidence pool, spreads also perform very well as opposed to intuition-based guessing and “knowledge” from years of being a fan. Can we do better? In this talk, we will discuss an approach to use machine learning algorithms to make improvements on the spread method of ranking winners on a weekly basis as an exercise in winning your friendly neighborhood confidence pool. https://datadialogs.ischool.berkeley.edu/2016/schedule/using-machine-learning-predicting-nfl-games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amit Bhattacharyya Senior Data Scientist Teachers Pay Teachers Amit is the Senior Data Scientist at Teachers Pay Teachers, an online marketplace for teachers to buy, sell and share original educational resources. At TpT, Amit works on developing both technical and modeling infrastructure to analyze customer behavior and ways to more effectively connect buyers and sellers. Amit also teaches in the MIDS program at the UC Berkeley School of Information. He received a Ph.D. in physics from Indiana Universtiy. Previously, he did a two-year stint in advertising, and worked as a quantitative [More]
In this video we will understand how we can implement Diabetes Prediction using Machine Learning. The dataset is taken from Kaggle. Please subscribe and support the channel. github url: https://github.com/krishnaik06/Diabetes-Prediction Data Science Projects playlist: https://www.youtube.com/watch?v=5Txi0nHIe0o&list=PLZoTAELRMXVNUcr7osiU7CCm8hcaqSzGw NLP playlist: https://www.youtube.com/watch?v=6ZVf1jnEKGI&list=PLZoTAELRMXVMdJ5sqbCK2LiM0HhQVWNzm Statistics Playlist: https://www.youtube.com/watch?v=GGZfVeZs_v4&list=PLZoTAELRMXVMhVyr3Ri9IQ-t5QPBtxzJO Feature Engineering playlist: https://www.youtube.com/watch?v=NgoLMsaZ4HU&list=PLZoTAELRMXVPwYGE2PXD3x0bfKnR0cJjN Computer Vision playlist: https://www.youtube.com/watch?v=mT34_yu5pbg&list=PLZoTAELRMXVOIBRx0andphYJ7iakSg3Lk Data Science Interview Question playlist: https://www.youtube.com/watch?v=820Qr4BH0YM&list=PLZoTAELRMXVPkl7oRvzyNnyj1HS4wt2K- You can buy my book on Finance with Machine Learning and Deep Learning from the below url amazon url: https://www.amazon.in/Hands-Python-Finance-implementing-strategies/dp/1789346371/ref=sr_1_1?keywords=krish+naik&qid=1560943725&s=gateway&sr=8-1
Check out my follow-up video where I explain how some financial market crashes can be predicted: https://youtu.be/6×4-GcIFDlM
The aim of the project is about the detection of the emotions elicited by the speaker while talking. As an example, speech produced in a state of fear, anger, or joy becomes loud and fast, with a higher and wider range in pitch, whereas emotions such as sadness or tiredness generate slow and low-pitched speech. Detection of human emotions through voice-pattern and speech-pattern analysis has many applications such as better assisting human-machine interactions. In particular, we are presenting a classification model of emotion elicited by speeches based on deep neural networks (CNNs), SVM, MLP Classification based on acoustic features such as Mel Frequency Cepstral Coefficient (MFCC). The model has been trained to classify eight different emotions (neutral, calm, happy, sad, angry, fearful, disgust, surprise). Our evaluation shows that the proposed approach yields accuracies of 86%, 84%, and 82% using CNN, MLP Classifier and SVM Classifiers, respectively, for 8 emotions using Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset and Toronto Emotional Speech Set (TESS) Dataset. Read more: https://tinyurl.com/y73zmdu3 For more detail visit our website===== Leadingindia.ai Follow us on Twitter ===== https://twitter.com/LeadingindiaAI?s=08 Flow us on instagram===== https://www.instagram.com/technology_ucan/ Like our Facebook page===== https://www.facebook.com/techucan/ Also subscribe this channel for Technical videos===== https://www.youtube.com/channel/UCdimTrr7ZsKbhI50j3VmJiQ Contact us===== madhushi.verma@bennett.edu.in Plz like, comment, share, subscribe and don’t forget to press Bell icon for new updates😊