The top 20 MLS courses you need to take

1. ML Algorithms

ML Algorithms is the fourth Course in the AWS Certified Machine Learning Specialty specialization. This Course enables learners to deep dive Machine Learning Algorithms. This course is divided into two modules and each module is further segmented by Lessons and Video Lectures. This course facilitates learners with approximately 2:00-2:30 Hours Video lectures that provide both Theory and Hands -On knowledge. Also, Graded and Ungraded Quiz are provided with every module in order to test the ability of learners. Module 1: ML Algorithms- Part 1 Module 2: ML Algorithms- Part 2 Minimum two year of hands-on experience in architecting, building or running ML/deep learning workloads on the AWS Cloud. By the end of this course, learners will be able to : - Determine algorithm concepts in ML - Design Regression algorithms and Classification based algorithms - Examine Reinforcement learning algorithms and Forecasting algorithms...

2. Analyze Data in Azure ML Studio

Did you know that you can use Azure Machine Learning to help you analyze data? In this 1-hour project-based course, you will learn how to display descriptive statistics of a dataset, measure relationships between variables and visualize relationships between variables. To achieve this, we will use one example diabetes data. We will calculate its descriptive statistics and correlations, train a machine learning model and calculate its feature importance to see how features affect the label and visualize categorical data, as well as relationships between variables, in Jupyter notebook. In order to be successful in this project, you will need knowledge of Python language and experience with machine learning in Python. Also, Azure subscription is required (free trial is an option for those who don’t have it), as well as Azure Machine Learning resource and a compute instance within. Instructional links will be provided to guide you through creation, if needed, in the first task. If you are ready to learn how to analyze data, this is a course for you! Let’s get started!...

3. Building Demand Forecasting with BigQuery ML

This is a self-paced lab that takes place in the Google Cloud console. In this lab you will build a time series model to forcast demand of multiple products using BigQuery ML. This lab is based on a blog post and featured in an episode of Cloud OnAir...

4. Graduate Admission Prediction with Pyspark ML

In this 1 hour long project-based course, you will learn to build a linear regression model using Pyspark ML to predict students' admission at the university. We will use the graduate admission 2 data set from Kaggle. Our goal is to use a Simple Linear Regression Machine Learning Algorithm from the Pyspark Machine learning library to predict the chances of getting admission. We will be carrying out the entire project on the Google Colab environment with the installation of Pyspark. You will need a free Gmail account to complete this project. Please be aware of the fact that the dataset and the model in this project, can not be used in the real-life. We are only using this data for the learning purposes. By the end of this project, you will be able to build the linear regression model using Pyspark ML to predict admission chances.You will also be able to setup and work with Pyspark on the Google Colab environment. Additionally, you will also be able to clean and prepare data for analysis. You should be familiar with the Python Programming language and you should have a theoretical understanding of Linear Regression algorithm. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions...

5. Deep Learning Inference with Azure ML Studio

In this project-based course, you will use the Multiclass Neural Network module in Azure Machine Learning Studio to train a neural network to recognize handwritten digits. Microsoft Azure Machine Learning Studio is a drag-and-drop tool you can use to rapidly build and deploy machine learning models on Azure. The data used in this course is the popular MNIST data set consisting of 70,000 grayscale images of hand-written digits. You are going to deploy the trained neural network model as an Azure Web service. Azure Web Services provide an interface between an application and a Machine Learning Studio workflow scoring model. You will write a Python application to use the Batch Execution Service and predict the class labels of handwritten digits. This is the third course in this series on building machine learning applications using Azure Machine Learning Studio. I highly encourage you to take the first course before proceeding. It has instructions on how to set up your Azure ML account with $200 worth of free credit to get started with running your experiments! This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions...

6. Analyze Datasets and Train ML Models using AutoML

In the first course of the Practical Data Science Specialization, you will learn foundational concepts for exploratory data analysis (EDA), automated machine learning (AutoML), and text classification algorithms. With Amazon SageMaker Clarify and Amazon SageMaker Data Wrangler, you will analyze a dataset for statistical bias, transform the dataset into machine-readable features, and select the most important features to train a multi-class text classifier. You will then perform automated machine learning (AutoML) to automatically train, tune, and deploy the best text-classification algorithm for the given dataset using Amazon SageMaker Autopilot. Next, you will work with Amazon SageMaker BlazingText, a highly optimized and scalable implementation of the popular FastText algorithm, to train a text classifier with very little code. Practical data science is geared towards handling massive datasets that do not fit in your local hardware and could originate from multiple sources. One of the biggest benefits of developing and running data science projects in the cloud is the agility and elasticity that the cloud offers to scale up and out at a minimum cost. The Practical Data Science Specialization helps you develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker. This Specialization is designed for data-focused developers, scientists, and analysts familiar with the Python and SQL programming languages and want to learn how to build, train, and deploy scalable, end-to-end ML pipelines - both automated and human-in-the-loop - in the AWS cloud...

7. Microsoft Azure Machine Learning (ML) Fundamentals

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Are you a company or a Web developer, IT administrator, data center architect, consultant, enterprise architect, data protection officer, programmer, data security specialist, or big data analyst and want to gain fundamental and intermediate level skills and enjoy a fascinating high paying career?Or maybe you just want to learn additional tips and techniques taking to a whole new level?Welcome to Microsoft Azure Machine Learning (ML) Fundamentals course For Beginners - A one of its kind course! The flipped classroom model with hand-on learning will help you experience direct  into the course as your begin your learning journey. Be sure to watch the preview lectures that set course expectations! In this course, you'll learn and practice: Working in Azure ML Studio  Creating a machine learning experimentModeling a real business use caseLearn the basic concepts of machine learningUnderstand  best practices, and much more....  You will also get complete resources, toolkit, and code where applicable with this course! We've built this course with our Team ClayDesk of industry recognized developers and consultants to bring you the best of everything! So, if you would like to:- start your freelancing career and consult companies, this course is for you- gain marketable skills as an IT expert and professional, this course is for you- This course is not designed for advanced level students... this Microsoft Azure Machine Learning (ML) Fundamentals course is exactly what you need, and more. (You'll even get a certification of completion)See what our students say "It is such a solid course that covers all important areas of machine learning, and I now know hoe to predict future products based on their features. Simply awesome!." - Alex Neuman"This is such an awesome course. I loved every bit of it - Wonderful learning experience!"  Ankit Goring. Join thousands of other students and share valuable experienceWhy take this course?As an enterprise architect consulting with global companies, technology evangelist, and brand innovator, I have designed, created, and implemented enterprise level projects, I am excited to share my knowledge and transfer skills to my students.  Enroll now in Microsoft Azure Machine Learning (ML) Fundamentals  today and revolutionize your learning. Stay at the cutting edge of Machine Learning and Data Science -and enjoy bigger, brighter opportunities with Microsoft Azure. Happy learning. Qasim Shah...

8. Digital Transformation Using AI/ML with Google Cloud

This series of courses begins by introducing fundamental Google Cloud concepts to lay the foundation for how businesses use data, machine learning (ML), and artificial intelligence (AI) to transform their business models.\n\nThe specialization is intended for anyone interested in how the use of AI and ML for the cloud, and especially for data, creates opportunities and requires change for businesses. No previous experience with ML, programming, or cloud technologies is required. The courses do not include any hands-on technical training...

9. Mastering MLOps: Complete course for ML Operations

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Are you interested in leveraging the power of Machine Learning (ML) to automate and optimize your business operations, but struggling with the complexity and challenges of deploying and managing ML models at scale? Look no further than this comprehensive MLOps course on Udemy. In this course, you'll learn how to apply DevOps and DataOps principles to the entire ML lifecycle, from designing and developing ML models to deploying and monitoring them in production. You'll gain hands-on experience with a wide range of MLOps tools and techniques, including Docker, Deepchecks, MLFlow, DVC, and DagsHub, and learn how to build scalable and reproducible ML pipelines. The course is divided into diferent sections, covering all aspects of the MLOps lifecycle in detail. What does the course include?MLOps fundamentals. We will learn about the Basic Concepts and Fundamentals of MLOps. We will look at traditional ML model management challenges and how MLOps addresses those problems to offer solutions. MLOps toolbox. We will learn how to apply MLOps tools to implement an end-to-end project. Model versioning with MLFlow. We will learn to version and register machine learning models with MLFlow. MLflow is an open source platform for managing the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. Data versioning with DVC. Data Version Control (DVC) lets you capture the versions of your data and models in Git commits, while storing them on-premises or in cloud storage. It also provides a mechanism to switch between these different data contents. Create a shared ML repository with DagsHub, DVC, Git and MLFlow. Use DagsHub, DVC, Git and MLFlow to version and registry your ML models. Auto-ML and Low-code MLOps. We will learn to automate the development of machine learning models with Auto-Ml and Low-code libraries such as Pycaret. Pycaret automates much of the MLOps cycle, including model versioning, training, evaluation, and deployment. Explainability, Auditability, and Interpretable machine learning. Learn about model interpretability, explainability, auditability, and data drift with SHAP and Evidently. Containerized Machine Learning WorkFlow With Docker. Docker is one of the most used tools to package the code and dependencies of our application and distribute it efficiently. We will learn how to use Docker to package our Machine Learning applications. Deploying ML in Production through APIS. We will learn about deploying models to production through API development with FastAPI and Flask. We will also learn to deploy those APIs in the Azure Cloud using Azure containers. Deploying ML in Production through web applications. We will learn to develop web applications with embedded machine learning models using Gradio. We will also learn how to develop an ML application with Flask and HTML, distribute it via a Docker container, and deploy it to production in Azure. BentoML for automated development of ML services. You will learn about BentoML, including introduction to BentoML, generating an ML service with BentoML, putting the service into production with BentoML and Docker, integrating BentoML and MLflow, and comparison of tools for developing ML services. MLOps in Azure Cloud. Finally, we will learn about the development and deployment of models in the Cloud, specifically in Azure. We will learn how to train models on Azure, put them into production, and then consume those models. Deploying ML services in Heroku. Including fundamentals of Heroku and a practical lab on deploying an ML service in Heroku. Continuous integration and delivery (CI/CD) with GitHub Actions and CML. You will learn about GitHub Actions and CML, including introduction to GitHub Actions, practical lab of GitHub Actions, Continuous Machine Learning (CML), and practical lab of applying GitHub Actions and CML to MLOps. Model Monitoring with Evidently AI. You will learn about model and service monitoring using Evidently AI and how to use it to monitor a model in production, identify data drift, and evaluate the model quality. Model Monitoring with Deepchecks. You will learn about the components of Deepchecks, including checks, conditions, and suites, and get hands-on experience using Data Integrity Suite, Train Test Validation Suite, Model Evaluation Suite, and Custom Performance Suite. Complete MLOps Project. You will work on a complete MLOps project from start to finish. This includes developing an ML model, validating code and pre-processing, versioning the project with MLFlow and DVC, sharing the repository with DagsHub and MLFlow, developing an API with BentoML, creating an app with Streamlit, and implementing a CI/CD workflow using GitHub Actions for data validation, application testing, and automated deployment to Heroku. Join today and get instant and lifetime access to:• MLOps Training Guide (PDF e-book)• Downloadable files, codes, and resources• Laboratories applied to use cases• Practical exercises and quizzes• Resources such as Cheatsheets • 1 to 1 expert support• Course question and answer forum• 30 days money back guaranteeWhether you're a data scientist, machine learning engineer, or DevOps professional, this course will equip you with the skills and knowledge you need to implement MLOps in your organization and take your ML projects to the next level. Sign up now and start your journey to becoming an MLOps expert!...

10. Data Science for Business Leaders: ML Fundamentals

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Machine learning is a capability that business leaders should grasp if they want to extract value from data.   There's a lot of hype; but there's some truth: the use of modern data science techniques could translate to a leap forward in progress or a significant competitive advantage.  Whether your are building or buying AI-powered solutions, you should consider how your organization could benefit from machine learning.  No coding or complex math. This is not a hands-on course. We set out to explain all of the fundamental concepts you'll need in plain English. This course is broken into 5 key parts: Part 1: Models, Machine Learning, Deep Learning, & Artificial Intelligence DefinedThis part has a simple mission: to give you a solid understanding of what Machine Learning is.  Mastering the concepts and the terminology is your first step to leveraging them as a capability.  We walk through basic examples to solidify understanding. Part 2: Identifying Use CasesTired of hearing about the same 5 uses for machine learning over and over?  Not sure if ML even applies to you?  Take some expert advice on how you can discover ML opportunities in *your* organization.  Part 3: Qualifying Use CasesOnce you've identified a use for ML, you'll need to measure and qualify that opportunity.  How do you analyze and quantify the advantage of an ML-driven solution?  You do not need to be a data scientist to benefit from this discussion on measurement.  Essential knowledge for business leaders who are responsible for optimizing a business process. Part 4: Building an ML CompetencyKey considerations and tips on building / buying ML and AI solutions. Part 5: Strategic Take-awaysA view on how ML changes the landscape over the long term; and discussion of things you can do *now* to ensure your organization is ready to take advantage of machine learning in the future...

11. Exam Prep MLS-C01: AWS Certified Specialty Machine Learning

Gain Skills to understand the fundamentals of Machine Learning. Learn working with various AWS Services necessary for Machine Learning. Hands on Experience working with AWS Management Console. Prepare for AWS Certified Machine Learning Specialty Certification...

12. The Pytorch basics you need to start your ML projects

In this 1-hour long project-based course, you will learn how to use simple commands to create and manipulate files and folders, perform multiple complex tasks using one simple command, use the superuser to perform high privilege operations...

13. Introduction to AI & ML techniques in Drug Discovery

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A perfect course for Bachelors / Masters / PhD students who are getting started into Drug Discovery research. This course is specially designed keeping in view of beginner level knowledge on Artificial Intelligence, Machine learning and computational drug discovery applications for science students. By the end of this course participants will be equipped with the basic knowledge required to navigate their drug discovery project making use of the Artificial Intelligence and Machine learning based tools...

14. AutoML Automated Machine Learning BootCamp (No Code ML)

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No code machine learning (ML) refers to the use of ML platforms, tools, or libraries that allow users to build and deploy ML models without writing any code. This approach is intended to make ML more accessible to a wider range of users, including those who may not have a strong programming background. Amazon SageMaker is a fully managed machine learning service provided by Amazon Web Services (AWS) that enables developers and data scientists to build, train, and deploy machine learning models at scale. SageMaker also includes built-in algorithms, pre-built libraries for common machine learning tasks, and a variety of tools for data pre-processing, model tuning, and model deployment. SageMaker also integrates with other AWS services to provide a complete machine learning environment. AutoML in SageMaker refers to the automatic selection and tuning of machine learning models to improve the accuracy and performance of the models. This can be done by using SageMaker's built-in algorithms and libraries or by using custom algorithms and libraries. SageMaker also includes a feature called Automatic Model Tuning which allows for tuning of the hyper-parameters of the models to improve their performance. SageMaker Studio Canvas is a feature that allows users to interact with their data, build and visualize workflows, and create, run, and debug Jupyter notebooks, all within the same web-based interface. The Canvas provides a visual and interactive way to explore, manipulate and visualize data, and allows users to create Jupyter notebooks and drag-and-drop pre-built code snippets, called recipes to quickly perform common data pre-processing, data visualization, and data analysis tasks. SageMaker Studio Canvas also allows users to easily share their notebooks, recipes, and data with other users and collaborate on projects. This helps to simplify the machine learning development process, accelerate the development of machine learning models, and improve collaboration among teams. IN THIS COURSE YOU WILL LEARN : LifeCycle of a Machine Learning ProjectMachine Learning FundamentalsCloud Computing for Machine LearningAWS SageMaker Canvas (NO CODE ML)...

15. Unsupervised Machine Learning: With 2 Capstone ML Projects

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Crazy about Unsupervised Machine Learning?This course is a perfect fit for you. This course will take you step by step into the world of Unsupervised Machine Learning. Unsupervised machine learning, uses machine learning algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention. Its ability to discover similarities and differences in information make it the ideal solution for exploratory data analysis, cross-selling strategies, customer segmentation, and image recognition. This course will give you theoretical as well as practical knowledge of Unsupervised Machine Learning. This Unsupervised Machine Learning course is fun as well as exciting. It will cover all common and important algorithms and will give you the experience of working on some real-world projects. This course will cover the following topics:-K Means ClusteringHierarchical ClusteringDBSCAN ClusteringEvaluation Metrics for Clustering AnalysisTechniques used for Treating DimensionalityDifferent algorithms for clusteringDifferent methods to deal with imbalanced data. Correlation filteringVariance filteringPCA & LDAt-SNE for Dimensionality ReductionWe have covered each and every topic in detail and also learned to apply them to real-world problems. You will have lifetime access to the resources and we update the course regularly to ensure that its up to date. I assure you, there onwards, this course can be your go-to reference to answer all questions about these algorithms. There are lots and lots of exercises for you to practice and also 2 bonus Unsupervised Machine Learning Project Optimizing Crop Production and Customer Segmentation Engine. In this Optimizing Crop Production project, you will learn about Precision Farming using Data Science Technologies such as Clustering Analysis and Classification Analysis. You will be able to Recommend the best Crops to Farmers to Increase their Productivity. In this Customer Segmentation Engine project, you will divide the customer base into several groups of individuals that share a similarity in different ways that are relevant to marketing such as gender, age, interests, and miscellaneous spending habits. You will make use of all the topics read in this course. You will also have access to all the resources used in this course. Make This Investment in YourselfIf you want to ride the machine learning wave and enjoy the salaries that data scientists make, then this is the course for you! Instructor Support - Quick Instructor Support for any queries. Enroll now and become a master in Unsupervised machine learning...

16. Full Stack Web Developer with AI & ML IntegrationHindiUrdu

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Welcome to the Python and Django Full Stack Web Developer Bootcamp with Machine Learning and Artificial Intelligence Integration. Worlds Only Top Level Course in Hindi and Urdu Language This Course is going to be Your One Stop Shop to be a Top Notch Web Developer using and Developing  Machine Learning Models. Question is why would you be taking this Course. First and foremost, there is no course like this in the market period. There are many Full Stack Courses  but they seem to shy away from Machine Learning and Artificial Intelligence Integration just because its a different level altogether and lets be honest, not many people know this skill. So its a very rare skill set and rewards are huge. Here are the skill set you are going to get at the end of this course. PythonDjangoHTMLCSS3JavaScriptHow to Built Startup Landing PageBootstrap Git and Version ControlDocument Object ModelFundamentals of Data ScienceMachine Learning Integration with Most Powerful Web Frame Work DjangoDeep Learning Neural NetsConvolution Neural Networks(CNN)Use AWS tools to host your websitesweb server tools  Nginx and GunicornHere is what other students Like you say about this course. Torikus Just awesome!!!! Rishab AroraAwesome is the right word. Abhishek singh rajputAmazing... Biswanandan PattanaykOne of best courseMohd Adnan Chaudharyowesome sir you should take moneyKrishnendu DuttaAmazing is an understatement. Thank you Mohsin Sir. Love from India. Nayak Kumbla Praveen This is one of the best course covering entire python starting from standalone , web development and machine learningUsman KhanM choosing python based development career just because of this course. simply amazingHafsa SheikhAmazing..! You made all the concepts clear and I hope I will be senior full stack developer after this course. Sir mein ne pora semester AI prhi us ka project bnaya , problems solve kin, mgr mujhe sach mein kuch smjh nhi ayiwi thi ap k course k baad mujhe boht zaida sekhne ko mila. ThanksThis course comes with Udemy's 30 days Money Back guarantee, This is the deal you can never go wrongI have 100% response rate , so I will always be there to respond to your questions. And of course after Completing this course not only you will built your portfolio but also get Certificate of completion which you can post on your linked Profile and Attract potential Employers. Enroll now and lets get started.....

17. Telecom Customer Churn Prediction in Apache Spark (ML)

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Apache Spark Started as a research project at the University of California in 2009, Apache Spark is currently one of the most widely used analytics engines. No wonder: it can process data on an enormous scale, supports multiple coding languages (you can use Java, Scala, Python, R, and SQL) and runs on its own or in the cloud, as well as on other systems (e. g., Hadoop or Kubernetes). In this Apache Spark tutorial, I will introduce you to one of the most notable use cases of Apache Spark: machine learning. In less than two hours, we will go through every step of a machine learning project that will provide us with an accurate telecom customer churn prediction in the end. This is going to be a fully hands-on experience, so roll up your sleeves and prepare to give it your best! First and foremost, how does Apache Spark machine learning work?Before you learn Apache Spark, you need to know it comes with a few inbuilt libraries. One of them is called MLlib. To put it simply, it allows the Spark Core to perform machine learning tasks - and (as you will see in this Apache Spark tutorial) does it in breathtaking speed. Due to its ability to handle significant amounts of data, Apache Spark is perfect for tasks related to machine learning, as it can ensure more accurate results when training algorithms. Mastering Apache Spark machine learning can also be a skill highly sought after by employers and headhunters: more and more companies get interested in applying machine learning solutions for business analytics, security, or customer service. Hence, this practical Apache Spark tutorial can become your first step towards a lucrative career! Learn Apache Spark by creating a project from A to Z yourself! I am a firm believer that the best way to learn is by doing. That's why I haven't included any purely theoretical lectures in this Apache Spark tutorial: you will learn everything on the way and be able to put it into practice straight away. Seeing the way each feature works will help you learn Apache Spark machine learning thoroughly by heart. I will also be providing some materials in ZIP archives. Make sure to download them at the beginning of the course, as you will not be able to continue with the project without it. And that's not all you're getting from this course - can you believe it?Apart from Spark itself, I will also introduce you to Databricks - a platform that simplifies handling and organizing data for Spark. It's been founded by the same team that initially started Spark, too. In this course, I will explain how to create an account on Databricks and use its Notebook feature for writing and organizing your code. After you finish my Apache Spark tutorial, you will have a fully functioning telecom customer churn prediction project. Take the course now, and have a much stronger grasp of machine learning and data analytics in just a few hours! Spark Machine Learning Project (Telecom Customer Churn Prediction) for beginners using Databricks Notebook (Unofficial) (Community edition Server) In this Data Science Machine Learning project, we will create Telecom Customer Churn Prediction Project using Classification Model Logistic Regression, Naive Bayes and One-vs-Rest classifier few of the predictive models. Explore Apache Spark and Machine Learning on the Databricks platform. Launching Spark ClusterCreate a Data PipelineProcess that data using a Machine Learning model (Spark ML Library)Hands-on learningReal time Use Case Publish the Project on Web to Impress your recruiter Graphical  Representation of Data using Databricks notebook. Transform structured data using SparkSQL and DataFramesTelecom Customer Churn Prediction a Real time Use Case on Apache SparkAbout Databricks: Databricks lets you start writing Spark ML code instantly so you can focus on your data problems...

18. Android & Firebase ML Kit in Java / Kotlin

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RequirementsYou should have some basic knowledge of Android App Development using Java or KotlinFirebase ML Kit for Android Developer'sMake your Android Applications smart, use ML trained model or train your own ML models explore the power of AI and Machine Learning. This course was recorded using Android Studio 3.6.1 (which is a great introduction to the development environment!) For a smooth experience I'd recommend you use the same, but students can still use the latest Android Studio version available if they prefer! Wish you'd thought of Object Recognition/Face Detection/Text Recognition?Me too. But until I work out how to build a time machine. Here's the next best thing. Firebase ML Kit for Android Developer'sCurriculum: In this course, we will explore the features of Firebase ML Kit for Android. We will start by learning about Firebase ML Kit and Features it provides. Then we will see how to integrate ML Kit inside your Android Application just using Android studio. After that, we will explore the features of ML Kit and develop Android Applications likeText Recognition Android ApplicationAndroid Application to Translate between LanguagesLanguage Detection ApplicationFace Detection ApplicationBarcode Scanner Android ApplicationObject Detection Android AppLandmark Recognition ApplicationStones Recognition ApplicationThen we will learn about Auto ML Vision edge feature of Firebase ML Kit using which we can train the Machine Learning model on our own dataset and build Android Application for that model. We will train model to recognize different types of stones and build an Android App for that model. At the end of this course, we will combine different features of Firebase ML kit to build an Android Application to categorize images of mobile gallery. Why choose me?My name's Hamza Asif, Udemy's coding instructor. It's not my first course on mobile Machine Leaning. I have a course named Complete Tensorflow Lite course for Android App Development on udemy. So which course you should take? It's recommended taking Machine Learning for Android Developer using Tensorflow lite first so that you can understand the working of Machine Learning. If you want to learn a practical implementation and use of Machine Learning in Android using Firebase ML Kit........................................................................................................................................................................................................................................................................................................................................................... then that course is for you. This is my 2nd course on Android Machine Learning and I am the only udemy instructor with more than one course on that topic. My goal is to promote the use of Machine Learning in Android and I am excited to share my knowledge with you. Android Version we will use?Android Pie, Android QAll the Android Application we will develop in this course we will use Android Pie and Q to test them. So we are/So join my Firebase ML Kit for Android Developer's course today and here's what you'll getLearn practical implementation of Text Recognition, Language Identification, Face and expression detection, Barcode scanning, Landmark Recognition, Text Translation, and Object detection and recognition inside Android App Development using Android Studio and ML kit. Learn how to use Auto ML to train the model on your own dataset and use those models in Android ApplicationLearn about both on-device and Cloud Machine LearningWhy take this course?Machine Learning use is at its peak so is the mobile tech but people having skills to implement both are rare. This course will enable you to empower your Android Applications with the practical implementation of Machine Learning, Computer Vision, and AI. Having a little knowledge of Android App Development, this course will differentiate you from other developers because you will have something that is currently in demand. This course will make provide you a smooth path to become a pro in using Machine Learning in your Applications. This course will not just enable you to apply machine learning in limited scenarios but It will enable you toPrepare or download your own datasetTrain machine learning modelDevelop Android ApplicationSo if you have very basic knowledge of Android App Development and want to apply Machine Learning in Android Applications without knowing background knowledge of  Machine Learning this course is or you. Is this course for you?This is a one-size-fits-all course for beginners to experts. So, this course is for you if you are: A total beginner, with a curious mind and a drive to make and create awesome stuff using Android App development and MLA fledgling developer, want to add Machine Learning implementation in his skillsetA pro app developer-heavyweight, with an itch to build your dream app An entrepreneur with big ideasBenefits to youRisk-free! 30-day money-back guaranteeFreedom to work from anywhere (beach, coffee shop, airport - anywhere with Wi-Fi)Potential to work with forward-thinking companies (from cool start-ups to pioneering tech firms)Rocket-fuelled job opportunities and powered-up career prospectsA sense of accomplishment as you build amazing thingsMake any Android app you like (your imagination is your only limit)Submit your apps to Google Play and potentially start selling within hoursUse ML Kit just using Android StudioThanks for getting this far. I appreciate your time! I also hope you're as excited to get started as I am to share the latest use of ML in Android development with you. All that remains to be said, is this…Don't wait another moment. The world is moving fast. And I know you've got ideas worth sharing. Coding really can help you achieve your dreams. So click the button to sign up today - completely risk-free. And join me on this trailblazing adventure, today. Who this course is for: Anyone who wants to learn the practical implementation of Machine Learning and Computer Vision in their Android Applications. Anyone who wants to make their Android App Development smart. Anyone who wants to train and deploy Machine Learning models on his own data without background knowledge of Machine Learning...

19. Deploy Face Recognition Web App with ML in Django

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Welcome to the Course Deploy Face Recognition Web App, Machine Learning, Django & Database in Heroku Cloud!!!. An Artificial Intelligence Project. Computer Vision & Face recognition is one of the most widely used in the area of Artificial Intelligence and Data Science. If at all you want to develop an end-to-end application in Data Science, then you need to be a master in Machine Learning / Deep Learning, and in addition to that, you need to have knowledge in Web Development. This course is one stop course where you will learn End to End development of a Computer-Vision Based Artificial Intelligence Project from SCRATCH. As this course is a full-stack course we designed this course into 4 phasesPhase-1: Machine Learning - Face Identify RecognitionPhase-2: Machine Learning - Facial Emotion RecognitionPhase-3: Django Web App DevelopmentPhase-4: Deployment / ProductionOverview: I will start the course by installing Python and installing the necessary libraries in Python for developing the end-to-end project. Then I will teach you one of the prerequisites of the course that is image processing techniques in OpenCV and the mathematical concepts behind the images. We will also do the necessary image analysis and required preprocessing steps for the images. Then we will do a mini project on Face Detection using OpenCV and Deep Neural Networks. With the concepts of image basics, we will then start our project phase-1, face identity recognition. I will start this phase with preprocessing images, we will extract features from the images using deep neural networks. Then with the features of faces, we will train the different Machine learning models like logistic regression, support vector machines, random forest. Then we combine all machine learning models with Voting Classifier (stacking method). I will teach you the model selection and hyperparameter tuning for face recognition modelsIn Phase-2, we will apply the machine learning techniques used in face identity recognition for facial emotion recognition. After that, we will combine all different detection and recognition models into a pipeline. Once our machine learning model is ready, will we move to Phase-3, and develop a Web Application in Django by rendering HTML CSS and bootstrap in the frontend and in the backend written in Python. Here I will teach you the necessary prerequisite of Django. Then we will develop a web app using the MVT (Models, Views, and Templates) framework. We will start developing Django App by designing a database in SQLite. Here I will also teach you to interphase machine learning pipeline models to the MVT framework. In the end, we will style our app using Bootstrap. Finally, we will deploy the entire Django Web App in Heroku Cloud for production and get a URL/domain where you can access it anywhere in the world. I will also teach all the necessary installation required and explain how to solve errors whenever you have encountered them while deploying your web app. If you want to become an AI developer this is the perfect course to starts with. Below given is the high-level abstract of the course and the learning objectives. What you will learn?Prerequisite of Project: OpenCVImage Processing with OpenCVFace Detection with Viola-Jones and Deep Neural Networks (SSD)Feature Extraction with OpenCV and Deep Learning NetworksProject Phase - 1: Face Recognition and Person IdentityGather ImagesExtract Faces only from ImagesLabeling (Target output) ImagesData PreprocessingTraining Face Recognition with OWN Machine Learning Models. Combine All Machine Learning Models using Ensemble Technique with Voting ClassifierTuning Machine Learning ModelModel EvaluationProject Phase - 2: Train Facial Emotion RecognitionGather Emotion ImagesData PreprocessingTrain Machine Learning ModelsTuning Machine Learning ModelsModel EvaluationProject Phase -3: Django Web App Developed in Local (Computer)Setting Up Visual Studio CodeInstall all Dependencies of VS CodeSetting Virtual EnvironmentFreeze RequirementsLearn Django BasicsSETTINGSURLSVIEWSTEMPLATES (HTML)Face Recognition Django ProjectModels Views Templates (MVT)Design SQLite Database in DjangoStore Uploaded Image in DatabaseIntegrate Machine Learning to DjangoMVT + Machine Learning FrameworkStyling Django Web App with BootstrapProject Phase -4: Deploy Web App in Heroku Cloud for ProductionSetting up Heroku Account. Creating App in HerokuInstall Heroku CLI, GITDeploy Heroku in CloudNecessary Installation to Fix CSS in Heroku. What are you waiting for? Start the course develop your own Computer Vision Django Web Project using Machine Learning, Python and Deploy it in Cloud with your own hands. I will see you inside the course...