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Machine learning isn’t just for self-driving cars and game-playing robots—it’s also helping identify whether someone’s wearing a mask or not. This course takes you through the full project lifecycle of building a Covid Mask Detector, using one of the most relevant applications of computer vision in recent years. Whether you're a data enthusiast or a coding hobbyist, you’ll enjoy diving into this machine learning challenge with a purpose that’s easy to relate to and timely. With clear, structured guidance, you'll explore how to prepare image data, train a neural network, and apply detection techniques—all from the comfort of your own screen. The content is delivered with clarity and a dash of wit, making the learning journey not just informative, but surprisingly enjoyable. You’ll walk away with confidence in building a full machine learning project, specifically tailored for image classification, and yes—taught in plain, human English (no jargon jungle here). Whether you're brushing up your Python skills or simply curious how AI spots face masks, this course offers an insightful experience in smart automation, delivered with a professional tone and just enough character to keep you grinning as you code. Learning Outcomes: Develop a Covid mask detector using machine learning. Master OpenCV, a popular computer vision library. Build models with TensorFlow. Design and build the app, upload files, and deploy it on AWS. Gain valuable experience in machine learning app development. The Hands-on Machine Learning Project - Covid Mask Detector course is designed to provide you with the skills and knowledge needed to develop a mask detector using deep learning. In this course, you'll learn how to master OpenCV, an open-source computer vision library used for image processing and face detection. You'll also learn how to build and train a deep learning model using Tensorflow, a popular machine learning framework. The course is perfect for aspiring data scientists, machine learning engineers, and developers who want to make a positive impact on society by contributing to public health and safety efforts. By the end of this course, you'll have a deep understanding of how to develop a mask detector app that can be used to detect whether individuals are wearing masks in public spaces. You'll be able to master OpenCV and use it to preprocess and detect faces in images. You'll also learn how to build and train a deep learning model using TensorFlow and how to deploy your mask detector app on AWS. This course provides a unique opportunity for individuals to gain real-world experience in developing cutting-edge technology that can make a positive impact on society. Hands on Machine Learning Project - Covid Mask Detector Course Curriculum Section 01: Introduction Introduction to Course Section 02: Mastering OpenCV Getting System Ready Read and Write Images Resize and Crop Working with Shapes Working with Text Section 03: Pre-Requisite for Face Detection Pre-Requisite for Face Detection Detect the Face Section 04: Deep Learning with Tensorflow Introduction to Deep Learning with Tensorflow Model Building Training the Mask Detector Saving the Best Model Basic Front End Design of App File Upload Interface for App App Prep App Build and Testing AWS Deployment AWS Deployment Continued How is the course assessed? Upon completing an online module, you will immediately be given access to a specifically crafted MCQ test. For each test, the pass mark will be set to 60%. Exam & Retakes: It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable. Certification Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of __ GBP. £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery). CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Aspiring data scientists. Machine learning engineers. Developers interested in machine learning app development. Anyone interested in developing technology to fight the pandemic. Professionals looking to upskill in the latest technology. Career path Data Scientist: £40,000 to £80,000 per year. Machine Learning Engineer: £55,000 to £90,000 per year. Artificial Intelligence Developer: £40,000 to £80,000 per year. Computer Vision Engineer: £40,000 to £80,000 per year. Deep Learning Engineer: £55,000 to £90,000 per year. Certificates Certificate of completion Digital certificate - £9 You can apply for a CPD Accredited PDF Certificate at the cost of £9.

Get ready for an exceptional online learning experience with the Python, Data Science, Machine Learning, Data Mining & Cyber Security bundle! This carefully curated collection of 20 premium courses is designed to cater to a variety of interests and disciplines. Dive into a sea of knowledge and skills, tailoring your learning journey to suit your unique aspirations. This Python, Data Science, Machine Learning, Data Mining & Cyber Security is a dynamic package, blending the expertise of industry professionals with the flexibility of digital learning. It offers the perfect balance of foundational understanding and advanced insights. Whether you're looking to break into a new field or deepen your existing knowledge, the Python & Data Science package has something for everyone. As part of the Python, Data Science, Machine Learning, Data Mining & Cyber Security package, you will receive complimentary PDF certificates for all courses in Python & Data Science bundle at no extra cost. Equip yourself with the Python & Data Science bundle to confidently navigate your career path or personal development journey. Enrol our Python & Data Science bundletoday and start your career growth! ThisBundle Comprises the Following CPD Accredited Courses: Python Programming: Beginner To Expert Data Science & Machine Learning with Python Coding with Python 3 Introduction to Coding With HTML, CSS, & Javascript Python for Spatial Analysis in ArcGIS Python Programming Bible | Networking, GUI, Email, XML, CGI Business Intelligence and Data Mining SQL for Data Science, Data Analytics and Data Visualization Python Data Science with Numpy, Pandas and Matplotlib Cloud Computing / CompTIA Cloud+ (CV0-002) Cyber Security Awareness Training Learn Ethical Hacking From A-Z: Beginner To Expert Easy to Advanced Data Structures R Programming for Data Science GDPR UK Training Career Development Plan Fundamentals CV Writing and Job Searching Learn to Level Up Your Leadership Networking Skills for Personal Success Ace Your Presentations: Public Speaking Masterclass Learning Outcome: By completing the course, you will: Gain comprehensive insights into multiple fields. Foster critical thinking and problem-solving skills across various disciplines. Understand industry trends and best practices through the Python & Data Science Bundle. Develop practical skills applicable to real-world situations. Enhance personal and professional growth with the Python & Data Science Bundle. Build a strong knowledge base in your chosen course via the Python & Data Science Bundle. Benefit from the flexibility and convenience of online learning. With the Python & Data Science package, validate your learning with a CPD certificate. Each course in this bundle holds a prestigious CPD accreditation, symbolising exceptional quality. The materials, brimming with knowledge, are regularly updated, ensuring their relevance. This bundle promises not just education but an evolving learning experience. Engage with this extraordinary collection, and prepare to enrich your personal and professional development. Embrace the future of learning with the Python, Data Science, Machine Learning, Data Mining & Cyber Security , a rich anthology of 15 diverse courses. Each course in the Python & Data Science bundle is handpicked by our experts to ensure a wide spectrum of learning opportunities. This Python, Data Science, Machine Learning, Data Mining & Cyber Security bundle will take you on a unique and enriching educational journey. The bundle encapsulates our mission to provide quality, accessible education for all. Whether you are just starting your career, looking to switch industries, or hoping to enhance your professional skill set, the Python, Data Science, Machine Learning, Data Mining & Cyber Security bundle offers you the flexibility and convenience to learn at your own pace. Make the Python & Data Science package your trusted companion in your lifelong learning journey. CPD 200 CPD hours / points Accredited by CPD Quality Standards Who is this course for? The Python, Data Science, Machine Learning, Data Mining & Cyber Security bundle is perfect for: Lifelong learners looking to expand their knowledge and skills. Professionals seeking to enhance their career with CPD certification. Individuals wanting to explore new fields and disciplines. Anyone who values flexible, self-paced learning from the comfort of home. Requirements Without any formal requirements, you can delightfully enrol this Python, Data Science, Machine Learning, Data Mining & Cyber Security course. Career path Unleash your potential with the Python, Data Science, Machine Learning, Data Mining & Cyber Security bundle. Acquire versatile skills across multiple fields, foster problem-solving abilities, and stay ahead of industry trends. Ideal for those seeking career advancement, a new professional path, or personal growth. Embrace the journey with the Python & Data Science bundle package. Certificates Certificate Of Completion Hard copy certificate - Included You will get a complimentary Hard Copy Certificate. Certificate Of Completion Digital certificate - Included

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Duration 1 Days 6 CPD hours This course is intended for The Azure AI Fundamentals course is designed for anyone interested in learning about the types of solution artificial intelligence (AI) makes possible, and the services on Microsoft Azure that you can use to create them. You don?t need to have any experience of using Microsoft Azure before taking this course, but a basic level of familiarity with computer technology and the Internet is assumed. Some of the concepts covered in the course require a basic understanding of mathematics, such as the ability to interpret charts. The course includes hands-on activities that involve working with data and running code, so a knowledge of fundamental programming principles will be helpful. This course introduces fundamentals concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions. The course is not designed to teach students to become professional data scientists or software developers, but rather to build awareness of common AI workloads and the ability to identify Azure services to support them. Prerequisites Prerequisite certification is not required before taking this course. Successful Azure AI Fundamental students start with some basic awareness of computing and internet concepts, and an interest in using Azure AI services. Specifically: Experience using computers and the internet. Interest in use cases for AI applications and machine learning models. A willingness to learn through hands-on exp... 1 - Fundamental AI Concepts Understand machine learning Understand computer vision Understand natural language processing Understand document intelligence and knowledge mining Understand generative AI Challenges and risks with AI Understand Responsible AI 2 - Fundamentals of machine learning What is machine learning? Types of machine learning Regression Binary classification Multiclass classification Clustering Deep learning Azure Machine Learning 3 - Fundamentals of Azure AI services AI services on the Azure platform Create Azure AI service resources Use Azure AI services Understand authentication for Azure AI services 4 - Fundamentals of Computer Vision Images and image processing Machine learning for computer vision Azure AI Vision 5 - Fundamentals of Facial Recognition Understand Face analysis Get started with Face analysis on Azure 6 - Fundamentals of optical character recognition Get started with Vision Studio on Azure 7 - Fundamentals of Text Analysis with the Language Service Understand Text Analytics Get started with text analysis 8 - Fundamentals of question answering with the Language Service Understand question answering Get started with the Language service and Azure Bot Service 9 - Fundamentals of conversational language understanding Describe conversational language understanding Get started with conversational language understanding in Azure 10 - Fundamentals of Azure AI Speech Understand speech recognition and synthesis Get started with speech on Azure 11 - Fundamentals of Azure AI Document Intelligence Explore capabilities of document intelligence Get started with receipt analysis on Azure 12 - Fundamentals of Knowledge Mining with Azure Cognitive Search What is Azure Cognitive Search? Identify elements of a search solution Use a skillset to define an enrichment pipeline Understand indexes Use an indexer to build an index Persist enriched data in a knowledge store Create an index in the Azure portal Query data in an Azure Cognitive Search index 13 - Fundamentals of Generative AI What is generative AI? Large language models What is Azure OpenAI? What are copilots? Improve generative AI responses with prompt engineering 14 - Fundamentals of Azure OpenAI Service What is generative AI Describe Azure OpenAI How to use Azure OpenAI Understand OpenAI's natural language capabilities Understand OpenAI code generation capabilities Understand OpenAI's image generation capabilities Describe Azure OpenAI's access and responsible AI policies 15 - Fundamentals of Responsible Generative AI Plan a responsible generative AI solution Identify potential harms Measure potential harms Mitigate potential harms Operate a responsible generative AI solution Additional course details: Nexus Humans AI-900T00 - Microsoft Azure AI Fundamentals training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the AI-900T00 - Microsoft Azure AI Fundamentals course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Register on the Machine Learning in Flutter today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career. The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials. Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get a digital certificate as a proof of your course completion. The Machine Learning in Flutter course is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablet, and smartphones. The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly! What You Get With The Machine Learning in Flutter Course Receive a e-certificate upon successful completion of the course Get taught by experienced, professional instructors Study at a time and pace that suits your learning style 24/7 help and advice via email or live chat Get full tutor support on weekdays (Monday to Friday) Course Design The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace. You are taught through a combination of Video lessons Online study materials Certification After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for £9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for £15.99, which will reach your doorsteps by post. Who Is This Course For: The course is ideal for those who already work in this sector or are an aspiring professional. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge. Requirements: The online training is open to all students and has no formal entry requirements. To study the Machine Learning in Flutter course, all your need is a passion for learning, a good understanding of English, numeracy, and IT skills. You must also be over the age of 16. Course Content Unit 01: Introduction Module 01: Course Curriculum 00:02:00 Unit 02: Image Picker and Camera Libraries Module 01: Image Picker Library for Flutter App Development 00:13:00 Module 02: Flutter Image Picker Application Testing 00:01:00 Module 03: Camera Package Setup for Flutter 00:04:00 Module 04: Flutter Camera Package Code 00:08:00 Unit 03: Firebase ML Kit Module 01: Firebase ML kit section Introduction 00:01:00 Module 02: Firebase ML Kit introduction 00:02:00 Unit 04: Image Labeling using ML Kit Module 01: Flutter Image Labeling Section Introduction 00:02:00 Module 02: Importing Starter code for image labeling 00:03:00 Module 03: Image labeling starter code explanation 00:06:00 Module 04: Creating firebase project for image labeling 00:06:00 Module 05: Adding Firebase ML Vision library in Flutter Application 00:10:00 Module 06: Testing Firebase Image labeling application 00:01:00 Module 07: Importing Image Labeling live feed application starter code 00:03:00 Module 08: Flutter Camera Package Code 00:06:00 Module 09: Flutter Image Labeling live feed application code 00:08:00 Module 10: Flutter Image labeling live feed application testing 00:01:00 Unit 05: Section Barcode Scanning Module 01: Flutter Barcode Scanning Section Introduction 00:02:00 Module 02: Importing Starter code for Flutter Barcode Scanning 00:03:00 Module 03: Flutter Barcode Scanning code 00:11:00 Module 04: Flutter Barcode Scanning Application Testing 00:01:00 Module 05: Flutter Barcode Scanning Live Feed Application code 00:08:00 Module 06: Flutter Barcode Scanning Live feed Application Testing 00:01:00 Unit 06: Section Text Recognition Module 01: Flutter Text Recognition Section Introduction 00:01:00 Module 02: Importing Starter code for Flutter Text Recognition 00:03:00 Module 03: Writing Flutter Text Recognition Code 00:09:00 Module 04: Testing Flutter Text Recognition Application 00:01:00 Unit 07: Section Face Detection Module 01: Flutter Face Detection Section Introduction 00:02:00 Module 02: Flutter Face Detection Application Flow 00:01:00 Module 03: Flutter Face Detection code 00:06:00 Module 04: Flutter drawing rectangles around detected faces 00:05:00 Unit 08: Pretrained Tensorflow lite models Module 01: Pretrained Tensorflow lite models Section Introduction 00:02:00 Unit 09: Section Image Classification Module 01: Flutter Image classification Section introduction 00:02:00 Module 02: Importing Starter code for Flutter Image classification application 00:03:00 Module 03: Starter code explanation for Flutter Image classification 00:06:00 Module 04: Writing flutter image classification code 00:13:00 Module 05: Testing flutter image classification application 00:02:00 Module 06: Importing Flutter live feed Image classification application starter code 00:03:00 Module 07: Starter code explanation of Flutter Live feed Image classification application 00:05:00 Module 08: Writing Flutter Image classification code 00:11:00 Module 09: Testing live feed image classification flutter application 00:01:00 Unit 10: Section object detection Module 01: Flutter Object detection section introduction 00:02:00 Module 02: Importing Application code object detection flutter 00:05:00 Module 03: Flutter Object detection code 00:13:00 Module 04: Flutter Drawing Rectangles around detected objects 00:04:00 Module 05: Importing the code for live feed object detection flutter application 00:02:00 Module 06: Testing object detection live feed flutter application 00:01:00 Module 07: Flutter Live feed object detection application code 00:10:00 Unit 11: Section human pose estimation Module 01: Flutter Pose estimation section introduction 00:02:00 Module 02: Importing Flutter Pose estimation Application code 00:04:00 Module 03: Flutter Pose estimation code 00:10:00 Module 04: Importing pose estimation live feed flutter application code 00:02:00 Module 05: Flutter Live feed pose estimation application demo 00:09:00 Module 06: Using PoseNet model for Flutter Live feed pose estimation application 00:08:00 Unit 12: Image segmentation section Module 01: Flutter Image Segmentation Section Introduction 00:02:00 Module 02: Importing Flutter Image Segmentation Application code 00:03:00 Module 03: Flutter using DeepLab model for image segmentation 00:09:00 Unit 13: Section Training Image Classification Models Module 01: Section Introduction 00:02:00 Module 02: Machine Learning and Image classification 00:02:00 Unit 14: Dog Breed Classification Module 01: Flutter getting the dataset for model training 00:05:00 Module 02: Flutter Training the model 00:06:00 Module 03: Flutter Dog Breed Classification Application 00:18:00 Module 04: Flutter Live feed dog breed classification application 00:03:00 Module 05: Testing live feed dog breed classification application 00:01:00 Unit 15: Fruits Recognition using Transfer Learning Module 01: Transfer learning introduction 00:02:00 Module 02: Flutter getting the dataset for model training 00:05:00 Module 03: Flutter Training fruit recognition model 00:09:00 Module 04: Flutter Testing Live feed fruits recognition application 00:01:00 Frequently Asked Questions Are there any prerequisites for taking the course? There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course. Can I access the course at any time, or is there a set schedule? You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience. How long will I have access to the course? For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime. Is there a certificate of completion provided after completing the course? Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks. Can I switch courses or get a refund if I'm not satisfied with the course? We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase. How do I track my progress in the course? Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course. What if I have technical issues or difficulties with the course? If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.

You will learn Python-based deep learning and machine learning techniques through this course. With numerous real-world case studies, we will go over all the mathematics needed to master deep learning algorithms. We will study Backpropagation, Feed Forward Network, Artificial Neural Networks, CNN, RNN, Transfer Learning, and more.

If you are working on data science projects and want to create powerful visualization and insights as an outcome of your projects or are working on machine learning projects and want to find patterns and insights from your data on your way to building models, then this course is for you. This course exclusively focuses on explaining how to build fantastic visualizations using Python. It covers more than 20 types of visualizations using the most popular Python visualization libraries, such as Matplotlib, Seaborn, and Bokeh along with data analytics that leads to building these visualizations so that the learners understand the flow of analysis to insights.

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

In this self-paced course, you will learn how to use TensorFlow 2 to build recurrent neural networks (RNNs). You will learn about sequence data, forecasting, Elman Unit, GRU, and LSTM. You will also learn how to work with image classification and how to get stock return predictions using LSTMs. We will also cover Natural Language Processing (NLP) and learn about text preprocessing and classification.

Dive into the fascinating world of deep learning with this expertly crafted course designed to unravel the mysteries of neural networks using R. This course guides you through the core principles of neural networks, illustrating how layers of algorithms mimic the human brain’s ability to identify patterns and make decisions. Whether you’re a data enthusiast or a professional seeking to enhance your analytical toolkit, this course offers a clear and engaging path to understanding deep learning concepts through the power of R programming. With a sharp focus on theory and application, you will explore how to build, train, and optimise neural networks effectively, while leveraging R’s rich ecosystem of libraries and tools. The course content is designed to maintain a perfect balance between depth and clarity, making complex topics accessible without oversimplification. By the end, you will be equipped with a strong conceptual foundation and the confidence to approach deep learning projects with R, all through an engaging online format that fits seamlessly into your schedule. Learning Outcomes: Understanding of single-layer and multi-layer neural networks Knowledge of R programming for neural network applications Implementation of neural networks in real-world projects Familiarity with agriculture and war datasets for neural network modelling Ability to evaluate neural network model accuracy and performance The Deep Learning Neural Network with R course is designed to provide learners with a comprehensive understanding of how to build and evaluate neural networks using R programming language. The course includes four modules that cover single-layer and multi-layer neural networks applied to agriculture and war datasets. Each module contains practical hands-on projects that allow learners to gain real-world experience in neural network development and evaluation. By the end of the course, learners will have a solid understanding of neural network concepts, R programming language, and practical experience with real-world datasets. Deep Learning Neural Network with R Course Curriculum Section 01: Single Layer Neural Networks Project - Agriculture (Part - 1) Section 02: Single Layer Neural Networks Project - Agriculture (Part - 2) Section 03: Multi-Layer Neural Networks Project - Deaths in wars (Part - 1) Section 04: Multi-Layer Neural Networks Project - Deaths in wars (Part - 2) How is the course assessed? Upon completing an online module, you will immediately be given access to a specifically crafted MCQ test. For each test, the pass mark will be set to 60%. Exam & Retakes: It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable. Certification Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of __ GBP. £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery). CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Data analysts and scientists seeking to expand their knowledge of neural networks and R programming Professionals interested in applying neural networks to agriculture or war datasets Students and researchers interested in deep learning and machine learning techniques Anyone looking to enhance their skills in data analysis and modelling using neural networks and R programming Requirements There are no formal entry requirements for the course, with enrollment open to anyone! Career path Data Analyst Machine Learning Engineer Data Scientist Artificial Intelligence Developer Research Scientist Entry-level positions such as Data Analysts can expect to earn between £25,000 to £35,000 per annum, whereas senior-level positions such as Machine Learning Engineers can earn upwards of £70,000 per annum. Certificates Certificate of completion Digital certificate - £9 You can apply for a CPD Accredited PDF Certificate at the cost of £9. Certificate of completion Hard copy certificate - £15 Hard copy can be sent to you via post at the expense of £15.