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Free Download [OFFER] Linkedin - Deep Learning Fundamentals for Healthcare Released 04/2025 With Wuraola Oyewusi MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 2h 26m 7s | Size: 290 MB Learn about deep learning in healthcare with this comprehensive course, including fundamentals, practical applications, advanced techniques, and more. Course details Explore the exciting world of deep learning applications in healthcare through this in-depth course. Learn how to classify and detect abnormalities in X-ray images through convolutional neural networks (CNNs), fine-tuning pre-trained models, and leveraging zero-shot learning. Understand the basics of deep learning, including neural networks, model training, and hyperparameter tuning tailored specifically to healthcare. Engage in hands-on activities where you'll preprocess data, build models with Python, and utilize frameworks like TensorFlow and PyTorch. Develop practical skills in object detection and segmentation to diagnose and detect medical conditions effectively. Gain insights into ethical considerations and data limitations pertinent to applying AI in a medical context. By the end of this course, you will be equipped to apply deep learning techniques to real-world healthcare challenges, improving diagnostic accuracy and patient outcomes. Homepage: [hide]

Free Download [OFFER] Machine Learning Algorithms in Depth, Video Edition by Vadim Smolyakov Published:: 1/2025 Duration: 6h 21m | Video: .MP4, 1920x1080 30 fps | Audio: AAC, 44.1kHz, 2ch | Size: 1.11 GB Genre: eLearning | Language: English In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video. Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance. Fully understanding how machine learning algorithms function is essential for any serious ML engineer. In Machine Learning Algorithms in Depth you'll explore practical implementations of dozens of ML algorithms including Monte Carlo Stock Price Simulation Image Denoising using Mean-Field Variational Inference EM algorithm for Hidden Markov Models Imbalanced Learning, Active Learning and Ensemble Learning Bayesian Optimization for Hyperparameter Tuning Dirichlet Process K-Means for Clustering Applications Stock Clusters based on Inverse Covariance Estimation Energy Minimization using Simulated Annealing Image Search based on ResNet Convolutional Neural Network Anomaly Detection in Time-Series using Variational Autoencoders Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probabilistic algorithms, you'll learn the fundamentals of Bayesian inference and deep learning. You'll also explore the core data structures and algorithmic paradigms for machine learning. Each algorithm is fully explored with both math and practical implementations so you can see how they work and how they're put into action. About the Technology Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance. This book guides you from the core mathematical foundations of the most important ML algorithms to their Python implementations, with a particular focus on probability-based methods. About the Book Machine Learning Algorithms in Depth dissects and explains dozens of algorithms across a variety of applications, including finance, computer vision, and NLP. Each algorithm is mathematically derived, followed by its hands-on Python implementation along with insightful code annotations and informative graphics. You'll especially appreciate author Vadim Smolyakov's clear interpretations of Bayesian algorithms for Monte Carlo and Markov models. What's Inside Monte Carlo stock price simulation EM algorithm for hidden Markov models Imbalanced learning, active learning, and ensemble learning Bayesian optimization for hyperparameter tuning Anomaly detection in time-series [hide]

Free Download [OFFER] Linkedin - Learning XAI Explainable Artificial Intelligence (2025) Released 04/2025 With Jazmia Henry MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 2h 39m 38s | Size: 507 MB This course explores how to identify, evaluate, and mitigate bias in large language models through data curation, mathematical analysis, and model constraints. Course details This course focuses on the data-based and mathematical factors contributing to bias in generative AI. Join instructor Jazmia Henry as she explores how data curation, analytical techniques, and post-training constraints can mitigate harmful biases embedded in these models. Through real-world examples and case studies, the course aims to help you understand and apply strategies for creating fairer, more transparent AI systems. Homepage: [hide]

Free Download [OFFER] Linkedin - Learning Blender Design Your First 3D Object Released: 04/2025 Duration: 49m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 191 MB Level: Beginner | Genre: eLearning | Language: English Looking to get started with Blender, the popular 3D-modeling tool? This course, designed by Skillshare, guides you through Blender's interface and fundamental 3D modeling techniques by constructing a scene that includes a cabinet, shelves, window, and pots-all illuminated by a realistic sky texture. Whether you're a graphic designer, architect, industrial designer, or simply curious about 3D, this course helps to outline an accessible path to jumpstart your exploration of 3D design. Discover how to navigate and customize Blender's UI for an efficient workflow. Practice building simple 3D objects, applying lighting, color, and materials, and then refining everything with denoising and rendering. By the end of this course, you'll be equipped with practical skills to bring your 3D creations to life. Homepage: [hide]

Free Download [OFFER] Linkedin - Learning NotebookLM Your AI-Powered Research Assistant Released 04/2025 With Nick Brazzi MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 33m 23s | Size: 117 MB Explore ways to leverage NotebookLM as an AI-powered research tool. Use it to gather valuable insights and compose notes from documents and other source materials. Course details Using Google's NotebookLM is like working with an AI-powered research assistant. You provide information sources and Google's Gemini AI helps you organize that information in notes, create summaries, and even generate a podcast-style discussion using the Audio Overview feature. And now you can ask questions in the Audio Overview with the Interactive Mode. LinkedIn Staff Instructor Nick Brazzi shows how to manage your information sources, organize your notes, and review results for accuracy. Homepage [hide]

Free Download [OFFER] Linkedin - Getting Started as a LinkedIn Learning Admin Updated: 04/2025 Duration: 1h 2m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 123 MB Level: Beginner | Genre: eLearning | Language: English Learn how to manage your organization's LinkedIn Learning account as an administrator. LinkedIn Learning staff instructors explain how to navigate the administration homepage and find the features available to you through your organization's LinkedIn Learning account. Find out how to quickly add and edit users, organize learners into groups, assign permissions, and recommend content, including curated learning paths and collections. Plus, learn how to use the reporting features for a visual "at a glance" or high-level view of usage. Homepage [hide]

Free Download [OFFER] Deep Learning with JAX, Video Edition by Grigory Sapunov Published:: 11/2024 Duration: 10h 58mm | Video: .MP4, 1920x1080 30 fps | Audio: AAC, 44.1kHz, 2ch | Size: 2.46 GB Genre: eLearning | Language: English In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video. Accelerate deep learning and other number-intensive tasks with JAX, Google's awesome high-performance numerical computing library. The JAX numerical computing library tackles the core performance challenges at the heart of deep learning and other scientific computing tasks. By combining Google's Accelerated Linear Algebra platform (XLA) with a hyper-optimized version of NumPy and a variety of other high-performance features, JAX delivers a huge performance boost in low-level computations and transformations. In Deep Learning with JAX you will learn how to Use JAX for numerical calculations Build differentiable models with JAX primitives Run distributed and parallelized computations with JAX Use high-level neural network libraries such as Flax Leverage libraries and modules from the JAX ecosystem Deep Learning with JAX is a hands-on guide to using JAX for deep learning and other mathematically-intensive applications. Google Developer Expert Grigory Sapunov steadily builds your understanding of JAX's concepts. The engaging examples introduce the fundamental concepts on which JAX relies and then show you how to apply them to real-world tasks. You'll learn how to use JAX's ecosystem of high-level libraries and modules, and also how to combine TensorFlow and PyTorch with JAX for data loading and deployment. About the Technology Google's JAX offers a fresh vision for deep learning. This powerful library gives you fine control over low level processes like gradient calculations, delivering fast and efficient model training and inference, especially on large datasets. JAX has transformed how research scientists approach deep learning. Now boasting a robust ecosystem of tools and libraries, JAX makes evolutionary computations, federated learning, and other performance-sensitive tasks approachable for all types of applications. About the Book Deep Learning with JAX teaches you to build effective neural networks with JAX. In this example-rich book, you'll discover how JAX's unique features help you tackle important deep learning performance challenges, like distributing computations across a cluster of TPUs. You'll put the library into action as you create an image classification tool, an image filter application, and other realistic projects. The nicely-annotated code listings demonstrate how JAX's functional programming mindset improves composability and parallelization. What's Inside Use JAX for numerical calculations Build differentiable models with JAX primitives Run distributed and parallelized computations with JAX Use high-level neural network libraries such as Flax [hide]

Free Download [OFFER] Linkedin - Learning Data Science (2025) Released: 04/2025 Duration: 2h 45m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 503 MB Level: Intermediate | Genre: eLearning | Language: English Many people who work on data science teams will become something other than data scientists. That said, many will become managers and associates who want to gain real business value from your organization's data. These team members need to understand the language of data science so they can ask better questions, understand processes, and help effectively lead their teams and organizations to making better data-driven decisions. In this course, get an introduction to data science for people who aren't planning on working as full-time data scientists. Explore big data concepts, tools, and techniques, including gathering and sorting data, working with databases, understanding structured and unstructured data types, applying statistical analysis, asking critical questions, and telling stories about data. Business coach and author Doug Rose helps you speak the language of data science so that you can guide your organization through the opportunities and limitations in this dramatically growing field. Homepage: [hide]

Free Download [OFFER] Linkedin - Gaining Skills with LinkedIn Learning Updated: 04/2025 Duration: 13m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 40 MB Level: Beginner | Genre: eLearning | Language: English With LinkedIn Learning, anyone can gain new skills. We offer expert-led, anytime training that you can take at your own pace, with tools and features to fit almost any learning style. Use this course to discover how you learn best and how LinkedIn Learning can help you set and achieve your personal and professional goals. Staff author Oliver Schinkten shows how to use LinkedIn Learning alongside cutting-edge, brain-based research to pinpoint the skills you want to learn, find the training to reach your goals, and make the knowledge stick. Discover which skills are in demand and how to showcase what you've learned on LinkedIn. Homepage: [hide]

Free Download [OFFER] Pluralsight - Learning Path - The Forecast Published: 2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 48 KHz Language: English | Size: 843.83 MB | Duration: 1h 9m The Forecast is designed to provide periodic and timely updates on recent developments in Cloud Technology delivered by subject matter experts. Homepage: [hide]

Free Download [OFFER] Udemy - Accelerate Your Learning With Master Studies In Pen And Ink Published: 3/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 2.82 GB | Duration: 1h 7m Get the Results You Want What you'll learn How to conduct analysis of masterworks for learning How to develop observation skills for drawing with pen and ink How to self-assess skills and knowledge as an artist How to set up a study blueprint and practice the right things Requirements Prior drawing experience is helpful. Description Accelerate your pen and ink drawing skills with this step-by-step process to study the works of master artists.Pen and ink is a drawing medium. Drawing is the ability to translate what you see to paper.What I love about studying the masters is that it helps grow observation skills.Better observation skills, means more convincing art.But, to get from observation to a finished piece, there are decisions to make and problems to solve.In this course, you'll learn effective methods to study masterpieces that will accelerate your decision-making and problem-solving skills in your art projects.The comprehensive class workbook includes:Self-assessmentsObservation Analysis TemplateStudy Guide TemplatePractice ExercisesDefinitionsAdditional Resources and TipsAt the end of this class, you'll have created a custom study blueprint. You'll know exactly what to work on so you can confidently progress to more advanced projects.You can use the tools and supplies you have on hand. At minimum you'll want:Pencil and eraserA set of inking pens, one with a thicker tipSketching paperOptional: Brush pen, dip pen, India ink, inking paperHi! I'm Chloe, a learning specialist turned full-time artist. I'm happy to share my methods to help pen and ink enthusiasts reach their art goals sooner. For more on how to use dip pens and my 5-step workflow method, check out my course: Dip Pens for Realistic Drawing Overview Section 1: About You Lecture 1 Your Influences Lecture 2 Your Vision Lecture 3 Self-assessment Section 2: The Fundamentals Lecture 4 The Art Fundamentals for Pen and Ink Lecture 5 How to Choose Masters to Study Section 3: Analysis and Study Exercises Lecture 6 Analysis | Franklin Booth Lecture 7 Study | Franklin Booth Lecture 8 Analysis | James Montgomery Flagg Lecture 9 Study | James Montgomery Flagg Lecture 10 Analysis | Moebius Lecture 11 Study | Moebius Lecture 12 Analysis | Bernie Wrightson Lecture 13 Study | Bernie Wrightson Lecture 14 Analysis | Takehiko Inoue and Kentaro Miura Lecture 15 Study | Takehiko Inoue and Kentaro Miura Section 4: Setting Up Your Objectives Lecture 16 More Exercise Ideas Lecture 17 Pitfalls to Avoid Lecture 18 Your Study Blueprint and Monitoring Progress Section 5: Wrap Up Lecture 19 Master Studies Course Conclusion For enthusiasts of pen and ink drawing Homepage: Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [hide] No Password - Links are Interchangeable

Free Download [OFFER] Udemy - Clustering & Unsupervised Learning in Python Published: 3/2025 Created by: Meta Brains,Skool of AI MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: All | Genre: eLearning | Language: English | Duration: 52 Lectures ( 4h 53m ) | Size: 1.57 GB Discover Hidden Data Patterns: Master K-Means, Hierarchical Clustering, DBSCAN & E-Commerce Segmentation What you'll learn Understand the fundamentals of clustering and its applications in data science. Implement K-Means clustering algorithm in Python step by step. Master DBSCAN algorithm for density-based clustering techniques. Explore Hierarchical Clustering and its real-world use cases. Conduct unsupervised learning analysis to uncover hidden data patterns. Visualize clusters effectively using Python libraries like Matplotlib. Preprocess and prepare raw data for efficient clustering tasks. Perform evaluation metrics to assess clustering performance accurately. Requirements Basic understanding of Python programming is helpful but not required. No prior knowledge of machine learning or clustering is needed. A computer with internet access to install Python and required libraries. Willingness to learn and explore unsupervised machine learning concepts. Description In a world drowning in data, those who can reveal the hidden patterns hold the true power. While others see chaos, you'll see natural groupings and actionable insights that drive real-world decisions. This comprehensive course transforms you from data novice to clustering expert through straightforward explanations and engaging hands-on projects.Unlike theoretical courses that leave you wondering "so what?", Pattern Whisperer is built around practical applications you'll encounter in your career or personal projects. We've stripped away the unnecessary complexity to focus on what actually works in real-world scenarios.Through this carefully crafted learning journey, you'll:Master the fundamentals of unsupervised learning with clear, jargon-free explanations that build your intuition about how machines find patterns without explicit guidanceImplement K-Means clustering from scratch and understand exactly when and how to apply this versatile algorithm to your own datasetsVisualize data relationships with hierarchical clustering and interpret dendrograms to uncover natural groupings your competitors might missDiscover outliers and density-based patterns using DBSCAN, perfect for geographic data and detecting anomalies that simple algorithms overlookPrepare and transform real-world data for effective clustering, including handling messy datasets that don't arrive in perfect conditionApply multiple clustering techniques to a comprehensive e-commerce customer segmentation project, creating actionable customer profiles that drive business strategyEvaluate and optimize your clustering results with practical metrics and visualization techniques that confirm you're extracting maximum insightEach concept is reinforced with mini-projects that build your confidence, from organizing everyday items to grouping friends by interests, before culminating in our major e-commerce segmentation project that ties everything together.By course completion, you'll possess the rare ability to look at raw, unlabeled data and extract meaningful patterns that inform strategic decisions - a skill increasingly valued across industries from marketing to finance, healthcare to technology.Don't settle for seeing only what's obvious in your data. Enroll now and develop your "pattern whispering" abilities to reveal insights hiding in plain sight. Your data is already speaking - it's time you learned how to listen. Who this course is for Beginners curious about machine learning and data science concepts. Data enthusiasts looking to explore unsupervised learning techniques. Python programmers aiming to enhance their skillset with clustering methods. Students or professionals transitioning into the field of data analytics. Analysts seeking to uncover hidden patterns in datasets. Anyone interested in practical applications of clustering algorithms. Homepage: Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [hide] No Password - Links are Interchangeable

Free Download [OFFER] Udemy - AI & Deep Learning From Scratch In Python Last updated: 4/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.44 GB | Duration: 5h 16m Understand Convolutional Neural Networks and Implement your Object-Detection Framework From Scratch What you'll learn Understand how Deep Neural Networks work, practically and mathematically Understand Forward- and Backpropagation processes, mathematically and practically Design and implement a Deep Neural Network for multi-class classification Understand and implement the building blocks of Convolutional Neural Networks Understand and Implement cutting-edge Optimization, Regularization and Initialization techniques Train and validate a Convolutional Model on widely used datasets like MNIST and CIFAR-10 Understand and implement Transfer Learning Use a Convolutional Model to create a Real-Time, Multi-Object Detection System Requirements No prior knowledge is required Description This course is for anyone willing to really understand how Convolutional Neural Networks (CNNs) work. Every component of CNNs is first presented and explained mathematically, and the implemented in Python.Interactive programming exercises, executable within the course webpage, allow to gradually build a complete Object-Detection Framework based on an optimized Convolutional Neural Network model. No prior knowledge is required: the dedicated sections about Python Programming Basics and Calculus for Deep Learning provide the necessary knowledge to follow the course and implement Convolutional Neural Networks.In this course, students will be introduced to one of the latest and most successful algorithms for real-time multiple object detection. Throughout the course, they will gain a comprehensive understanding of the Backpropagation process, both from a mathematical and programming perspective, allowing them to build a strong foundation in this essential aspect of neural network training.By the course's conclusion, students will have hands-on experience implementing a sophisticated convolutional neural network framework. This framework will incorporate cutting-edge optimization and regularization techniques, enabling them to tackle complex real-world object detection tasks effectively and achieve impressive performance results. This practical knowledge will empower students to advance their capabilities in the exciting field of Computer Vision and Deep Learning. Everyone interested in really understanding Convolutional Neural Networks and willing to create their own Object Detection Framework in Python Homepage: Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [hide] No Password - Links are Interchangeable

Free Download [OFFER] Udemy - Master Machine Learning & AI with Python Published: 3/2025 Created by: Paul Carlo Tordecilla MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Beginner | Genre: eLearning | Language: English | Duration: 62 Lectures ( 5h 4m ) | Size: 2.46 GB Building Intelligent Systems from the Ground Up What you'll learn Understand the theory behind machine learning algorithms, including supervised, unsupervised, and reinforcement learning. Learn data preprocessing, feature engineering, and visualization methods to prepare data for modeling. Gain hands-on experience building and evaluating models for regression, classification, clustering, and recommendation systems using Python. Explore deep learning, neural networks, generative models, and advanced topics like meta-learning, federated learning, and graph neural networks through real-wo Discover how to deploy machine learning models, optimize performance with distributed computing, and integrate AI solutions into applications. Requirements Familiarity with Python programming, including data types, control structures, and functions. A basic understanding of algebra, calculus, and statistics to grasp algorithmic concepts. Prior exposure to simple ML concepts or courses can be beneficial, though not mandatory for beginners. Working knowledge of libraries like NumPy and Pandas for data manipulation and analysis. A proactive attitude toward solving problems, experimenting with code, and building projects. Description Embark on a transformative journey into the world of Machine Learning and Artificial Intelligence with our comprehensive online course. Designed for beginners and intermediate learners alike, this course bridges theory and practice, enabling you to master key concepts, techniques, and tools that drive today's intelligent systems. Whether you're aiming to launch a career in data science, build innovative projects, or simply expand your technical prowess, this course provides the robust foundation and hands-on experience you need.What you'll learnIntroduction to Machine LearningWhat is Machine Learning?Understand the definition, historical evolution, and transformative impact of machine learning in various industries.Types of Machine Learning:Dive deep into supervised, unsupervised, and reinforcement learning with real-world applications.Applications & Tools:Explore practical use cases across industries and get acquainted with the Python ecosystem and essential libraries like NumPy, Pandas, and Scikit-Learn.Data PreprocessingUnderstanding Data:Learn to distinguish between structured and unstructured data, and use visualization techniques to explore datasets.Data Cleaning & Feature Engineering:Master techniques for handling missing data, encoding categorical variables, feature scaling, and engineering new features.Data Splitting:Get hands-on experience with training/testing splits and cross-validation to ensure robust model performance.Regression TechniquesStart with Simple Linear Regression and progress to Multiple Linear, Polynomial Regression, and more advanced methods like Support Vector Regression, Decision Tree, and Random Forest Regression.Learn how to tackle issues like multicollinearity, overfitting, and implement these models using Python.Classification TechniquesFoundational Algorithms:Gain insights into Logistic Regression, K-Nearest Neighbors (KNN), and Support Vector Machines (SVM) for both binary and multiclass problems.Advanced Methods:Understand Naive Bayes, Decision Trees, and ensemble methods such as Random Forests and boosting algorithms like AdaBoost, GBM, and XGBoost.Deep Dive into XGBoost:Learn the introduction to XGBoost and explore its advanced concepts, making it a powerful tool for your classification tasks.Clustering TechniquesExplore unsupervised learning with K-Means, Hierarchical Clustering, DBSCAN, and Gaussian Mixture Models.Understand how to determine optimal cluster numbers and interpret dendrograms for meaningful insights.Association Rule LearningApriori & Eclat Algorithms:Learn how to mine frequent itemsets and derive association rules to uncover hidden patterns in data.Natural Language Processing (NLP)Text Processing Fundamentals:Delve into tokenization, stopword removal, stemming, and lemmatization.Vectorization Techniques:Build models using Bag of Words and TF-IDF, and explore sentiment analysis to interpret textual data.Deep LearningNeural Networks & Training:Understand the architecture, training processes (forward and backpropagation), and optimization techniques of neural networks.Specialized Networks:Learn about Convolutional Neural Networks (CNNs) for image recognition and Recurrent Neural Networks (RNNs) including LSTM for sequence modeling.Hands-On with Keras & TensorFlow:Build, evaluate, and tune models using industry-standard frameworks.Why Enroll?Comprehensive Curriculum:Our course is meticulously structured to take you from foundational concepts to advanced machine learning techniques, ensuring a holistic understanding of the field.Hands-On Learning:With practical labs and real-world projects, you'll not only learn the theory but also gain the experience needed to implement your ideas effectively.Expert Guidance:Learn from seasoned professionals who bring real industry experience and cutting-edge insights into every lesson.Career Advancement:Gain in-demand skills that are highly valued in tech, finance, healthcare, and beyond, positioning you for success in a rapidly evolving job market.Community & Support:Join a vibrant community of learners and experts, engage in discussions, receive feedback, and collaborate on projects to accelerate your learning journey.Enroll Now!Don't miss this opportunity to transform your career with advanced skills in Machine Learning and AI. Whether you're aspiring to build intelligent systems, analyze complex data, or innovate in your current role, this course is your gateway to success. Secure your spot today and start building the future!Ready to revolutionize your learning journey? Enroll now and become a leader in the era of AI! Who this course is for Individuals looking to start a career in data science and machine learning with a solid practical foundation. Developers who want to expand their skill set to include AI and machine learning technologies. University students or researchers interested in applying ML concepts to academic projects or research problems. Professionals from various fields seeking to transition into roles that focus on data analytics and machine learning. Anyone passionate about technology, eager to build real-world AI projects and deepen their understanding of advanced ML techniques. 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Does Anybody has Sophos Certified Administrator XG Firewall official Learning Material including Training Videos?