Python Calculus for Data Science and Machine Learning
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Microsoft Applied Skills Get started in C# (for beginners)
Free Download Microsoft Applied Skills Get started in C# (for beginners) Published 8/2025 Created by Phillip Burton • 940000+ students so far MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Beginner | Genre: eLearning | Language: English | Duration: 31 Lectures ( 2h 9m ) | Size: 909 MB
Mathematics for Machine Learning and AI I Essentials
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Generative AI for Customer Service Professionals By Di Le
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Free Download Filmmaking Masterclass: Script To Screen Secrets Published 8/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 7.16 GB | Duration: 3h 15m Learn professional filmmaking from a globally experienced director, with insider tips & a Master's from NYFA, LA
Perplexity AI Masterclass A Gen AI Certification Course
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Free Download Modify Data for Analysis in MongoDB Released 8/2025 By Pinal Dave MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 1h 18m | Size: 153 MB Learn how to safely modify data in MongoDB. This course will teach you how to update, replace, and delete documents with precision while following best practices for accuracy, safety, and collaboration
Microsoft Dynamics 365 CE & Power Platform Fundamentals 2025
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Machine learning Basics and Advanced Topics Using Python
Free Download Machine learning Basics and Advanced Topics Using Python Published 8/2025 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 47m | Size: 295 MB Machine learning What you'll learn Introduction, Machine Learning (ML) Definition, Types of learning Techniques: Supervised Learning, Un-supervised Learning, Reinforcement Learning Dataset Analysis, Preprocessing Techniques, Framework of ML Development for a Project in Business Explaining supervised ML algorithms such as Linear Regression, Logisitc Regression, Support vector Machines, Decision Trees, Naive bayes, KNN, Random Forrest Explaining unsupervised ML algorithms such as Hierarchical Clustering, DBSCAN, PCA Explaining Reinforcement Learning algorithms such as Q-learning, Deep Q-Network (DQN) Implementing ML algorithms using Python Requirements Python Description Introduction to Machine Learning •Overview•What is Machine Learning (ML)?•Workflow of Machine Learning Model•How to Obtain Best Results with a ML Model?•Types of Tasks Using Machine Learning Models•Terminologies•Responsibilities of Job Positions in Machine Learning•Some Applications of Machine Learning•Some Forecasting Applications Used in Business•Prediction of Time Series Data•Nature/behavior of Time series data may be include:•Other Applications Used in Business Using Machine Learning•Challenges of Machine Learning•Some Issues in Machine Learning•Hugging Face•Python Tools & Python LibrariesLearning Techniques •What is Difference between Traditional Programming & Machine Learning?•Machine learning in Practice•Machine learning FrameworksTypes of Learning•Supervised Learning•Unsupervised Learning•Reinforcement LearningML Tasks & Applications•Regression•Classification•Clustering•Dimensionality ReductionExample on Supervised Learning in Learning PhaseExample on Supervised Learning in Prediction PhaseML Learning Algorithms/TechniquesAdvs. & Disadvs. of ML AlgorithmsMachine learning (ML) for ClassificationMachine Learning (ML) for RegressionMachine Learning ProcessOverall Process of Building a ML ModelDataset Analysis • Data Overview• Dataset Workloads• Typical dataset composition• Sources of Dataset• Data Types• Framework for a Business Problem• Data Collection & labeling dataData Evaluation•Format of Data•Examine Data Types•Describe Dataset with its Statistics•VisualizationData Processing•Data cleansing•Feature EngineeringData Conversion•Data Encoding•Data scalingData ImbalancedSMOTESupervised Learning AlgorithmsLinear Regression (LR)Logistics RegressionSupport Vector Machine (SVM)Decision Tree (DT)Naïve Bayes (NB)K-Nearest Neighbor (KNN)Ensemble Learning: Bagging Techniques e.g. Random Forest (RF)Ensemble Learning: Boosting Techniques e.g. Gradient Boosting Decision Trees (GBDT)Unsupervised Learning AlgorithmsK-meansHierarchical ClusteringDBSCANPrinciple Component Analysis (PCA)Reinforcement LearningQ-LearningDeep Q-Network (DQN) Who this course is for for all Homepage https://www.udemy.com/course/machine-learning-basics-and-advanced-topics-using-python/ Buy Premium From My Links To Get Resumable Support,Max Speed & Support Me Rapidgator elytu.Machine.learning.Basics.and.Advanced.Topics.Using.Python.rar.html Fikper elytu.Machine.learning.Basics.and.Advanced.Topics.Using.Python.rar.html FreeDL elytu.Machine.learning.Basics.and.Advanced.Topics.Using.Python.rar.html No Password - Links are Interchangeable
Machine Learning Bootcamp Build Ml Models Using GenAI
Free Download Machine Learning Bootcamp: Build Ml Models Using GenAI Published 8/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 5.42 GB | Duration: 12h 24m Machine Learning for non-coders | Understand Machine Learning concepts & use GenAI to write code for building ML models What you'll learn Build a strong foundation in Python, statistics, and machine learning—covering regression, classification, and model evaluation Work with real datasets to clean, preprocess, and visualize data using NumPy, Pandas, and Seaborn for ML readiness Implement core ML algorithms in Python with ChatGPT-assisted coding for faster, cleaner, and more efficient development Master advanced ML techniques like ensemble methods, grid search, and SVMs to create high-performing predictive models Handle missing values, outliers, categorical variables, and feature scaling to improve model quality and accuracy Leverage ChatGPT to explain complex ML concepts, debug Python code, and generate efficient solutions in real-time Compare multiple models side-by-side to select the best fit for predictive accuracy and business requirements Requirements No prior programming or machine learning experience required—A computer with internet access, basic familiarity with browsing tools, and a willingness to explore AI-assisted learning methods. Description If you're an aspiring data scientist, analyst, or AI enthusiast looking to break into one of the most in-demand fields of the decade, imagine having a hands-on guide that teaches you not only the theory—but also how to code, implement, and fine-tune models—without getting lost in complexity. What if you could accelerate your learning curve by having an AI partner (ChatGPT) that helps you write cleaner code, debug faster, and understand concepts more intuitively?In this immersive, practical bootcamp, you'll gain the technical skills, problem-solving mindset, and project experience needed to work confidently with real-world machine learning applications. Whether you're building predictive models, classifying data, or tuning advanced algorithms, this course equips you to move from "learning about ML" to "building with ML" in record time.In this hands-on course, you will:Master the full ML workflow – from data import, exploration, and preprocessing to model building, evaluation, and optimization.Understand the math and logic behind key algorithms like Linear & Logistic Regression, Decision Trees, Random Forests, KNN, SVM, Boosting methods, and more.Learn with ChatGPT-assisted coding – using AI to generate, optimize, and debug Python code for faster, more accurate implementation.Work with Python's top ML libraries like NumPy, Pandas, Seaborn, Scikit-learn, and XGBoost.Build both regression and classification models and understand when to apply each.Gain experience in advanced topics like model tuning with Grid Search, feature engineering, ensemble methods, and kernel-based SVMs.Throughout the course, you'll:Use ChatGPT to write and refine Python code for ML tasks.Explore side-by-side the theory of an algorithm and its real Python implementation.Work with real-world datasets, handling missing values, outliers, and categorical variables.Compare and evaluate models to select the best approach for a given problem.Build a portfolio-ready set of projects that showcase both coding ability and ML understanding.Machine Learning is more than just knowing algorithms—it's about applying them effectively to real data. By the end of this bootcamp, you'll be able to confidently approach ML problems, build and optimize models, and leverage AI tools like ChatGPT to boost your productivity and accuracy.Whether you're preparing for a career in data science, adding ML to your skill set as a developer, or simply exploring the potential of AI-powered problem solving, you'll walk away with the skills, confidence, and workflow to succeed.Enroll today to build the future—one model at a time—powered by Python, guided by AI, and driven by data. Aspiring data scientists and analysts who want a structured, hands-on introduction to machine learning using Python.,Students and professionals from non-technical backgrounds eager to break into the ML/AI field with the help of ChatGPT-assisted coding.,Software developers, engineers, and IT professionals looking to expand their skill set into machine learning and predictive analytics.,Business analysts, managers, and domain experts who want to use data-driven models to solve real-world problems.,Anyone curious about how to apply machine learning—from regression to advanced ensemble methods—without needing prior ML experience. 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Introduction to Generative Adversarial Networks (GANs) (2025)
Free Download Introduction to Generative Adversarial Networks (GANs) (2025) Released 08/2025 With Gwendolyn Stripling MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 1h 1m 2s | Size: 86 MB Gain a practical understanding of Generative Adversarial Networks (GANs) and unlock the exciting possibilities of these powerful generative modeling technologies. Course details Generative Adversarial Networks (GANs) are a groundbreaking innovation in deep learning, capable of generating realistic images, audio, and synthetic data. In this hands-on course, Gwendolyn Stripling—a machine learning and artificial intelligence and PhD—provides a solid foundation in the architecture and training of GANs. Learn how GANs work and get proactive practice, applying GANs to tasks such as synthetic data and image generation. This course will help you confidently step into the world of generative modeling and discover how these architectures are applied to real-world tasks. Homepage https://www.linkedin.com/learning/introduction-to-generative-adversarial-networks-gans-26283306 Buy Premium From My Links To Get Resumable Support,Max Speed & Support Me Rapidgator ivkge.Introduction.to.Generative.Adversarial.Networks.GANs.2025.rar.html Fikper ivkge.Introduction.to.Generative.Adversarial.Networks.GANs.2025.rar.html FreeDL ivkge.Introduction.to.Generative.Adversarial.Networks.GANs.2025.rar.html No Password - Links are Interchangeable
Hands On AI Building Agents with the Google Agent Development Toolkit (ADK)
Free Download Hands-On AI: Building Agents with the Google Agent Development Toolkit (ADK) Released: 08/2025 Duration: 2h 38m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 344 MB Level: Intermediate | Genre: eLearning | Language: English This course teaches the skills to build and test your own agents using the Google Agent Development Kit. Instructor Jigyasa Grover shows you how to define agents, equip them with various tools including custom functions and third-party integrations, and configure their underlying language models. The course also covers managing input, output, and context within agents to enhance their functionality. Jigyasa also explores the concept of workflow agents, including sequential, loop, and parallel agent systems, and how to leverage built-in and MCP tools within the ADK. By the end of this course, you should understand how to develop agents and multi-agent systems using the Google Agent Development Kit. Homepage https://www.linkedin.com/learning/hands-on-ai-building-agents-with-the-google-agent-development-toolkit-adk Buy Premium From My Links To Get Resumable Support,Max Speed & Support Me UploadCloud fssif.HandsOn.AI.Building.Agents.with.the.Google.Agent.Development.Toolkit.ADK.rar.html Rapidgator fssif.HandsOn.AI.Building.Agents.with.the.Google.Agent.Development.Toolkit.ADK.rar.html Fikper fssif.HandsOn.AI.Building.Agents.with.the.Google.Agent.Development.Toolkit.ADK.rar.html FreeDL fssif.HandsOn.AI.Building.Agents.with.the.Google.Agent.Development.Toolkit.ADK.rar.html No Password - Links are Interchangeable
Google Workspace Administrator Associate Examprep 2025
Free Download Google Workspace Administrator Associate – Examprep – 2025 Published 8/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 5.69 GB | Duration: 17h 49m All-in-one preparation for passing the Google Workspace Administrator Associate certification exam - Updated 2025 What you'll learn Set up and manage Google Workspace accounts and organizational units. Create, manage, and secure user accounts and groups. Configure core services like Gmail, Drive, Calendar, and Meet. Manage domain settings, including domain aliases and DNS configuration. Implement security policies, including 2-Step Verification and password policies. Assign and manage administrator roles and permissions. Monitor usage and audit activity using reports and logs. Troubleshoot common issues across services and users. Plan and execute data migration to Google Workspace. Prepare confidently for the Google Workspace Administrator Associate certification exam. Requirements Basic understanding of IT concepts such as user accounts, domains, and cloud services. Familiarity with Google Workspace tools (like Gmail, Drive, Calendar) is helpful but not mandatory. Access to a Google Workspace trial or admin account for hands-on practice (optional but recommended). A computer with internet access to follow along with demonstrations and labs. Willingness to learn and explore the Admin Console. No prior Google Workspace admin experience required — this course is beginner-friendly. Description Get fully prepared to become a Google Workspace Administrator Associate with this comprehensive, hands-on course designed specifically for the 2025 exam and real-world admin responsibilities.Whether you're aiming to pass the certification exam, manage a Google Workspace domain in production, or deepen your admin console knowledge, this course walks you through every critical skill, configuration, and concept — step by step.We start by building a solid foundation: you'll explore the Google Workspace ecosystem, understand how the Admin Console functions, and learn about the key services under your control — including Gmail, Calendar, Drive, Meet, and more.As you progress, you'll take a deep dive into user and organizational unit (OU) management. You'll learn how to create users, apply policies to different departments, configure access controls, and structure your environment for optimal management.The course includes detailed walkthroughs of role-based access control, where you'll assign admin roles with principle of least privilege, ensuring both security and delegation efficiency. You'll also explore group management for simplified policy enforcement and communication workflows.A major focus is placed on security and compliance settings — including password policies, 2-Step Verification (2SV), app access control, and context-aware access — all critical for safeguarding your organization.In addition, you'll configure and troubleshoot DNS records (SPF, DKIM, DMARC), verify domains, and understand domain aliasing and routing. You'll even go under the hood of email routing rules, user policies, and service-specific configurations.You'll work with realistic case studies and hands-on lab simulations, giving you the confidence to solve admin challenges in actual production environments. From setting up custom admin roles to auditing logs for suspicious activity, you'll gain the expertise to act decisively and securely.We also include extensive coverage of reporting and monitoring tools in Google Workspace. You'll learn how to track user behavior, audit login activity, and pull data from reports that help drive decisions or investigations.Finally, the course wraps up with exam-specific guidance, including domain-by-domain breakdowns, sample questions, exam tips, and a mock exam that mirrors the real test experience.Throughout the course, you'll benefit from:Real-world scenarios and lab demos using the Admin ConsoleClear, jargon-free explanations of every feature and settingUpdates aligned with the latest Google Workspace features and exam objectivesExam readiness checkpoints to measure your progress Aspiring IT professionals preparing for the Google Workspace Administrator Associate certification.,New or junior system administrators looking to manage Google Workspace environments.,IT support staff transitioning into cloud-based admin roles.,Freelancers or consultants managing Google Workspace for clients.,Students and recent graduates interested in cloud administration careers.,Small business owners or team leads managing their own Google Workspace instance.,Anyone curious about cloud-based IT management using Google's tools and services. 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