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AI Agents: From Foundations to Enterprise Systems

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4.3(3.8K students)

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Disclaimer: This course contains the use of artificial intelligence(AI).AI agents are rapidly transforming how software is built, decisions are made, and work gets done across industries. This course is a comprehensive, hands-on journey into designing, building, and deploying intelligent AI agents—from foundational concepts to enterprise-grade systems. Over 52 structured weeks, learners progress step by step through the full lifecycle of agentic AI without relying on a single capstone project, ensuring continuous, practical learning every week.You begin by mastering the core foundations of AI agents, including agent architectures, perception–action loops, reasoning, planning, and memory.

Early modules focus on understanding how large language models power modern agents, how prompts differ from programs, and how agents decompose complex goals into executable tasks. Through guided labs, you build your first agents, add memory, enable tool usage, and implement structured reasoning patterns that go far beyond simple chatbots.As the course progresses, you move into agent frameworks, orchestration, and multi-agent collaboration. You learn how agents communicate, delegate tasks, resolve conflicts, and operate as coordinated systems rather than isolated components.

Hands-on labs emphasize real execution—building sequential and parallel workflows, debugging agent failures, evaluating outputs, and optimizing for latency and cost. You gain practical experience designing agents that are reliable, explainable, and measurable.A major focus of the course is knowledge-driven and data-aware agents. You build retrieval-augmented agents, integrate structured and unstructured data sources, work with documents, and design long-term memory systems that persist and evolve over time.

You also explore advanced capabilities such as multi-modal agents that reason across text, images, and audio, as well as real-time and event-driven agents that respond dynamically to changing inputs.The course then shifts into enterprise-grade agent systems. You learn how to secure agents, prevent prompt injection, enforce governance, and design human-in-the-loop workflows. Topics such as observability, monitoring, versioning, scaling, and cost optimization prepare you to deploy agents in production environments.

Ethical considerations and responsible AI practices are woven throughout, ensuring agents are safe, transparent, and aligned with organizational policies.In the final phase, you apply agentic AI across real business domains including HR, finance, sales, IT operations, compliance, research, and personal productivity. Each week includes multiple topics, two hands-on labs, and a practical homework assignment, reinforcing skills through continuous application rather than a single end-of-course project.By the end of this course, learners will confidently design, build, deploy, and govern AI agents that operate autonomously, collaborate effectively, and deliver real business value—equipping them with future-ready skills for the rapidly evolving world of agentic AI.

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AI System Design & MLOps: From Raw Data to AWS Kubernetes (End-to-End Project)Stop Learning Machine Learning in IsolationMost machine learning courses focus on building models in isolation. You train a model, evaluate accuracy, and consider the job done.But in real-world systems, that is only a small part of the problem.Organizations do not need models. They need systems that can:ingest and process real-world datagenerate reliable predictionsserve those predictions through APIsmonitor performance over timeadapt when data changesThis course is designed to bridge that gap.The Story Behind This CapstoneImagine a large hospital network handling thousands of patients every day.Patients arrive with different conditions. Some cases are routine, while others escalate into high-risk situations requiring immediate attention. At the same time, every visit generates billing records, which are later submitted to insurance providers. Some claims are approved quickly, while others are delayed or rejected, leading to revenue loss and operational inefficiencies.Now consider the questions hospital leadership is asking:Can we identify high-risk patient visits early so that resources can be allocated proactively?Can we predict which claims are likely to be rejected before they are submitted?Can we continuously monitor the system and adapt when patient patterns or insurance behaviors change?These are not just modeling questions. They require a complete, well-designed system.In this course, you will build that system from the ground up.What You Will BuildYou will design and implement a complete healthcare AI platform that includes:1. Data LayerYou will start with raw datasets such as patients, visits, and billing records. Instead of working directly on CSV files, you will create a structured analytics layer using SQL, ensuring that data can be queried, validated, and joined properly.You will then perform exploratory data analysis and build meaningful features such as visit frequency, average length of stay, and provider rejection rates.2. Machine Learning LayerYou will build two real-world models:A visit risk classifier that predicts whether a patient visit is low, medium, or high riskA claim outcome predictor that determines whether a claim will be paid, pending, or rejectedYou will implement multiple algorithms, including Logistic Regression, Random Forest, and XGBoost, and evaluate them using proper metrics such as precision, recall, and F1 score.More importantly, you will understand how data quality impacts model performance and how fixing labels can dramatically improve outcomes.3. MLOps LayerThis is where the system becomes production-ready.You will integrate:MLflow for experiment tracking and model versioningDVC for data versioning and reproducible pipelinesYou will define clear artifacts such as trained models, feature schemas, and prediction logs, ensuring that every step in the pipeline is traceable and repeatable.4. Serving LayerYou will expose your models through a FastAPI-based service with well-defined endpoints for prediction.You will enforce input validation using Pydantic and build a browser-based interface using Gradio for demonstration purposes.You will also implement monitoring mechanisms such as PSI-based drift detection to identify when the system starts behaving differently due to changes in incoming data.5. Cloud Deployment LayerYou will containerize your application using Docker and push images to AWS Elastic Container Registry.You will then deploy the system on AWS EKS using Kubernetes, enabling scalability, high availability, and zero-downtime updates.A complete CI/CD pipeline using GitHub Actions will automate build, test, and deployment steps.6. Continuous Retraining LoopThe system does not stop after deployment.You will implement a feedback loop where:predictions are loggeddrift is detectedretraining is triggered using DVC pipelinesThis ensures that the system continuously improves as new data flows in.How This Course Connects the DotsOne of the biggest challenges in learning AI and machine learning is fragmentation. You learn SQL in one place, modeling in another, APIs somewhere else, and cloud deployment separately.This course connects all of these pieces into a single, coherent system.You will see how:raw data flows into structured analyticsfeatures feed into modelsmodels are tracked and versionedpredictions are served via APIssystems are deployed to the cloudmonitoring drives retrainingBy the end, you will not just understand individual tools. You will understand how they work together.Who This Course Is ForThis course is ideal for:software engineers who want to transition into AI/ML systemsmachine learning practitioners who want to learn production deploymentbackend developers interested in building AI-powered APIsarchitects who want to understand end-to-end AI system designWhat You Will Walk Away WithBy the end of this course, you will have:built a complete end-to-end AI systemdeployed it on AWS using modern cloud practicesimplemented monitoring and retraining mechanismsdeveloped a strong understanding of production-first architectureMore importantly, you will develop the ability to think beyond models and design systems that deliver real business value.Final NoteThis is not a course about isolated concepts. It is about building something that resembles real-world systems.If your goal is to move from learning machine learning to applying it in production, this course is designed for you.Production-first architecture is not an advanced topic. It is the standard.

Data Analytics with Python & AI

Udemy Instructor

Disclaimer: This course contains the use of artificial intelligence(AI).Most data courses teach you Python syntax. This course teaches you how to think like a data analyst, work like a professional, and get hired like one. you will go from writing your first line of Python to deploying a complete data analytics project — with machine learning, AI-powered insights, interactive dashboards, and a GitHub portfolio that proves you can do the work.This is not a theory course. Every lecture has real data, real code, and real business decisions behind it.WHY THIS BOOTCAMP IS DIFFERENTEvery other Python for data course stops at the technical skills. This one goes further. You will learn how to use AI tools like the Anthropic API to analyze data 5x faster, how to communicate insights to executives using the SCQA framework, and how to position yourself for U.S. data analyst roles as an international professional navigating OPT, STEM OPT, and H-1B pathways.The skills in this course are not academic. They are the exact skills hiring managers test in data analyst interviews at U.S. companies right now.WHAT YOU WILL BUILDBy the end of this bootcamp you will have completed a full end-to-end analytics project . Your deliverables will include a clean, documented GitHub repository, a machine learning model that predicts high-value orders with over 80% accuracy, an interactive Plotly Dash dashboard your stakeholders can actually use, an AI-generated executive summary built with the Anthropic API, and an automated Python report that runs and emails itself on a schedule.That portfolio project is your most powerful job application tool. More credible than a certificate. More specific than a resume bullet.WHAT IS COVERED — DAY BY DAYFirst day covers Python foundations built specifically for data work. You will set up your environment, learn the only Python you actually need, and read your first real-world dataset.Day 2 covers data cleaning and wrangling with pandas. You will handle nulls, fix data types, remove duplicates, merge DataFrames, and build an analysis-ready dataset from scratch.Day 3 covers exploratory data analysis. You will apply descriptive statistics correctly, build professional visualizations with Matplotlib and Seaborn, and generate 10 business insights from a real e-commerce dataset without touching a single machine learning model.Day 4 covers AI-assisted analysis. You will make your first API call to a large language model, build a personal analyst assistant in Python, and master prompt engineering techniques specifically designed for data tasks.Day 5 covers machine learning fundamentals. You will build regression and classification models with scikit-learn, interpret every output in plain English, and evaluate your models correctly using cross-validation, confusion matrices, and F1 scores.Day 6 covers dashboards and data storytelling. You will build interactive dashboards with Plotly and Dash, structure analyst presentations using the SCQA framework, and automate a complete reporting pipeline that generates and emails a PDF report on a schedule.Last Day covers your capstone project and career positioning. You will complete a full end-to-end analytics project, publish it on GitHub with a recruiter-ready README, and build a 30-day job search action plan designed specifically for international professionals in the U.S. market.WHO THIS COURSE IS FORThis course is built for international students and professionals on F-1, OPT, or STEM OPT who want to transition into data roles in the U.S. It is also for working professionals in non-technical fields who want to upskill into analytics without a computer science background, and for early-career analysts who know the basics but have never built a complete portfolio project or integrated AI into their workflow.You do not need a mathematics degree. You do not need prior programming experience. You need a laptop, a willingness to write code alongside the lectures, and a goal to land a data role.REQUIREMENTSNo prior Python experience is required. A computer with internet access and at least 8GB of RAM is sufficient. All software used in this course is free and open source. API usage in the AI modules requires a free account — setup is covered in the lecture.A NOTE ON CAREER OUTCOMESData Analyst, Business Intelligence Analyst, Analytics Engineer, and Data Scientist roles are among the most consistent H-1B sponsoring positions in the U.S. market. All qualify for STEM OPT extension under the relevant CIP codes. This course does not just build your technical skills — it builds the portfolio evidence that makes your application stand out to employers who sponsor visas.Every skill in this bootcamp was chosen because it appears in job descriptions, gets tested in interviews, and transfers directly to your first week on the job.Enroll now. Your portfolio project starts in the first lecture.

Microsoft Certified: Power BI Data Analyst Associate (PL-300

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Are you getting ready to take your Power BI certification exam? I know how you feel. Looking at all those topics can be scary. I made this practice test to help you study without the stress. We will go through the material together, step by step, so you feel ready on test day.When you practice with me, you are not just memorizing answers. You are learning how to solve problems the right way. This course is built to show you exactly how the exam questions look and how to pick the best answer every time.I have set this up to be as simple as possible. You can take these tests whenever you have a little bit of time. Whether you have ten minutes or an hour, you can jump in and practice. My goal is to make sure you walk into your exam feeling calm and prepared.We cover all the parts of the exam that you need to know for 2026. I explain the "why" behind every answer. This way, you learn the logic, not just the words. We are going to get you that certification together.What You Will LearnReal Practice Tests: You get to see questions that are just like the ones you will face on the real exam.Clear Explanations: For every question, I explain why the answer is right so you can learn quickly.Up-to-Date for 2026: All the questions are fresh and match what the exam tests this year.Study at Your Own Speed: You choose when to study. You can pause, review, and try again whenever you want.Test Your Knowledge: See which topics you know well and which ones you need to look at again.Feel More Confident: By taking these tests, you will stop worrying about the exam and start feeling ready.How Practice Exams Help You PassThe best way to pass is to practice what the real test feels like. When you take these exams, you get used to the way the questions are written. It stops the exam from being a surprise.If you get a question wrong, don't worry! That is part of learning. I explain the right answer so you can fix that gap in your knowledge. The more you practice, the more natural the answers will feel.Grow Your CareerHaving this certification on your resume shows that you know your stuff. It tells people you can work with data and build reports that help businesses grow. Many companies look for these skills when they hire new team members.Getting certified can help you get a better job or stand out in your current one. It shows you put in the time to learn the tools. We want to make sure you have the best chance to reach your goals.disclaimer,These are not actual exam questions. They are independently created practice materials, developed through extensive research and advanced digital content tools, designed to align with the latest 2026 exam objectives and blueprint standards.

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AI Agents: From Foundations to Enterprise Systems

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