Coursera - Microsoft AI & ML Engineering Professional Certificate

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Free Download Coursera - Microsoft AI & ML Engineering Professional Certificate
Released 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + subtitle | Duration: 71 Lessons ( 6h 26m ) | Size: 11 GB
Prepare for a Career in AI & ML Engineering. Build, deploy, and innovate with advanced techniques and real-world projects. Intermediate programming knowledge of Python required.

What you'll learn
Design and Implement AI & ML Infrastructure: Develop environments, including data pipelines, model development frameworks, and deployment platforms.
Master AI & ML Algorithms and Techniques: Apply supervised, unsupervised, reinforcement learning, and deep learning methods to solve challenges.
Develop Intelligent Troubleshooting Agents: Create AI-powered agents capable of diagnosing and resolving issues autonomously.
Leverage Microsoft Azure for AI & ML Workflows: Set up, manage, and optimize the entire AI & ML lifecycle using Azure.
Skills you'll gain
Model Optimization
Real-World Problem-Solving
Ethical AI Practices
Data Preprocessing
Azure Deployment
This comprehensive program is designed to prepare you for the dynamic field of artificial intelligence and machine learning. Across five courses, you gain a deep understanding of AI & ML fundamentals, practical skills, and hands-on experience.
Starting with the design of scalable AI & ML infrastructure, you learn to build robust environments. You then explore core algorithms and techniques. The program also delves into AI agent development, teaching you how to create intelligent systems capable of autonomous troubleshooting using natural language processing (NLP) and decision-making strategies.
A key focus is on leveraging cloud-based AI & ML services, specifically through Microsoft Azure, where you manage end-to-end workflows. The program concludes with advanced concepts, ethical considerations, and a capstone project.
Upon completion, you will have the expertise to design, deploy, and optimize AI & ML solutions, making you a valuable asset in the tech industry. This program is ideal for those seeking to master AI & ML techniques, build scalable solutions, and apply your knowledge to real-world problems.
To be successful, you should have intermediate programming knowledge of Python, plus basic knowledge of AI and ML capabilities, and newer capabilities through generative AI (GenAI) and pretrained large language models (LLM). Familiarity with statistics is also recommended. You will need a license to Microsoft Azure (or a free trial version) and appropriate hardware.
Applied Learning Project
The capstone project is the culmination of all the skills you have acquired throughout the program. It simulates real-world challenges faced by AI & ML professionals, giving you practical experience with the entire machine learning lifecycle-from problem identification to deployment and evaluation. This hands-on project allows you to integrate theoretical knowledge into a practical, comprehensive solution.
You might develop a fraud detection system for finance, an intelligent chatbot for customer support, or a predictive maintenance model for manufacturing. By focusing on real-world challenges, you gain insight into the complexities of AI & ML deployment, such as adapting models to new data or ensuring solutions can scale effectively.
This project allows you to experience firsthand the process of turning an idea into a fully functional AI & ML solution, preparing you for advanced roles in AI & ML engineering.
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8.8 GB | 00:08:54 | mp4 | 1920X1080 | 16:9
Genre:eLearning |Language:English​

Files Included :

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02 introduction-to-the-ai-ml-algorithms-and-techniques-course.mp4 (197.09 MB)
03 the-importance-of-algorithms-and-techniques-in-your-work.mp4 (316.07 MB)
01 what-is-supervised-learning.mp4 (256.78 MB)
08 compare-implementation-techniques-using-python.mp4 (19.33 MB)
02 use-case-demonstration-of-evaluation-metrics.mp4 (17.77 MB)
05 use-case-demonstration-of-cross-validation-and-multiple-metrics-in-ml.mp4 (20.25 MB)
01 summary-supervised-learning.mp4 (174.79 MB)
01 overview-of-unsupervised-learning.mp4 (114.38 MB)
06 how-to-implement-and-visualize-clustering.mp4 (17.27 MB)
04 use-case-demonstration-of-dimensionality-reduction.mp4 (21.66 MB)
01 overview-of-reinforcement-learning.mp4 (170.6 MB)
01 overview-of-neural-networks.mp4 (168.31 MB)
02 use-case-demonstration-of-fnns-cnns-and-rnns.mp4 (22.31 MB)
01 overview-of-ai-ml-engineering-approaches.mp4 (263.59 MB)
01 the-importance-of-collaboration-in-ai-ml-professions.mp4 (134.7 MB)
01 summary-ai-ml-engineering-and-working-with-models.mp4 (295.24 MB)
03 thank-you-and-congratulations.mp4 (59.84 MB)
02 introduction-to-the-foundations-of-ai-ml-infrastructure.mp4 (128.78 MB)
03 a-day-in-the-life-of-an-ai-ml-engineer.mp4 (133.46 MB)
07 getting-started-with-jupyter-notebooks-in-azure-machine-learning-studio.mp4 (28.61 MB)
01 introduction-to-ai-ml-infrastructure.mp4 (280.74 MB)
02 data-sources-and-pipelines-frameworks-and-platforms.mp4 (243.25 MB)
01 introduction-to-data-sources-and-pipelines.mp4 (208.54 MB)
04 examples-of-data-sources-and-pipelines.mp4 (262.98 MB)
01 introduction-to-deployment-platforms.mp4 (241.71 MB)
02 importance-of-deployment-platforms.mp4 (244 MB)
03 features-and-requirements-for-effective-deployment.mp4 (215.12 MB)
01 summary-ai-ml-applications.mp4 (171.79 MB)
05 industry-exemplar-model-deployment.mp4 (165.4 MB)
01 overview-of-data-sources.mp4 (252.47 MB)
02 methods-for-acquiring-data.mp4 (229.66 MB)
01 importance-of-data-cleaning-and-preprocessing.mp4 (148.92 MB)
01 introduction-to-rag.mp4 (172.16 MB)
03 best-practices-for-maintaining-efficient-data-sources-for-rag.mp4 (195.72 MB)
01 summary-data-management-in-ai-ml.mp4 (205.38 MB)
04 hear-from-an-expert-industry-exemplar.mp4 (183.26 MB)
03 applicability-of-pretrained-llms.mp4 (201.79 MB)
01 summary-selecting-a-framework.mp4 (193.45 MB)
02 introduction-to-microsoft-azure.mp4 (57.11 MB)
01 preparing-models-for-deployment.mp4 (154.16 MB)
01 importance-of-version-control.mp4 (171.2 MB)
03 ensuring-reproducibility.mp4 (178.86 MB)
01 summary-platform-deployment.mp4 (326.47 MB)
01 overview-of-the-ai-ml-engineers-responsibilities.mp4 (240.07 MB)
03 typical-tasks-and-projects.mp4 (269.94 MB)
02 balancing-model-development-deployment-and-maintenance.mp4 (273.83 MB)
01 summary-ai-ml-concepts-in-practice.mp4 (331.58 MB)
01 course-summary.mp4 (267.11 MB)
03 example-pitching-to-the-c-suite.mp4 (284.93 MB)
04 congratulations-on-completing-the-course.mp4 (75.81 MB)

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