Microsoft AI & ML Course 1: Foundations of AI and Machine Learning Complete Tutorial скачать в хорошем качестве

Microsoft AI & ML Course 1: Foundations of AI and Machine Learning Complete Tutorial

Microsoft AI & ML Engineering Professional Certificate Course 1 - Foundations of AI and Machine Learning Complete Tutorial Master the fundamental components of artificial intelligence and machine learning (AI & ML) infrastructure with Microsoft's comprehensive professional certificate program. Learn AI/ML infrastructure, data security, application frameworks, TensorFlow, scalability, data pipelines, data cleansing, machine learning, infrastructure architecture, data processing, artificial intelligence, data management, application deployment, MLOps (Machine Learning Operations), applied machine learning, and PyTorch. What You'll Learn: Analyze, describe, and critically discuss the critical components of AI & ML infrastructure and their interrelationships Analyze, describe, and critically discuss efficient data pipelines for AI & ML workflows Analyze and evaluate model development frameworks for various AI & ML applications including TensorFlow and PyTorch Prepare AI & ML models for deployment in production environments with MLOps best practices Prerequisites: Intermediate programming knowledge of Python, basic knowledge of AI and ML capabilities, familiarity with generative AI (GenAI) and pretrained large language models (LLM). Statistics knowledge recommended. Skills: Artificial Intelligence and Machine Learning (AI/ML), Data Security, Application Frameworks, TensorFlow, Scalability, Data Pipelines, Data Cleansing, Machine Learning, Infrastructure Architecture, Data Processing, Artificial Intelligence, Data Management, Application Deployment, MLOps (Machine Learning Operations), Applied Machine Learning, PyTorch (Machine Learning Library). Course 1 of 5 - Microsoft AI & ML Engineering Professional Certificate Complete Certificate Program: Watch the full 5-course specialization here (Playlist):    • Microsoft AI & ML Engineering Professional...   Access Resources: https://drive.google.com/file/d/1kHHb... Week 1- INTRODUCTION TO AI/ML ENGINEERING 00:00:00 - Introduction to the AIML engineering advanced professional certificate program 00:03:35 - Introduction to the foundations of AIML infrastructure 00:07:37 - A day in the life of an AIML engineer 00:11:35 - Getting started with Jupyter Notebooks in Azure Machine Learning Studio 00:17:52 - Introduction to AIML infrastructure Week 2- DATA SOURCES & PIPELINES 00:23:34 - Data sources and pipelines, frameworks, and platforms 00:28:57 - Introduction to data sources and pipelines 00:33:41 - Examples of data sources and pipelines 00:38:23 - Introduction to model development approaches and frameworks 00:43:14 - Introduction to deployment platforms 00:48:29 - Importance of deployment platforms 00:53:49 - Features and requirements for effective deployment 00:59:48 - Summary AIML applications 01:03:37 - Industry exemplar Model deployment Week 3- FRAMEWORK SELECTION & IMPLEMENTATION 01:07:16 - Key features to consider in deployment platforms 01:13:40 - Introduction to Microsoft Azure 01:21:34 - Preparing models for deployment 01:26:10 - Additional steps for preparing a model for production deployment 01:32:36 - Importance of version control 01:37:08 - Ensuring reproducibility 01:41:40 - Summary Platform deployment 01:49:40 - Key features and use cases for frameworks and models 01:55:45 - Applicability of pretrained LLMs 02:01:10 - Guide to implementing a simple model in TensorFlow 02:07:32 - Guide to implementing a simple model in PyTorch 02:07:49 - Criteria for selecting frameworks based on project needs 02:13:48 - Summary Selecting a framework 02:19:04 - Hear from an expert Industry exemplar Week 4- DATA MANAGEMENT & RAG 02:24:36 - Overview of data sources 02:30:03 - Methods for acquiring data 02:36:30 - Importance of data cleaning and preprocessing 02:42:15 - Hear from an expert The value of consistent taxonomy 02:45:10 - Introduction to RAG 02:50:17 - Best practices for maintaining efficient data sources for RAG 02:55:39 - Hear from an expert Security considerations when working with data 03:02:00 - Summary Data management in AIML 03:08:21 - Hear from an expert Industry exemplar Week 5- AIML ENGINEER RESPONSIBILITIES & COURSE WRAP-UP 03:12:48 - Overview of the AIML engineer's responsibilities 03:18:55 - Typical Tasks and Projects 03:26:18 - Hear from an expert Data quality in the corporate setting 03:30:32 - Balancing model development, deployment, and maintenance 03:38:23 - Hear from an expert Understanding the problem before building AI solutions 03:42:54 - Summary AIML concepts in practice 03:51:48 - Course summary 03:58:50 - Congratulations on completing the course!