- Thread Starter
- #1
AWS Machine Learning: From Basics To Hands-On Projects
Master AWS Machine Learning with comprehensive lessons and hands-on projects to transform data into actionable insights.
What you'll learn
Introduction to AWS Machine Learning: Understand the fundamentals of AWS Machine Learning and its key features.
Data Sourcing and Preparation: Learn the lifecycle of AML, from data ingestion to model deployment.
Managing Data Quality and Variables: Address data quality issues, including handling invalid values.
Hands-On Data Insights: Engage in practical exercises to create and manage data sources.
Building and Evaluating ML Models: Develop and fine-tune machine learning models using AWS's advanced settings.
End-to-End ML Project Management: Master the creation, management, and evaluation of ML objects in AWS.
Requirements
Basic Knowledge of AWS Services: Familiarity with core AWS services like S3, EC2, and IAM will be beneficial.
Foundational Programming Skills: Basic knowledge of Python is recommended, as it will be used for scripting and model management.
Interest in Machine Learning: No prior experience in machine learning is required, but an enthusiasm for learning how to build ML models will enhance your experience.
Description
In the era of data-driven decision-making, mastering machine learning is a valuable skill. The AWS Machine Learning Mastery: From Basics to Hands-On Projects course is designed to take you from the fundamentals of AWS Machine Learning (AML) to practical applications. Whether you are new to the field or looking to deepen your knowledge, this course offers a structured and engaging approach to mastering AWS's machine learning services. Through step-by-step guidance, real-world examples, and hands-on exercises, you will gain the skills needed to implement powerful ML models using AWS.Section-wise Writeup:Section 1: IntroductionThis section lays the foundation by introducing you to AWS Machine Learning (AML). We begin with an overview of the platform, its capabilities, and how it integrates with other AWS services. You'll learn about the key features of AWS Machine Learning and how it simplifies the process of building, training, and deploying machine learning models. By the end of this section, you'll have a clear understanding of AML's role in modern data science.Section 2: DatasourceIn this section, we dive into the critical aspect of data sourcing, which forms the backbone of any machine learning project. We start with the Lifecycle of AML, exploring the journey from data preparation to model deployment. You'll learn how to connect to various data sources, including S3 buckets, databases, and on-premises systems. Additionally, you'll discover how to create robust data schemes within AML, setting the stage for effective model training. This section ensures you are equipped to handle the complexities of data integration in AWS.Section 3: ValueThis section focuses on the value aspect of machine learning models. We address how to manage invalid values in datasets and set up variable targets for accurate predictions. You'll gain insights into the different types of ML models available in AML and how to select the best fit for your project needs. We also cover managing machine learning objects, such as datasets, models, and batch predictions, providing a comprehensive understanding of AML's functionalities.Section 4: Datasource Hands-OnLearning by doing is crucial for mastering new skills, which is why this section emphasizes practical application. You'll engage in hands-on exercises, starting with creating data sources in AML. This includes a step-by-step walkthrough on setting up and managing data sources, followed by deeper dives into extracting insights from your datasets. By the end of this section, you'll be proficient in leveraging AWS's tools to analyze and interpret data, turning raw information into actionable insights.Section 5: ML Model Hands-OnThe final section brings everything together by guiding you through the process of building, evaluating, and deploying machine learning models. You'll explore real-world examples, create ML models, and learn how to fine-tune them using advanced settings. We also cover batch predictions, enabling you to automate the process of generating predictions for large datasets. The hands-on sessions culminate in a comprehensive overview of managing ML objects in AML, ensuring you are ready to implement these techniques in practical scenarios.Conclusion:By the end of the AWS Machine Learning Mastery: From Basics to Hands-On Projects course, you will have gained a robust understanding of AWS Machine Learning. You'll be proficient in sourcing, preparing, and analyzing data, as well as building and deploying machine learning models on AWS. This course is designed to provide you with practical skills that can be directly applied in real-world scenarios, making you a valuable asset in any data-driven organization. Whether you are looking to advance your career, transition into a new role, or simply expand your knowledge, this course provides the tools and confidence needed to succeed in the dynamic field of machine learning.
Overview
Section 1: Introduction
Lecture 1 Introduction to AWS Machine Learning (AML)
Section 2: Datasource
Lecture 2 Lifecycle of AML
Lecture 3 Connecting to Data Source in AML
Lecture 4 Creating Data Scheme in AML
Section 3: Value
Lecture 5 Invaild Value and Varible Target in AML
Lecture 6 ML Models in AML
Lecture 7 Manging ML Object in AML
Section 4: Datasource Handson
Lecture 8 Creating DataSource Handson
Lecture 9 Creating DataSource Handson Continues
Lecture 10 Example of Data Insight In AML
Lecture 11 More on Data Insight In AML
Section 5: ML Model Handson
Lecture 12 ML Model Example in Data Sources
Lecture 13 Creating ML Model Evaluating
Lecture 14 Advanced Setting In ML Model
Lecture 15 Creating ML Model for Batch Prediction
Lecture 16 Batch Prediction Result
Lecture 17 Overvies of ML Model Handson
Lecture 18 ML objects Handson in ML
Aspiring Data Scientists & Machine Learning Engineers: Individuals looking to break into the field of data science and machine learning, especially those interested in leveraging AWS's powerful ML services.,Developers & Software Engineers: Developers who wish to expand their skill set by integrating machine learning capabilities into their applications.,Data Analysts & BI Professionals: Analysts aiming to enhance their data insights using predictive modeling and machine learning.,AWS Enthusiasts & Cloud Practitioners: Individuals who are already familiar with AWS services and want to explore its machine learning capabilities.,Tech Managers & Project Leads: IT managers and project leads looking to understand the potential of AWS Machine Learning for strategic decision-making.,Students & Academics: University students, researchers, and educators who want to apply AWS ML tools in academic projects or research.
Say "Thank You"
rapidgator.net:
k2s.cc:
Published 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.14 GB | Duration: 2h 21m
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.14 GB | Duration: 2h 21m
Master AWS Machine Learning with comprehensive lessons and hands-on projects to transform data into actionable insights.
What you'll learn
Introduction to AWS Machine Learning: Understand the fundamentals of AWS Machine Learning and its key features.
Data Sourcing and Preparation: Learn the lifecycle of AML, from data ingestion to model deployment.
Managing Data Quality and Variables: Address data quality issues, including handling invalid values.
Hands-On Data Insights: Engage in practical exercises to create and manage data sources.
Building and Evaluating ML Models: Develop and fine-tune machine learning models using AWS's advanced settings.
End-to-End ML Project Management: Master the creation, management, and evaluation of ML objects in AWS.
Requirements
Basic Knowledge of AWS Services: Familiarity with core AWS services like S3, EC2, and IAM will be beneficial.
Foundational Programming Skills: Basic knowledge of Python is recommended, as it will be used for scripting and model management.
Interest in Machine Learning: No prior experience in machine learning is required, but an enthusiasm for learning how to build ML models will enhance your experience.
Description
In the era of data-driven decision-making, mastering machine learning is a valuable skill. The AWS Machine Learning Mastery: From Basics to Hands-On Projects course is designed to take you from the fundamentals of AWS Machine Learning (AML) to practical applications. Whether you are new to the field or looking to deepen your knowledge, this course offers a structured and engaging approach to mastering AWS's machine learning services. Through step-by-step guidance, real-world examples, and hands-on exercises, you will gain the skills needed to implement powerful ML models using AWS.Section-wise Writeup:Section 1: IntroductionThis section lays the foundation by introducing you to AWS Machine Learning (AML). We begin with an overview of the platform, its capabilities, and how it integrates with other AWS services. You'll learn about the key features of AWS Machine Learning and how it simplifies the process of building, training, and deploying machine learning models. By the end of this section, you'll have a clear understanding of AML's role in modern data science.Section 2: DatasourceIn this section, we dive into the critical aspect of data sourcing, which forms the backbone of any machine learning project. We start with the Lifecycle of AML, exploring the journey from data preparation to model deployment. You'll learn how to connect to various data sources, including S3 buckets, databases, and on-premises systems. Additionally, you'll discover how to create robust data schemes within AML, setting the stage for effective model training. This section ensures you are equipped to handle the complexities of data integration in AWS.Section 3: ValueThis section focuses on the value aspect of machine learning models. We address how to manage invalid values in datasets and set up variable targets for accurate predictions. You'll gain insights into the different types of ML models available in AML and how to select the best fit for your project needs. We also cover managing machine learning objects, such as datasets, models, and batch predictions, providing a comprehensive understanding of AML's functionalities.Section 4: Datasource Hands-OnLearning by doing is crucial for mastering new skills, which is why this section emphasizes practical application. You'll engage in hands-on exercises, starting with creating data sources in AML. This includes a step-by-step walkthrough on setting up and managing data sources, followed by deeper dives into extracting insights from your datasets. By the end of this section, you'll be proficient in leveraging AWS's tools to analyze and interpret data, turning raw information into actionable insights.Section 5: ML Model Hands-OnThe final section brings everything together by guiding you through the process of building, evaluating, and deploying machine learning models. You'll explore real-world examples, create ML models, and learn how to fine-tune them using advanced settings. We also cover batch predictions, enabling you to automate the process of generating predictions for large datasets. The hands-on sessions culminate in a comprehensive overview of managing ML objects in AML, ensuring you are ready to implement these techniques in practical scenarios.Conclusion:By the end of the AWS Machine Learning Mastery: From Basics to Hands-On Projects course, you will have gained a robust understanding of AWS Machine Learning. You'll be proficient in sourcing, preparing, and analyzing data, as well as building and deploying machine learning models on AWS. This course is designed to provide you with practical skills that can be directly applied in real-world scenarios, making you a valuable asset in any data-driven organization. Whether you are looking to advance your career, transition into a new role, or simply expand your knowledge, this course provides the tools and confidence needed to succeed in the dynamic field of machine learning.
Overview
Section 1: Introduction
Lecture 1 Introduction to AWS Machine Learning (AML)
Section 2: Datasource
Lecture 2 Lifecycle of AML
Lecture 3 Connecting to Data Source in AML
Lecture 4 Creating Data Scheme in AML
Section 3: Value
Lecture 5 Invaild Value and Varible Target in AML
Lecture 6 ML Models in AML
Lecture 7 Manging ML Object in AML
Section 4: Datasource Handson
Lecture 8 Creating DataSource Handson
Lecture 9 Creating DataSource Handson Continues
Lecture 10 Example of Data Insight In AML
Lecture 11 More on Data Insight In AML
Section 5: ML Model Handson
Lecture 12 ML Model Example in Data Sources
Lecture 13 Creating ML Model Evaluating
Lecture 14 Advanced Setting In ML Model
Lecture 15 Creating ML Model for Batch Prediction
Lecture 16 Batch Prediction Result
Lecture 17 Overvies of ML Model Handson
Lecture 18 ML objects Handson in ML
Aspiring Data Scientists & Machine Learning Engineers: Individuals looking to break into the field of data science and machine learning, especially those interested in leveraging AWS's powerful ML services.,Developers & Software Engineers: Developers who wish to expand their skill set by integrating machine learning capabilities into their applications.,Data Analysts & BI Professionals: Analysts aiming to enhance their data insights using predictive modeling and machine learning.,AWS Enthusiasts & Cloud Practitioners: Individuals who are already familiar with AWS services and want to explore its machine learning capabilities.,Tech Managers & Project Leads: IT managers and project leads looking to understand the potential of AWS Machine Learning for strategic decision-making.,Students & Academics: University students, researchers, and educators who want to apply AWS ML tools in academic projects or research.
Screenshots
Say "Thank You"
rapidgator.net:
You must reply in thread to view hidden text.
k2s.cc:
You must reply in thread to view hidden text.
