Real-World AWS ML Certification Labs: 2022 Edition
Hands-On Labs: MLS-CO1: AWS Certified Machine Learning Specialty- Based On Real World Case Studies: First Edition – 2022 is a comprehensive and practical guide for individuals seeking to become AWS Certified Machine Learning Specialists. This first edition, released in 2022, offers real-world case studies to provide hands-on experience and practical knowledge in the field of machine learning.
This book covers a wide range of topics, including data preparation, model building, evaluation, deployment, and optimization. It equips readers with the necessary skills and expertise to design, implement, and maintain machine learning solutions using AWS services.
The content of this book is based on actual case studies, ensuring that readers gain a deep understanding of the challenges and best practices encountered in real-world scenarios. The hands-on labs provided throughout the book offer a practical learning experience, allowing readers to apply their knowledge in a simulated environment.
Whether you are a beginner or an experienced professional in the field of machine learning, this book is a valuable resource for preparing for the MLS-CO1 exam and advancing your career in AWS machine learning. Get ready to dive into the exciting world of machine learning with this comprehensive guide.
Hands-On Labs: MLS-CO1: AWS Certified Machine Learning Specialty – Based On Real World Case Studies: First Edition – 2022
Hands-On Labs is thrilled to introduce the first edition of their MLS-CO1: AWS Certified Machine Learning Specialty course, which is based on real-world case studies. This comprehensive program is designed for individuals who are looking to enhance their skills and gain a competitive edge in the field of machine learning.
Real-world Case Studies: One of the key features of this course is its focus on real-world case studies. Rather than just learning theoretical concepts, students will have the opportunity to work on practical projects that simulate real-life scenarios. This hands-on approach enables learners to develop a deep understanding of how machine learning is applied in various industries and settings.
The course covers a wide range of topics, including data preprocessing, feature engineering, model evaluation, and deployment. Each topic is explored through a series of case studies that highlight the challenges and solutions faced by professionals in the field. By working on these case studies, students will not only gain technical skills but also learn how to think critically and solve complex problems.
Comprehensive Preparation for AWS Certified Machine Learning Specialty Exam: In addition to providing practical experience, this course is specifically designed to help students prepare for the AWS Certified Machine Learning Specialty exam. The curriculum aligns with the exam objectives and covers all the necessary topics in detail.
Students will have access to a range of learning resources, including video lectures, hands-on labs, and practice exams. These resources are designed to reinforce key concepts and provide ample practice opportunities. By the end of the course, students will be well-equipped to tackle the exam with confidence.
Overall, the MLS-CO1: AWS Certified Machine Learning Specialty course by Hands-On Labs offers a unique learning experience that combines real-world case studies with comprehensive exam preparation. Whether you are a beginner in the field or an experienced professional looking to upskill, this course is a valuable resource for advancing your machine learning career.
MLS-CO1: AWS Certified Machine Learning Specialty- Real World Case Studies – 2022 Edition
ASIN: B0B9NPW4QD
Publication date: August 14, 2022
Language: English
File size: 312435 KB
Text-to-Speech: Enabled
Screen Reader: Supported
Enhanced typesetting: Enabled
X-Ray: Not Enabled
Word Wise: Enabled
Sticky notes: On Kindle Scribe
Print length: 0 pages
Hands-On Labs: MLS-CO1: AWS Certified Machine Learning Specialty- Based On Real World Case Studies: First Edition – 2022
- Lab 1: Building a Recommendation System using AWS SageMaker
- Lab 2: Deploying a Sentiment Analysis Model with AWS Lambda and API Gateway
- Lab 3: Creating a Fraud Detection System using AWS Glue and Amazon S3
- Lab 4: Implementing a Chatbot with AWS Lex and AWS Lambda
- Lab 5: Training and Deploying a Deep Learning Model with AWS DeepRacer
- Lab 6: Building a Personalized News Recommendation System using AWS Personalize
- Lab 7: Designing a Predictive Maintenance Solution with AWS IoT and AWS Machine Learning
- Lab 8: Analyzing Customer Churn using AWS Redshift and Amazon QuickSight
- Lab 9: Building a Voice Recognition System with AWS Transcribe and AWS Lambda
- Lab 10: Implementing Anomaly Detection with AWS Kinesis and AWS Elasticsearch Service
