Mastering AWS Sage Maker: From Fundamentals to Advance

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Akhil Vydyula

54:02

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1. Foundations of AWS SageMaker Module 1 - Introduction and Basics

1. Foundations of AWS SageMaker Module 1 - Introduction and Basics.mp4

03:57

2. SageMaker Data Preparation Essentials

1. SageMaker Data Preparation Essentials.mp4

05:14

3. Advanced Model Training with SageMaker Distributed Training and Debugging

1. Advanced Model Training with SageMaker Distributed Training and Debugging.mp4

03:20

4. Effective Model Deployment with Amazon SageMaker Strategies for Success

1. Effective Model Deployment with Amazon SageMaker Strategies for Success.mp4

15:40

5. SageMaker Excellence Best Practices and Case Studies in Machine Learning Operat

1. SageMaker Excellence Best Practices and Case Studies in Machine Learning Operat.mp4

02:16

6. AWS SageMaker Mastery From Data to User Interface - Unleashing Functional Scena

1. AWS SageMaker Mastery From Data to User Interface - Unleashing Functional Scena.mp4

09:10

7. Leveraging AWS SageMaker Hands-On Machine Learning with the Iris Dataset

1. Leveraging AWS SageMaker Hands-On Machine Learning with the Iris Dataset.mp4

01:34

8. Leveraging AWS SageMaker Building Machine Learning Models for Banking Data

1. Leveraging AWS SageMaker Building Machine Learning Models for Banking Data.mp4

12:51

Description

Unlock the Power of AWS SageMaker: Mastering Fundamentals and Advancing Your Skills

What You'll Learn?

Introduction to Amazon SageMaker: Explore the features and capabilities of SageMaker as a machine learning platform.
Introduction to Machine Learning: Understand the basics of machine learning, including supervised and unsupervised learning, algorithms, and models.
Data Visualization: Explore techniques for visualizing and understanding your data using tools and libraries available in SageMaker.
Model Training: Understand how to train machine learning models using SageMaker's infrastructure, including distributed training and hyperparameter tuning.

Who is this for?

Data Scientists: Data scientists looking to expand their machine learning and data modeling skills with a focus on using AWS SageMaker for developing, training, and deploying machine learning models.

Machine Learning Engineers: Machine learning engineers interested in mastering the tools and techniques within SageMaker to build, test, and deploy machine learning models at scale.

Software Developers: Developers who want to integrate machine learning into their applications and are interested in using SageMaker to streamline model development and deployment.

Business Analysts: Business analysts looking to gain insights from data using machine learning techniques and SageMaker.

AWS Enthusiasts: Those who are already familiar with AWS services and want to explore SageMaker's capabilities.

What You Need to Know?

Basic Cloud Computing Knowledge: It's essential to have a fundamental understanding of cloud computing concepts and services, as AWS SageMaker is a cloud-based machine learning platform.

Machine Learning Fundamentals: A solid understanding of machine learning concepts and algorithms is usually necessary. You should be familiar with supervised and unsupervised learning, regression, classification, and model evaluation.

Jupyter Notebooks: Many SageMaker courses use Jupyter notebooks for practical exercises. Familiarity with Jupyter notebooks is helpful.

More details

Description
Course Description:
Unlock the full potential of AWS SageMaker and become a machine learning and data science expert with our comprehensive "Mastering AWS SageMaker" course. Whether you are a beginner looking to explore the world of machine learning or a seasoned professional seeking to enhance your skills, this course is your key to mastering the AWS SageMaker platform.
Course Highlights:
Fundamentals of AWS SageMaker: Begin your journey by understanding the core concepts of AWS SageMaker, cloud computing, and machine learning. You'll gain insights into the key components of SageMaker and how they fit into the machine learning workflow.
Data Preprocessing and Feature Engineering: Learn how to prepare and preprocess data for machine learning, an essential step in building robust models. Explore feature engineering techniques to extract meaningful insights from your data.
Model Building and Training: Dive into the heart of machine learning by creating, training, and fine-tuning models on SageMaker. Understand various algorithms, optimization strategies, and hyperparameter tuning for better model performance.
Deploying Models: Discover how to deploy your machine learning models into production with SageMaker. You'll explore best practices for deploying models at scale, ensuring high availability, and achieving optimal performance.
Automated Machine Learning (AutoML): Uncover the power of AutoML with SageMaker, allowing you to automate many aspects of the machine learning process, saving you time and effort in model development.
MLOps and Model Monitoring: Learn how to implement MLOps best practices and set up automated model monitoring to ensure your deployed models remain accurate and reliable.
Advanced Topics: Delve into advanced topics such as natural language processing (NLP), computer vision, and reinforcement learning on AWS SageMaker. Explore real-world use cases and applications.
Hands-On Projects: Throughout the course, you will work on practical projects and exercises, applying what you've learned to real-world scenarios.
Certification Preparation: If you're looking to earn AWS certification in machine learning, this course provides a strong foundation to help you succeed in your certification exam.
Who Should Enroll:
Data scientists and analysts
Software developers
Machine learning engineers
Data engineers
IT professionals
Anyone interested in mastering AWS SageMaker and machine learning
Who this course is for:
Data Scientists: Data scientists looking to expand their machine learning and data modeling skills with a focus on using AWS SageMaker for developing, training, and deploying machine learning models.
Machine Learning Engineers: Machine learning engineers interested in mastering the tools and techniques within SageMaker to build, test, and deploy machine learning models at scale.
Software Developers: Developers who want to integrate machine learning into their applications and are interested in using SageMaker to streamline model development and deployment.
Business Analysts: Business analysts looking to gain insights from data using machine learning techniques and SageMaker.
AWS Enthusiasts: Those who are already familiar with AWS services and want to explore SageMaker's capabilities.

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Amazon AWS

Akhil Vydyula

Instructor's Courses

Hi There!My Name is Akhil Vydyula, I am a Data Scientist I was previously worked on BFSI data analysis and modelling skills to oversee the full-life cycle of development and execution. He possess strong.ability to data wrangling, feature engineering, algorithm development, model training and implementation.SKILLS AND COMPETENCIESExpert knowledge and experience with C/C++/python Programming and SQL.Should be able to learn and Implement new technologies quickly and effectively.Excellent Mathematical Skills, Problem Solving & Logical Skills.Actively Participating in hackathons in various platforms and writing blogs in medium.TECHNICAL SKILLSMachine Learning, Natural Language Processing(NLP),Computer Vision,Regression, Multi LabelClassification.Transfer Learning, Transformers, Ensembles, Stacking Classifiers.AutoML, SQL, Python, Keras, Pandas, NumPy, Seaborn,Matplotlib,Clustering,Recommendation Systems,Time Series Analysis.

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