Free Coupon Code Build and End to End ML Projects on AWS SageMaker 100% OFF

Udemy coupon code for Build and End to End ML Projects on AWS SageMaker course taught by Akhil Vydyula, which has 8,898 students and is rated 3.4 out of 29 votes. This course is about 1 hour(s) in English and was updated on September 02, 2025. You can use this Udemy course with a free certificate and find the coupon at the bottom of this page.

Learn to Build, Train & Deploy Machine Learning Models on AWS

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:

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. Hands-On Projects: Throughout the course, you will work on practical projects and exercises, applying what you've learned to real-world scenarios.

9. 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