Enroll Course: https://www.udemy.com/course/best-aws-sagemaker/
In the rapidly evolving tech landscape, Machine Learning (ML) and Deep Learning (DL) are no longer buzzwords but essential tools driving innovation across industries. For those looking to harness the power of cloud-based ML, Amazon Web Services (AWS) is a dominant force. Their fully managed service, SageMaker, empowers data scientists and AI practitioners to build, train, and deploy ML models efficiently. If you’re a beginner aiming to gain practical experience with SageMaker, the course “【한글자막】 초보자를 위한 AWS SageMaker 실습 6개 프로젝트 구축하기” (AWS SageMaker Practice for Beginners: Building 6 Projects with Korean Subtitles) is an excellent choice.
This comprehensive course is designed for aspiring data scientists and developers who want to solve real-world problems using AWS SageMaker. While prior knowledge of ML, Python programming, and AWS cloud is beneficial, the course breaks down complex topics into digestible modules. It’s ideal for anyone looking to build a portfolio, transform businesses with AI/ML, or gain hands-on experience in data science and AI.
The curriculum is robust, covering crucial areas like data engineering, feature engineering, selecting appropriate ML/DL algorithms, and deploying models. You’ll delve into Python libraries such as Pandas, NumPy, and Scikit-learn, and explore AWS services like Amazon S3. The course also provides a solid foundation in ML and DL concepts, including neural networks (ANN, CNN), activation functions, training strategies, gradient descent, and various evaluation metrics.
What truly sets this course apart is its project-driven approach. You’ll work through six practical projects, applying your knowledge to diverse scenarios:
* **Project 1:** Predicting employee salaries using a linear regression model.
* **Project 2:** Building a multi-linear regression model for healthcare premium prediction.
* **Project 3:** Forecasting store sales with XGBoost and optimizing hyperparameters.
* **Project 4:** Performing dimensionality reduction with PCA and building a cardiovascular disease prediction model.
* **Project 5:** Developing a traffic sign classification model using SageMaker and TensorFlow.
* **Project 6:** A deep dive into SageMaker Studio, AutoML, and model debugging.
The course is regularly updated, with recent additions including an AWS SageMaker Autopilot case study and bug fixes for code scripts. The instructors encourage active participation through the Q&A section, though it’s requested that questions be posed in English for a prompt response.
**Recommendation:**
For beginners eager to gain practical, hands-on experience with AWS SageMaker and build a strong foundation in ML/DL, this course is highly recommended. The project-based learning approach ensures you not only understand the concepts but can also apply them to real-world challenges, significantly boosting your portfolio and career prospects in the exciting field of AI and cloud computing.
Enroll Course: https://www.udemy.com/course/best-aws-sagemaker/