The Practical Data Science with Amazon SageMaker course is a comprehensive program designed to teach learners the fundamentals and advanced techniques of machine learning (ML) with a focus on Amazon SageMaker. This fully managed service enables developers and data scientists to build, train, and deploy ML models at scale.

The course covers key areas such as:

• Machine Learning Fundamentals: Introduction to different types of ML (supervised, unsupervised, and reinforcement learning), key ML job roles, and the ML pipeline
• Data Preparation: Preparing datasets, launching Jupyter Notebooks, and engaging in data analysis and visualization techniques
• Model Training and Evaluation: Hands-on exercises for training models, evaluating performance, and optimizing with hyperparameter tuning
• Deployment and Production Readiness: Best practices for deploying models, A/B testing, auto-scaling, and ensuring production readiness
• Cost and Error Analysis: Understanding the cost of errors in ML models and managing trade-offs between accuracy and business objectives

By the end of this course, learners will be able to:

• Understand the types of machine learning and how they apply to real-world problems
• Prepare and visualize datasets, applying data science techniques using SageMaker
• Train and evaluate machine learning models, utilizing SageMaker’s built-in algorithms
• Optimize models with hyperparameter tuning for better performance
• Deploy models into production with strategies such as A/B testing and auto-scaling
• Analyze the cost and impact of errors in machine learning models

This course is ideal for those looking to apply machine learning in real-world scenarios and leverage SageMaker to streamline the model development and deployment process.

Course Prerequisites

To successfully engage with the Practical Data Science with Amazon SageMaker course, learners should meet the following prerequisites:

• Basic understanding of machine learning concepts (supervised, unsupervised, and reinforcement learning)
• Foundational knowledge of Python programming
• Familiarity with Python libraries like pandas and NumPy for data manipulation
• Exposure to data visualization tools/libraries such as matplotlib or seaborn
• Understanding of the data science workflow from data prep to model evaluation
• Awareness of AWS cloud services is helpful, but not required
• No prior experience with Amazon SageMaker is necessary—this will be introduced during the course

These prerequisites help ensure learners are equipped to follow the hands-on activities and maximize their learning outcomes.

Target Audience

The Practical Data Science with Amazon SageMaker course is ideal for professionals aiming to implement machine learning models in cloud-based environments using AWS:

• Data Scientists and Analysts
• Machine Learning Engineers
• Software Developers with ML interests
• IT Professionals transitioning to data science
• Business Analysts interested in ML applications
• Technical Project Managers leading ML initiatives
• Cloud Engineers and AWS Architects
• Professionals exploring customer churn analytics
• Data Engineers managing ML datasets
• AI/ML Consultants providing client ML solutions
• Students pursuing data science and ML careers

Why Choose us

⭢ Live Online Training (Duration : 8 Hours)

⭢ Guaranteed to run classes

⭢ Experienced & certified trainers

⭢ Query Handling session

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Learning Objectives

After completing the Practical Data Science with Amazon SageMaker course, learners will be able to:

• Understand the different types of machine learning (supervised, unsupervised, and reinforcement learning) and their real-world applications.
• Recognize various job roles within the machine learning field and the key responsibilities associated with them.
• Gain insights into the steps involved in the machine learning pipeline, from data preparation to model deployment.
• Define training and test datasets, and get introduced to Amazon SageMaker’s capabilities and environment.
• Formulate real-world business problems (e.g., customer churn) into machine learning tasks, and prepare relevant datasets for analysis.
• Perform data analysis and visualization, including cleaning data and exploring the relationships between features.
• Master the process of training and evaluating machine learning models in SageMaker using algorithms like XGBoost, and understand how to set hyperparameters.
• Learn to automate hyperparameter tuning for optimal model performance using SageMaker.
• Deploy machine learning models into production, following best practices such as A/B testing and auto-scaling to manage varying loads.
• Understand the relative cost of errors in machine learning models and manage trade-offs between accuracy, precision, and recall.

These objectives will equip learners with the necessary skills to effectively build, train, tune, and deploy machine learning models using AWS SageMaker.