Practical Data Science with Amazon SageMaker Course in Ethiopia
Master ML with Amazon SageMaker
Get real-world insights from certified instructors
Apply machine learning to tackle real business challenges
Gain in-demand ML skills and boost your job prospects
Learn at your own pace with flexible, instructor-led sessions
Master the full ML workflow and its practical applications
Convenient and hassle-free payment plans
4.7/55884 EnrolledEnrolled Learners
4.7/55884 EnrolledEnrolled Learners
Overview
What our training includes:
- Learn data preparation techniques using SageMaker Data Wrangler
- Train models with powerful algorithms like XGBoost on SageMaker
- Evaluate and optimise models with hyperparameter tuning using SageMaker
- Deploy models to real-time endpoints with Amazon SageMaker
- Master MLOps practices for automating and monitoring model deployment
- Hands-on experience with no-code ML using Amazon SageMaker Canvas
Upcoming sessions
Curriculum
1
Benefits of machine learning (ML)
2
Types of ML approaches
3
Framing the business problem
4
Prediction quality
5
Processes, roles, and responsibilities for ML projects
1
Data analysis and preparation
2
Data preparation tools
3
Demonstration: Review Amazon SageMaker Studio and Notebooks
4
Hands-On Lab: Data Preparation with SageMaker Data Wrangler
1
Steps to train a model
2
Choose an algorithm
3
Train the model in Amazon SageMaker
4
Hands-On Lab: Training a Model with Amazon SageMaker
5
Amazon CodeWhisperer
6
Demonstration: Amazon CodeWhisperer in SageMaker Studio Notebooks
1
Model evaluation
2
Model tuning and hyperparameter optimization
3
Hands-On Lab: Model Tuning and Hyperparameter
4
Optimization with Amazon SageMaker
1
Model deployment
2
Hands-On Lab: Deploy a Model to a Real-Time Endpoint
3
and Generate a Prediction
1
Responsible ML
2
ML team and MLOps
3
Automation
4
Monitoring
5
Updating models (model testing and deployment)
1
Different tools for different skills and business needs
2
No-code ML with Amazon SageMaker Canvas
3
Demonstration: Overview of Amazon SageMaker Canvas
4
Amazon SageMaker Studio Lab
5
Demonstration: Overview of SageMaker Studio Lab
6
(Optional) Hands-On Lab: Integrating a Web Application
7
with an Amazon SageMaker Model Endpoint
Meet your Trainer
Our Trainer
We take immense pride in our skilled instructors and trainers who teach the Finance for Non-Finance course at Learns Point. Our trainers are globally recognized for their expertise and experience in various aspects of financial fields. Many of our instructors have worked in companies around the globe and have a lot of practical experience to share with the students. We at Learners Point adopt a data-driven research approach to learning and teaching so the experience is highly customizable and thoroughly engaging for learners from all walks of life. The sessions are classroom-based and led by an instructor. For those who seek more flexibility, we also offer high quality live and interactive sessions online.
We at Learners Point believe in encouraging our students to embark upon a journey of lifelong learning and self-development, with the aid of our comprehensive and distinctive courses tailored to current market trends. The manifestation of our career-oriented approach is what we assure through a pleasant professional enriched environment with cutting-edge technology, and an outstanding while highly acknowledged training staff that uses up-to-date methodologies and quality course material. With our aim to mold professionals to be future leaders, our industry expert trainers provide the best in town mentorship to our students while endowing them with the thirst for knowledge and inspiring them to strive for professional and human excellence.
Learning Outcomes
Upon finishing the training, you will:
1
Master data preparation using SageMaker Data Wrangler for efficient data processing
2
Train models with advanced algorithms like XGBoost and optimise with hyperparameter tuning
3
Deploy machine learning models to real-time endpoints using Amazon SageMaker for predictions
4
Apply MLOps practices for automating, monitoring, and managing deployed models
5
Gain hands-on experience with no-code machine learning through SageMaker Canvas
KHDA Certificate
Earn a KHDA attested Course Certificate. The Knowledge and Human Development Authority (KHDA) is the educational quality assurance and regulatory authority of the Government of Dubai, United Arab Emirates.
Learners Point Certificate
Earn a Course Completion Certificate, an official Learners Point credential that confirms that you have successfully completed a course with us.
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Frequently asked questions
This program takes you end-to-end through the machine learning lifecycle using core AWS services such as SageMaker Studio, Data Wrangler, and SageMaker Canvas. You will learn about ML fundamentals, model tuning and deployment. It includes instructor-led demonstrations and lab sessions, and culminates in a completion credential that validates your ability to implement ML solutions on AWS and strengthens career prospects.
You will be able to prepare and process data with SageMaker Data Wrangler, train models (including XGBoost) and optimise them via hyperparameter tuning, then deploy to real-time endpoints. You’ll also learn to apply MLOps practices for automation, monitoring, and management of deployed models, and get exposure to no-code ML using SageMaker Canvas.
The course is ideal for professionals aiming to advance in data science, machine learning, and AWS-based roles across industries that rely on ML solutions. Recommended prerequisites include AWS Technical Essentials plus entry-level knowledge of Python and statistics.
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