Habeshapros

Course Summary

The AWS Certified Machine Learning – Specialty Course is tailored for individuals with a strong background in machine learning who want to advance their skills in designing, building, deploying, and optimizing ML solutions on AWS. This course prepares you for the AWS Certified Machine Learning – Specialty (MLS-C01) certification, focusing on real-world applications, model optimization, and operations. Ideal for ML developers, data scientists, and AI specialists, this course will deepen your understanding of ML techniques, AWS services, and best practices for implementing scalable and cost-effective ML solutions.

Course Details

• Course Price: $1,400
• Total Duration: 12 weeks
• Class Schedule: Twice a week (1 hour and 30 minutes per session)
• Total Weekly Hours: 3 hours
• Mode: Live, instructor-led classes (Online)
• Platform: Zoom for classes and Slack for communication, assignments, and discussions

What You Will Learn

1. Data Engineering (20% of Exam Content)

• Data Repositories for ML: Creating data stores using AWS storage solutions like Amazon S3, EFS, and EBS
• Data Ingestion and Transformation: Using tools like AWS Glue, Amazon EMR, Kinesis Data Streams, and Amazon Managed Streaming for Apache Flink
• Data Preparation: ETL processes, feature engineering, and handling structured and unstructured data

2. Exploratory Data Analysis (24% of Exam Content)

• Data Cleaning and Preprocessing: Techniques for handling missing values, corrupt data, and data formatting
• Feature Engineering: Identifying and creating relevant features, dimensionality reduction, one-hot encoding, and handling text and image data
• Data Visualization and Statistical Analysis: Using descriptive statistics, correlation analysis, and visualizing data with histograms, scatter plots, and box plots

3. Modeling (36% of Exam Content)

• Problem Framing: Translating business problems into ML problems, choosing between supervised and unsupervised learning
• Model Selection and Training: Selecting models such as XGBoost, k-means, decision trees, and large language models (LLMs)
• Hyperparameter Optimization: Applying techniques such as cross-validation, L1/L2 regularization, and tuning learning rates
• Model Evaluation: Analyzing model performance metrics like ROC-AUC, F1 score, precision, recall, and interpreting confusion matrices

4. Machine Learning Implementation and Operations (20% of Exam Content)

• ML Solution Architecture: Designing ML systems with high availability, fault tolerance, and scalability using AWS best practices
• ML Services on AWS: Understanding AWS AI services like Amazon Rekognition, Transcribe, Polly, and SageMaker
• ML Deployment and Operationalization: Deploying ML models using AWS resources, setting up endpoints, monitoring performance with CloudWatch, and implementing A/B testing

Learning Outcomes

• Gain expertise in data engineering, exploratory data analysis, and feature engineering.
• Develop skills in framing business problems, selecting ML models, and optimizing model performance.
• Learn to deploy, monitor, and manage ML solutions on AWS using best practices.
• Be well-prepared for the AWS Certified Machine Learning – Specialty (MLS-C01) exam.

Course Schedule Overview

• Weeks 1-3: Data Engineering for ML
• Weeks 4-6: Exploratory Data Analysis and Feature Engineering
• Weeks 7-9: Modeling Techniques and Hyperparameter Optimization
• Weeks 10-12: ML Implementation, Operations, and Deployment

Frequently Asked Questions (FAQs)

1. How long will the course take to complete?
The course takes 12 weeks with 2 sessions per week.

2. Do I need prior experience in ML or AWS?
Yes, this course is intended for individuals with at least 2 years of experience in developing, architecting, and running ML or deep learning workloads in AWS.

3. What tools do I need?
You’ll need a computer with internet access and an AWS account. We’ll guide you through setting up the necessary AWS resources.

4. Will the classes be recorded?
Yes, all sessions are recorded, and you’ll have lifetime access to the recordings.

5. What happens if I miss a class?
You can catch up with the recorded session, and instructors are available on Slack for follow-up questions.

6. Will I receive a certificate?
Yes, you’ll receive a certificate of completion, and this course prepares you for the AWS Certified Machine Learning – Specialty (MLS-C01) certification exam.

7. Are there hands-on projects?
Yes, each domain includes practical assignments, and you’ll apply your skills in building, deploying, and optimizing ML models on AWS.

Why Choose This Course?

• AWS-Approved Curriculum: Designed around the AWS Machine Learning – Specialty (MLS-C01) exam guide.
• Hands-On Learning: Includes practical exercises and projects.
• Flexible Learning: Lifetime access to recorded sessions.
• Career Preparation: Ideal for ML developers, data scientists, and AI specialists.

Enroll Now for $1,400

Advance your career in machine learning with AWS expertise. Start your journey toward becoming a certified AWS Machine Learning Specialist.