AWS Certified Machine Learning – Specialty (MLS-C01)

AWS Machine Learning Specialty

Best AWS Certified Machine Learning Training and Certification Institute in Coimbatore

The AWS Certified Machine Learning – Specialty (MLS-C01) course is designed for data scientists, machine learning engineers, and developers who want to design, implement, and maintain machine learning solutions using AWS. This certification validates deep knowledge of ML modeling, data engineering, and algorithm optimization within the AWS ecosystem. 

At Linux Training Center in Coimbatore, we offer a hands-on, project-oriented course that equips you with the practical skills to build and deploy machine learning models at scale using Amazon SageMaker, AWS Lambda, S3, and more. You’ll gain expertise in selecting the right ML algorithms, preparing and transforming data, training and tuning models, and deploying intelligent applications with high accuracy and efficiency. 

The course includes guided labs, exam-oriented content, and real-world datasets to ensure you’re not just prepared for the MLS-C01 exam—but also job-ready.

Who Should Enroll?

This course is ideal for professionals with a background in data analysis, machine learning, software development, or AI who want to specialize in AWS-powered ML workflows. It is also suitable for experienced cloud engineers looking to move into AI/ML roles, or anyone interested in deploying scalable ML models in production environments.

What You Will Learn

You’ll learn to select appropriate machine learning services for a given use case, preprocess large datasets using AWS Glue and SageMaker Data Wrangler, train and evaluate supervised and unsupervised models, fine-tune hyperparameters, track model performance, deploy models using real-time or batch inference, and monitor production models for drift and accuracy. The course also covers security, compliance, and performance tuning practices in ML pipelines built on AWS.

Prerequisites

A foundational understanding of machine learning concepts such as regression, classification, and clustering is recommended. Basic knowledge of Python and some experience with AWS services (like S3 and IAM) will be helpful. This course is suitable for intermediate learners with a strong interest in data science and cloud computing.

Course Benefits

Fully mapped to the latest MLS-C01 exam objectives, real-world projects using SageMaker and other AWS ML services, live mentorship from AWS-certified ML practitioners, access to curated datasets and exam preparation material, and full certification and placement support post-training.

Career Opportunities After Certification

With this certification, you can qualify for high-demand roles such as AWS ML Engineer, AI/ML Specialist, Data Scientist – AWS, SageMaker Developer, or Cloud AI Consultant in industries ranging from healthcare and finance to retail and autonomous systems.

Step into the future of intelligent computing with the AWS Certified Machine Learning – Specialty (MLS-C01) course at Linux Training Center, Coimbatore. Gain hands-on experience, master AWS ML services, and pass the certification with confidence. Enroll now and get ready to transform your career in AI and machine learning. 

Reach out to reserve your seat today.

AWS Machine Learning Specialty Syllabus

Chapter 1

Course Introduction, About the Training Architect, About the Exam

Chapter 2

Machine Learning Fundamentals, Artificial Intelligence, What Is Machine Learning?, What Is Deep Learning?

Chapter 3

Section Introduction, Machine Learning Lifecycle, Supervised, Unsupervised, and Reinforcement, Optimization, Regularization, Hyperparameters, Validation

Chapter 4

Section Introduction, Feature Selection and Engineering, Principal Component Analysis (PCA), Missing and Unbalanced Data, Label and One Hot Encoding, Splitting and Randomization RecordIO Format

Chapter 5

Machine Learning Algorithms, Section Introduction, Logistical Regression, Linear Regression, Support Vector Machines, Decision Trees, Random Forests, K-Means, K-Nearest Neighbour Latent Dirichlet Allocation (LDA) Algorithm

Chapter 6

Deep Learning Algorithms, Section Introduction, Neural Networks, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN)

Chapter 7

Model Performance and Optimization, Section Introduction, Confusion Matrix, Sensitivity and Specificity, Accuracy and Precision, ROC/AUC, Gini Impurity, F1 Score

Chapter 8

Machine Learning Tools and Frameworks, Section Introduction, Introduction to Jupyter Notebooks, ML and DL Frameworks, TensorFlow, PyTorch, MXNet, Scikit-learn, HANDS-ON LAB Introduction to Jupyter Notebooks (AWS SageMaker), HANDS-ON LAB TensorFlow/Keras Basic Image Classifier (AWS SageMaker), HANDS-ON LAB MXNet Basic Classification (AWS SageMaker) HANDS-ON LAB Scikit-Learn Random Forest Classifier (AWS SageMaker)

Chapter 9

AWS Services, Section Introduction, S3, Glue, Athena, QuickSight, Kinesis, Streams, Firehose, Video, and Analytics, EMR with Spark, EC2 for ML, Amazon ML, HANDS-ON LAB Using Kinesis Data Firehose and Kinesis Data Analytics

Chapter 10

AWS Application Services AI/ML, Section Introduction, Amazon Rekognition (Images) Part 1, Amazon Rekognition (Images) Part 2 - the API, Amazon Rekognition (Video), Amazon Polly, Amazon Transcribe, Amazon Translate, Amazon Comprehend, Amazon Lex, Amazon Service Chaining with AWS Step Functions, HANDS-ON LAB Trigger an AWS Lambda Function from an S3 Event, HANDS-ON LAB Using AWS Step Functions to Manage a Long-Running Process, HANDS-ON LAB Perform Parallel Execution in AWS Step Functions

Chapter 11

Introduction, Section Introduction, What is Amazon SageMaker?, The Three Stages, Control (Console/SDK/Notebooks), SageMaker Notebooks

Chapter 12

Build, Data Preprocessing, Ground Truth, Preprocessing Image Data (Pinehead NotPinehead), Algorithms

Chapter 13

Train, SageMaker Algorithms - Architecture 1, SageMaker Algorithms - Architecture 2, SageMaker Algorithms - Architecture 3, Training an Image Classifier - Part 1 (Pinehead NotPinehead), Training an Image Classifier - Part 2 (Pinehead NotPinehead), Hyperparameter Tuning

Chapter 14

Deploy, inference Pipelines, Real-Time and Batch Inference, Deploy an Image Classifier (Pinehead, NotPinehead), Accessing Inference from Apps, Create a custom API for inference - Part 1 (Pinehead NotPinehead), Create a custom API for inference - Part 2 (Pinehead NotPinehead)

Chapter 15

Security, Securing SageMaker Notebooks, SageMaker and the VPC

Chapter 16

Other AWS Services, Section Introduction, DeepLens - Part 1, DeepLens - Part 2, DeepRacer - Part 1, DeepRacer - Part 2

Chapter 17

The Exam, How to Answer Questions, How to Prepare, PRACTICE EXAM AWS Certified Machine Learning-Specialty (MLS-C01) Final Practice Exam

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