AWS Certified Machine Learning

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Module 1 – Data Engineering
  • Data collection, transformation, and storage Choosing appropriate storage solutions: S3, Redshift, RDS, DynamoDB Data ingestion services: AWS Glue Kinesis Data Pipeline Database Migration Service (DMS) Data preparation and cleansing: Handling missing values Handling outliers Normalization Creating and managing data lakes with AWS Lake Formation
Module 2 – Exploratory Data Analysis
  • Analyzing datasets with pandas, NumPy, Jupyter Notebooks Data visualization using: Amazon QuickSight matplotlib seaborn Feature engineering: Feature scaling Encoding Feature selection Feature transformation Detecting: Data imbalance Data bias Data correlation Understanding data distribution & data drift
Module 3 – Modeling
  • Selecting the right ML algorithm: Classification Regression Clustering Using Amazon SageMaker for: Model building Built-in algorithms Bring Your Own Model (BYOM) SageMaker Studio Training & tuning: Hyperparameter tuning Distributed training Model evaluation: Confusion matrix Precision/Recall F1 score AUC Model explainability & bias detection with SageMaker Clarify
Module 4 – Machine Learning Implementation & Operations
  • Model deployment strategies: Real-time inference Batch transform Edge deployment (SageMaker Edge) CI/CD for ML (MLOps) using: SageMaker Pipelines Model registry Endpoint monitoring Monitoring & debugging ML models: SageMaker Model Monitor CloudWatch Scaling & securing ML endpoints: Auto Scaling IAM policies VPC access
Module 5 – Key AWS Services to Master
  • Amazon SageMaker (Studio, Pipelines, Clarify, Model Monitor, Edge) AWS Glue, Kinesis, Data Pipeline S3, Redshift, Athena, Lake Formation CloudWatch, IAM, Lambda, Step Functions Amazon QuickSight
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Course Detail

🟪 AWS Certified Machine Learning – Specialty – Course Content

  • Module 1 – Data Engineering 
    • Data collection, transformation, and storage
    • Choosing appropriate storage solutions: S3, Redshift, RDS, DynamoDB
    • Data ingestion services:
      • AWS Glue
      • Kinesis
      • Data Pipeline
      • Database Migration Service (DMS)
    • Data preparation and cleansing:
      • Handling missing values
      • Handling outliers
      • Normalization
    • Creating and managing data lakes with AWS Lake Formation
  • Module 2 – Exploratory Data Analysis 
    • Analyzing datasets with pandas, NumPy, Jupyter Notebooks
    • Data visualization using:
      • Amazon QuickSight
      • matplotlib
      • seaborn
    • Feature engineering:
      • Feature scaling
      • Encoding
      • Feature selection
      • Feature transformation
    • Detecting:
      • Data imbalance
      • Data bias
      • Data correlation
    • Understanding data distribution & data drift
  • Module 3 – Modeling 
    • Selecting the right ML algorithm:
      • Classification
      • Regression
      • Clustering
    • Using Amazon SageMaker for:
      • Model building
      • Built-in algorithms
      • Bring Your Own Model (BYOM)
      • SageMaker Studio
    • Training & tuning:
      • Hyperparameter tuning
      • Distributed training
    • Model evaluation:
      • Confusion matrix
      • Precision/Recall
      • F1 score
      • AUC
    • Model explainability & bias detection with SageMaker Clarify
  • Module 4 – Machine Learning Implementation & Operations 
    • Model deployment strategies:
      • Real-time inference
      • Batch transform
      • Edge deployment (SageMaker Edge)
    • CI/CD for ML (MLOps) using:
      • SageMaker Pipelines
      • Model registry
      • Endpoint monitoring
    • Monitoring & debugging ML models:
      • SageMaker Model Monitor
      • CloudWatch
    • Scaling & securing ML endpoints:
      • Auto Scaling
      • IAM policies
      • VPC access
  • Module 5 – Key AWS Services to Master
    • Amazon SageMaker (Studio, Pipelines, Clarify, Model Monitor, Edge)
    • AWS Glue, Kinesis, Data Pipeline
    • S3, Redshift, Athena, Lake Formation
    • CloudWatch, IAM, Lambda, Step Functions
    • Amazon QuickSight
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