AWS SageMaker Machine Learning Mastery: Build 30+ Projects in 30 Days

What you will learn:

• Build, Train, Test, and Deploy Machine Learning Models in AWS
• Master AWS SageMaker Services: JumpStart, Canvas, AutoPilot, Data Wrangler, Lambda, and S3
• Leverage ChatGPT to Enhance Coding Efficiency and Automate Tasks
• Perform Image and Text Labeling with AWS SageMaker GroundTruth
• Prepare, Clean, and Visualize Data Using AWS SageMaker Data Wrangler
• Optimize ML Model Hyperparameters Using GridSearch, Bayesian & Random Search Techniques
• Master Key AWS Services: Simple Storage Service (S3), Elastic Compute Cloud (EC2), Identity and Access Management (IAM), and CloudWatch
• Understand Machine Learning Workflow Automation Using AWS Lambda, Step Functions, and SageMaker Pipelines
• Define and Invoke Lambda Functions in AWS
• Train Machine Learning Regression and Classifier Models Using No-code AWS Canvas
• Leverage Amazon SageMaker Autopilot and SageMaker Canvas for Model Training Without Code
• Perform Exploratory Data Analysis and Visualization Using Pandas, Seaborn, and Matplotlib Libraries
• Understand Regression Model KPIs: RMSE, MSE, MAE, R2, and Adjusted R2
• Understand Classification Model KPIs: Accuracy, Precision, Recall, F1-Score, ROC, and AUC
• Define a Machine Learning Training Job Using AWS SageMaker JumpStart
• Deploy an Endpoint Using Amazon SageMaker, Perform Inference, and Generate Predictions
• Define a Lambda Function Using Boto3 SDK and Test Using Eventbridge (Cloudwatch Events)
• Understand the Difference Between Synchronous and Asynchronous Lambda Function Invocations
• Perform AI/ML Model Prototyping Using AutoGluon Library
• Understand the Difference Between Artificial Intelligence (AI), Machine Learning (ML), Data Science (DS), and Deep Learning (DL)
• Learn the Fundamentals of Amazon SageMaker, SageMaker Components, and Training Options
• Leverage a Yolo V3 Object Detection Algorithm from the AWS Marketplace
• Understand the Format and Use Case of Json Lines and Manifest Files
• Learn Auto-Labeling Workflow and the Difference Between SageMaker GroundTruth and GroundTruth Plus
• Define a Labeling Job with Bounding Boxes (Object Detection), Pixel-Level Semantic Segmentation, and Text Data
• Understand Different Data Labeling Workforces in AWS
• Learn the Difference Between Supervised, Unsupervised, and Reinforcement Machine Learning Strategies
• Perform Data Visualization Using Seaborn & Matplotlib Libraries, Including Line Plots, Pie Charts, Subplots, Pairplots, Countplots, and Correlations Heatmaps
• Export a Data Wrangler Workflow into a Python Script, Create Custom Formulas, and Generate Summary Tables/Bias Reports
• Train an XG-boost Algorithm in SageMaker Using AWS JumpStart, Assess Model Performance, Plot Residuals, and Deploy an Endpoint
• Understand Bias-Variance Trade-Off, L1 and L2 Regularization Techniques
• Train and Test Several ML Classifiers: Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Trees, and Random Forest Classifiers
• Learn SageMaker Built-in Algorithms: Linear Learner, XG-Boost, Principal Component Analysis (PCA), and K-Nearest Neighbors

Description