AWS SageMaker Machine Learning Engineer in 30 Days + ChatGPT
Build 30+ ML Projects in 30 Days in AWS, Master SageMaker JumpStart, Canvas, AutoPilot, DataWrangler, Lambda & S3
4.50 (1053 reviews)
9,450
students
43 hours
content
Jan 2024
last update
$99.99
regular price
What you will learn
Build, Train, Test and Deploy Machine Learning Models in AWS
Leverage ChatGPT and GPT-4 to Automate Coding Tasks, Perform Code Debugging, Write Documentation and Add New Features to your Code
Define and Perform Image and Text Labeling Jobs Using AWS SageMaker GroundTruth
Prepare, Clean and Visualize data Using AWS SageMaker Data Wrangler without Writing any Code
Optimize ML model hyperparameters using GridSearch, Bayesian & Random Search Optimization Techniques
Master Key AWS services such as 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.
Learn how to define a lambda function in AWS management console, understand the anatomy of Lambda functions, and how to configure a test event in Lambda
Train a Machine Learning Regression and Classifier Models Using No-code AWS Canvas
Learn how to leverage Amazon SageMaker Autopilot and SageMaker Canvas to train multiple models without writing any code.
Perform Exploratory Data Analysis and Visualization Using Pandas, Searborn and Matplotlib Libraries
Understand Regression Models KPIs Such as RMSE, MSE, MAE, R2 and Adjusted R2
Understand Classification Models KPIs such as 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 the lambda function using Eventbridge (cloudwatch events)
Understand the difference between synchronous and asynchronous Lambda Functions invocations
Perform AI/ML Models Prototyping Using AutoGluon Library
How to monitor billing dashboard, set alarms, S3/EC2 instances pricing and request service limits increase
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, training options including built-in algorithms, AWS Marketplace, & customized ML Algorithms
Leverage a Yolo V3 Object Detection Algorithm available on the AWS Marketplace
Understand the format and Use Case of Json Lines and Manifest Files
Learn auto-labeling workflow and understand the difference between SageMaker GroundTruth and GroundTruth Plus
Learn how to define a labeling job with bounding boxes (object detection), pixel-level Semantic Segmentation, and text data
Understand the difference between data labeling workforces in AWS such as public mechanical Turks, private labelers and AWS curated third-party vendors
Learn the difference between Supervised, Unsupervised and Reinforcement Machine Learning Strategies
Perform data visualization using Seaborn & Matplotlib libraries, plots include line plot, pie charts, subplots, pairplots, countplots, and correlations heatmaps
Export a data wrangler workflow into Python script, create a custom formula and apply it to a given column in the data, and generate summary tables/bias report
Learn how to train an XG-boost algorithm in SageMaker using AWS JumpStart, assess trained model performance, plot residuals, & deploy an endpoint
Understand Bias-Variance Trade-off, L1 and L2 Regularization Techniques
Train/Test several ML Classifiers such as Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Trees, and Random Forest Classifiers
Learn SageMaker Built-in Algorithms such as Linear Learner, XG-Boost, Principal Component Analysis (PCA), and K-Nearest Neighbors
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Related Topics
4579406
udemy ID
3/3/2022
course created date
6/24/2022
course indexed date
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