With the knowledge you gain from this course, you can quickly develop Machine Learning solutions on Amazon Web Services (AWS).
You will be introduced to techniques for data exploration to help you find patterns, meaning, and value in your company data through hands-on laboratories and practical activities.
At the end of the course, participants will be able to:
Understand Machine Learning (ML) concepts
Prepare your data
Upload your dataset to Amazon SageMaker
Train, analyze, and tune an ML model
Analyze and publish your results
Anyone who needs to develop machine learning solutions to analyze data and wants to take use of Amazon's cloud-based data science platform.
Overview of key Machine Learning and Deep Learning concepts
Getting about in AWS
Overview of SageMaker features
Taking your first look at SageMaker studio
Identifying your data and articulating your problem
Format data for consistency
Cleaning and validating your data
Uploading to SageMaker
Clustering
Trend analysis
Finding other relationships between different types of data
Frequency tables
Cross-tabulation tables
Bar charts
Line graphs
Pie charts
Heat Maps
Scatter graphs
Creating a Training job
Assigning Compute resources
Selecting the right algorithm
Overview of using custom code (Python, TensorFlow)
SageMaker Hosting Services
Configuring and creating an HTTPS endpoint
Making inferences from your dataset
Indexing and real-time indices
Using Batch Transform to preprocess data to train a new model
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