Analyze Datasets and Prepare ML Fashions utilizing AutoML

About this Course

Within the first course of the Sensible Knowledge Science Specialization, you’ll be taught foundational ideas for exploratory knowledge evaluation (EDA), automated machine studying (AutoML), and textual content classification algorithms. With Amazon SageMaker Make clear and Amazon SageMaker Knowledge Wrangler, you’ll analyze a dataset for statistical bias, remodel the dataset into machine-readable options, and choose a very powerful options to coach a multi-class textual content classifier. You’ll then carry out automated machine studying (AutoML) to robotically practice, tune, and deploy one of the best text-classification algorithm for the given dataset utilizing Amazon SageMaker Autopilot. Subsequent, you’ll work with Amazon SageMaker BlazingText, a extremely optimized and scalable implementation of the favored FastText algorithm, to coach a textual content classifier with little or no code.

Sensible knowledge science is geared in direction of dealing with large datasets that don’t slot in your native {hardware} and will originate from a number of sources. One of many greatest advantages of growing and operating knowledge science initiatives within the cloud is the agility and elasticity that the cloud provides to scale up and out at a minimal value. The Sensible Knowledge Science Specialization helps you develop the sensible abilities to successfully deploy your knowledge science initiatives and overcome challenges at every step of the ML workflow utilizing Amazon SageMaker. This Specialization is designed for data-focused builders, scientists, and analysts conversant in the Python and SQL programming languages and wish to learn to construct, practice, and deploy scalable, end-to-end ML pipelines – each automated and human-in-the-loop – within the AWS cloud.

WHAT YOU WILL LEARN

• Put together knowledge, detect statistical knowledge biases, and carry out function engineering at scale to coach fashions with pre-built algorithms.

   

SKILLS YOU WILL GAIN

• Statistical Knowledge Bias Detection
• Multi-class Classification with FastText and BlazingText
• Knowledge ingestion
• Exploratory Knowledge Evaluation
• Automated Machine Studying (AutoML)

Syllabus – What you’ll be taught from this course

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WEEK

1

5 hours to finish

Week 1: Discover the Use Case and Analyze the Dataset

Ingest, discover, and visualize a product overview knowledge set for multi-class textual content classification.

WEEK

2

4 hours to finish

Week 2: Knowledge Bias and Function Significance

Decide a very powerful options in an information set and detect statistical biases.

WEEK

3

5 hours to finish

Week 3: Use Automated Machine Studying to coach a Textual content Classifier

Examine and evaluate fashions generated with automated machine studying (AutoML).

WEEK

4

5 hours to finish

Week 4: Constructed-in algorithms

Prepare a textual content classifier with BlazingText and deploy the classifier as a real-time inference endpoint to serve predictions.