Analyze Datasets and Train ML Models using AutoML from Coursera

In the first course of the Practical Data Science Specialization, you will learn foundational concepts for exploratory data analysis (EDA), automated machine learning (AutoML), and text classification algorithms. With Amazon SageMaker Clarify and Amazon SageMaker Data Wrangler, you will analyze a dataset for statistical bias, transform the dataset into machine-readable features, and select the most important features to train a multi-class text classifier. You will then perform automated machine learning (AutoML) to automatically train, tune, and deploy the best text-classification algorithm for the given dataset using Amazon SageMaker Autopilot. Next, you will work with Amazon SageMaker BlazingText, a highly optimized and scalable implementation of the popular FastText algorithm, to train a text classifier with very little code. Practical data science is geared towards handling massive datasets that do not fit in your local hardware and could originate from multiple sources. One of the biggest benefits of developing and running data science projects in the cloud is the agility and elasticity that the cloud offers to scale up and out at a minimum cost. The Practical Data Science Specialization helps you develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker. This Specialization is designed for data-focused developers, scientists, and analysts familiar with the Python and SQL programming languages and want to learn how to build, train, and deploy scalable, end-to-end ML pipelines - both automated and human-in-the-loop - in the AWS cloud.

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Develops foundational skills in exploratory data analysis, automated machine learning, and text classification for practical data science applications

Involves hands-on practice using AWS cloud services like SageMaker Clarify, Data Wrangler, Autopilot, and BlazingText

Taught by experts from AWS, ensuring industry-relevant knowledge and practical insights

Suitable for individuals familiar with Python and SQL, catering to both developers and data professionals

May require prior knowledge of cloud computing and AWS services

Focuses on practical skills and real-world applications of data science

Reviews summary

Practical automl with sagemaker

This course covers the foundational concepts for exploratory data analysis, automated machine learning (AutoML), and text classification algorithms using Amazon SageMaker. Students will learn how to use Amazon SageMaker Clarify and Amazon SageMaker Data Wrangler to analyze a dataset for statistical bias, transform the dataset into machine-readable features, and select the most important features to train a multiclass text classifier. They will also learn how to use Amazon SageMaker Autopilot to automatically train, tune, and deploy the best text classification algorithm for the given dataset.

Students will gain experience with SageMaker Studio.

"I enjoyed playing with Sagemaker Studio."

Students will learn about automated machine learning (AutoML) and how to use it to automatically train, tune, and deploy the best text classification algorithm for a given dataset.

"I learned foundational concepts for exploratory data analysis (EDA), automated machine learning (AutoML), and text classification algorithms."

"Using Amazon SageMaker Autopilot, I then used automated machine learning (AutoML) to automatically train, tune, and deploy the best text classification algorithm for the given dataset."

Students have access to practical labs to reinforce their learning.

"I learnt a lot with this incredible course."

"This course really helped me understand about AWS services"

Some students have experienced technical issues with the labs, such as them not starting or the kernel taking a long time to initialize.

"The Labs don't start, the kernel takes ages to initialise."

"The labs took a long time to load and even crashed for several days for the entire cohort."

Some students found the labs to be too easy and not challenging enough.

"The labs are pure copy past of variable names but they are clear in terms of explanations, the quizzes are nice but should be a bit harder."

"The beginning of the course was a bit slow-paced and look more like a marketing campagin for AWS product : AWS Glue, AWS Athena, etc.. but I like a lot the part on AWS SageMaker Autopilot for auto-ML and the implementation of BlazingText for NLP and Sentiment Analysis. It finishes really strong."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Analyze Datasets and Train ML Models using AutoML with these activities:

Read 'Hands-On Machine Learning with Amazon SageMaker'

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Gain a comprehensive understanding of machine learning concepts and Amazon SageMaker

View Generative AI on AWS on Amazon

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Read through the book's chapters, focusing on topics covered in the course
Complete the exercises and hands-on projects provided in the book
Reference the book as needed throughout the course to reinforce learning

Participate in online forums and discussion groups

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Engage with peers and experts to clarify concepts and share knowledge

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Join online forums and discussion groups related to data science and machine learning
Ask questions, share insights, and collaborate with other learners
Attend virtual meetups and conferences to connect with the data science community

practice exploratory data analysis exercises

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Develop foundational understanding of preparing data for machine learning algorithms

Browse courses on Exploratory Data Analysis

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Use Amazon SageMaker Data Wrangler to transform a dataset into machine-readable features
Select the most important features to train a multi-class text classifier
Use Python libraries like Pandas and NumPy for data manipulation and analysis

Five other activities

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Build a text classification model using Amazon SageMaker Autopilot

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Gain hands-on experience with automated machine learning for text classification

Browse courses on Automated Machine Learning

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Use Amazon SageMaker Autopilot to automatically train, tune, and deploy the best text-classification algorithm for a given dataset
Evaluate the performance of the model and make necessary adjustments
Deploy the model to production and monitor its performance

Attend data science workshops and hackathons

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Learn from experts, network with peers, and refine practical skills

Browse courses on Data Science

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Identify and register for data science workshops and hackathons that cover relevant topics
Attend the events, actively participate in discussions and hands-on activities
Network with industry professionals, researchers, and fellow learners

explore advanced text classification techniques

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Expand knowledge of text classification beyond the course materials

Browse courses on Text Classification

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Read research papers and articles on recent advancements in text classification
Follow online tutorials and workshops on advanced text classification techniques
Experiment with different text classification algorithms and models

Contribute to open-source data science projects

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Gain practical experience and contribute to the data science community

Browse courses on Open Source

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Identify open-source data science projects that align with your interests
Review the project documentation and contribute code or other resources
Engage with the project maintainers and other contributors

Develop a personal data science project

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Apply course concepts to a real-world problem and enhance practical skills

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Identify a problem or challenge that can be addressed using data science
Gather and preprocess data relevant to the problem
Apply machine learning techniques to analyze data and develop a solution
Evaluate the solution and make necessary improvements
Present the project outcomes, including the problem statement, methodology, results, and insights gained

Career center

Learners who complete Analyze Datasets and Train ML Models using AutoML will develop knowledge and skills that may be useful to these careers:

Data Scientist

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Project Manager

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Data Engineer

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