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Kirsten Gokay,
Meeta Dash,
Alyssa Simpson-Rochwerger,
Andrea Butkovic,
and
Kiran Vajapey
Learn how to train and evaluate a neural network using automated machine learning tools.
Register for this course and see more details by visiting:
OpenCourser.com/course/1z71nb/build
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UDACITY40
What's inside
Syllabus
Learn strategies for training a model from scratch or using transfer learning. Evaluate a model using machine learning tools, such as AutoML.
Build a classification model to classify images of chest xrays using Google AutoML, an automated machine learning tool.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by industry renown instructors such as Kirsten Gokay, Meeta Dash, Alyssa Simpson-Rochwerger, Andrea Butkovic, and Kiran Vajapey
Provides foundational knowledge for beginners or intermediate learners to strengthen their understanding of machine learning
Delivers hands-on experience through the use of Google AutoML, an automated machine learning tool
Offers practical knowledge and skills for building and evaluating machine learning models in a real-world setting
Covers up-to-date industry practices, such as transfer learning, evaluation using machine learning tools, and model building for image classification
Requires a background in machine learning principles and familiarity with programming languages like Python
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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 Build A Model with these
activities:
Calculate the correlation coefficient
Show steps
Refresh your knowledge of calculating the correlation coefficient to strengthen your foundation for machine learning concepts.
Browse courses on
Correlation
Show steps
-
Review the formula for calculating the correlation coefficient.
-
Practice calculating the correlation coefficient using sample data.
Attend a meetup on machine learning
Show steps
Attending a meetup on machine learning will allow you to connect with other learners and professionals in the field.
Browse courses on
Machine Learning
Show steps
-
Find a meetup on machine learning.
-
Register for the meetup.
-
Attend the meetup.
Compile your course materials
Show steps
By compiling your course materials, you'll have easy access to all the resources you need to succeed in the course.
Browse courses on
Organization
Show steps
-
Gather your course materials.
-
Organize your materials.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Familiarize with Transfer Learning
Show steps
By refreshing your knowledge of transfer learning, you'll be better prepared to understand how to use it in training and evaluating neural networks.
Browse courses on
Transfer Learning
Show steps
-
Read articles or watch videos on transfer learning.
-
Experiment with transfer learning using a pre-trained model.
Attend an AutoML workshop
Show steps
Enhance your learning by attending an AutoML workshop to gain practical insights and network with experts in the field.
Browse courses on
AutoML
Show steps
-
Find an AutoML workshop that aligns with your interests.
-
Register for the workshop and prepare any necessary materials.
-
Actively participate in the workshop sessions and ask questions.
Explore Google AutoML using the Quickstart Guide
Show steps
Enhance your understanding of Google AutoML by following the Quickstart Guide to gain hands-on experience with the tool.
Show steps
-
Follow the steps in the Google AutoML Quickstart Guide.
-
Experiment with different AutoML configurations and observe the results.
Participate in a workshop on neural networks
Show steps
Participating in a workshop on neural networks will provide you with hands-on experience and guidance from experts.
Browse courses on
Neural Networks
Show steps
-
Find a workshop on neural networks.
-
Register for the workshop.
-
Attend the workshop.
Follow tutorials on Google AutoML
Show steps
Guided tutorials on Google AutoML will provide hands-on practice with using this automated machine learning tool for image classification.
Show steps
-
Find tutorials on Google AutoML.
-
Follow the steps in the tutorials to train and evaluate a model.
Classify Images Using AutoML Tables
Show steps
Solidify your understanding of image classification and AutoML Tables by practicing with real-world datasets.
Browse courses on
Image Classification
Show steps
-
Import your own dataset into AutoML Tables.
-
Train an image classification model using AutoML Tables.
-
Evaluate the performance of your trained model.
Practice training and evaluating models
Show steps
Practice training and evaluating models will solidify your understanding of the concepts and techniques covered in the course.
Browse courses on
Model Training
Show steps
-
Create a dataset.
-
Train a model on the dataset.
-
Evaluate the model's performance.
Write a blog post on the applications of transfer learning
Show steps
Expand your knowledge and demonstrate your understanding of transfer learning by creating a blog post that explores its applications in various industries.
Browse courses on
Transfer Learning
Show steps
-
Research different industries where transfer learning is used.
-
Provide examples of successful transfer learning projects.
-
Discuss the benefits and challenges of using transfer learning.
Build a Chatbot Using AutoML
Show steps
Challenge yourself by applying your knowledge to build a chatbot using AutoML, enhancing your practical skills and solidifying your understanding.
Browse courses on
Chatbot
Show steps
-
Choose a specific domain or purpose for your chatbot.
-
Gather and prepare your training data.
-
Train and deploy your chatbot using AutoML.
-
Test and iterate on your chatbot to improve its performance.
Build a classification model using Google AutoML
Show steps
By building a classification model using Google AutoML, you'll gain practical experience using the tool and applying the concepts learned in the course.
Browse courses on
Image Classification
Show steps
-
Choose a dataset.
-
Preprocess the data.
-
Train a model using Google AutoML.
-
Evaluate the model's performance.
-
Deploy the model.
Write a blog post about your experience using Google AutoML
Show steps
By creating a blog post about your experience using Google AutoML, you'll reflect on what you learned and share your knowledge with others.
Show steps
-
Choose a topic.
-
Write an outline.
-
Write the blog post.
-
Edit and proofread your post.
-
Publish your post.
Calculate the correlation coefficient
Show steps
Refresh your knowledge of calculating the correlation coefficient to strengthen your foundation for machine learning concepts.
Browse courses on
Correlation
Show steps
Show steps
Show steps
- Review the formula for calculating the correlation coefficient.
- Practice calculating the correlation coefficient using sample data.
Attend a meetup on machine learning
Show steps
Attending a meetup on machine learning will allow you to connect with other learners and professionals in the field.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a meetup on machine learning.
- Register for the meetup.
- Attend the meetup.
Compile your course materials
Show steps
By compiling your course materials, you'll have easy access to all the resources you need to succeed in the course.
Browse courses on
Organization
Show steps
Show steps
Show steps
- Gather your course materials.
- Organize your materials.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Familiarize with Transfer Learning
Show steps
By refreshing your knowledge of transfer learning, you'll be better prepared to understand how to use it in training and evaluating neural networks.
Browse courses on
Transfer Learning
Show steps
Show steps
Show steps
- Read articles or watch videos on transfer learning.
- Experiment with transfer learning using a pre-trained model.
Attend an AutoML workshop
Show steps
Enhance your learning by attending an AutoML workshop to gain practical insights and network with experts in the field.
Browse courses on
AutoML
Show steps
Show steps
Show steps
- Find an AutoML workshop that aligns with your interests.
- Register for the workshop and prepare any necessary materials.
- Actively participate in the workshop sessions and ask questions.
Explore Google AutoML using the Quickstart Guide
Show steps
Enhance your understanding of Google AutoML by following the Quickstart Guide to gain hands-on experience with the tool.
Show steps
Show steps
Show steps
- Follow the steps in the Google AutoML Quickstart Guide.
- Experiment with different AutoML configurations and observe the results.
Participate in a workshop on neural networks
Show steps
Participating in a workshop on neural networks will provide you with hands-on experience and guidance from experts.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Find a workshop on neural networks.
- Register for the workshop.
- Attend the workshop.
Follow tutorials on Google AutoML
Show steps
Guided tutorials on Google AutoML will provide hands-on practice with using this automated machine learning tool for image classification.
Show steps
Show steps
Show steps
- Find tutorials on Google AutoML.
- Follow the steps in the tutorials to train and evaluate a model.
Classify Images Using AutoML Tables
Show steps
Solidify your understanding of image classification and AutoML Tables by practicing with real-world datasets.
Browse courses on
Image Classification
Show steps
Show steps
Show steps
- Import your own dataset into AutoML Tables.
- Train an image classification model using AutoML Tables.
- Evaluate the performance of your trained model.
Practice training and evaluating models
Show steps
Practice training and evaluating models will solidify your understanding of the concepts and techniques covered in the course.
Browse courses on
Model Training
Show steps
Show steps
Show steps
- Create a dataset.
- Train a model on the dataset.
- Evaluate the model's performance.
Write a blog post on the applications of transfer learning
Show steps
Expand your knowledge and demonstrate your understanding of transfer learning by creating a blog post that explores its applications in various industries.
Browse courses on
Transfer Learning
Show steps
Show steps
Show steps
- Research different industries where transfer learning is used.
- Provide examples of successful transfer learning projects.
- Discuss the benefits and challenges of using transfer learning.
Build a Chatbot Using AutoML
Show steps
Challenge yourself by applying your knowledge to build a chatbot using AutoML, enhancing your practical skills and solidifying your understanding.
Browse courses on
Chatbot
Show steps
Show steps
Show steps
- Choose a specific domain or purpose for your chatbot.
- Gather and prepare your training data.
- Train and deploy your chatbot using AutoML.
- Test and iterate on your chatbot to improve its performance.
Build a classification model using Google AutoML
Show steps
By building a classification model using Google AutoML, you'll gain practical experience using the tool and applying the concepts learned in the course.
Browse courses on
Image Classification
Show steps
Show steps
Show steps
- Choose a dataset.
- Preprocess the data.
- Train a model using Google AutoML.
- Evaluate the model's performance.
- Deploy the model.
Write a blog post about your experience using Google AutoML
Show steps
By creating a blog post about your experience using Google AutoML, you'll reflect on what you learned and share your knowledge with others.
Show steps
Show steps
Show steps
- Choose a topic.
- Write an outline.
- Write the blog post.
- Edit and proofread your post.
- Publish your post.
Career center
Learners who complete Build A Model will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists build, train, and evaluate models to solve complex problems and create solutions for a variety of industries. This course will help you gain the skills and knowledge necessary for a successful career as a Data Scientist by providing you with a foundation in machine learning and model evaluation. You will learn how to train and evaluate neural networks using automated machine learning tools, which is a valuable skill for Data Scientists.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning systems. This course will help you gain the skills and knowledge necessary for a successful career as a Machine Learning Engineer by providing you with a foundation in machine learning and model evaluation. You will learn how to train and evaluate neural networks using automated machine learning tools, which is a valuable skill for Machine Learning Engineers.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to help businesses make informed decisions. This course will help you gain the skills and knowledge necessary for a successful career as a Data Analyst by providing you with a foundation in machine learning and model evaluation. You will learn how to train and evaluate neural networks using automated machine learning tools, which is a valuable skill for Data Analysts.
Business Analyst
Business Analyst
Business Analysts use data to help businesses improve their operations and make better decisions. This course will help you gain the skills and knowledge necessary for a successful career as a Business Analyst by providing you with a foundation in machine learning and model evaluation. You will learn how to train and evaluate neural networks using automated machine learning tools, which is a valuable skill for Business Analysts.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course may be useful for Software Engineers who want to learn more about machine learning and model evaluation. You will learn how to train and evaluate neural networks using automated machine learning tools, which is a valuable skill for Software Engineers.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course may be useful for Quantitative Analysts who want to learn more about machine learning and model evaluation. You will learn how to train and evaluate neural networks using automated machine learning tools, which is a valuable skill for Quantitative Analysts.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment decisions. This course may be useful for Financial Analysts who want to learn more about machine learning and model evaluation. You will learn how to train and evaluate neural networks using automated machine learning tools, which is a valuable skill for Financial Analysts.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve complex problems in business and industry. This course may be useful for Operations Research Analysts who want to learn more about machine learning and model evaluation. You will learn how to train and evaluate neural networks using automated machine learning tools, which is a valuable skill for Operations Research Analysts.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to help businesses and organizations make informed decisions. This course may be useful for Statisticians who want to learn more about machine learning and model evaluation. You will learn how to train and evaluate neural networks using automated machine learning tools, which is a valuable skill for Statisticians.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. This course may be useful for Actuaries who want to learn more about machine learning and model evaluation. You will learn how to train and evaluate neural networks using automated machine learning tools, which is a valuable skill for Actuaries.
Economist
Economist
Economists study the production, distribution, and consumption of goods and services. This course may be useful for Economists who want to learn more about machine learning and model evaluation. You will learn how to train and evaluate neural networks using automated machine learning tools, which is a valuable skill for Economists.
Market Researcher
Market Researcher
Market Researchers study the needs and wants of consumers. This course may be useful for Market Researchers who want to learn more about machine learning and model evaluation. You will learn how to train and evaluate neural networks using automated machine learning tools, which is a valuable skill for Market Researchers.
Risk Manager
Risk Manager
Risk Managers identify, assess, and manage risks. This course may be useful for Risk Managers who want to learn more about machine learning and model evaluation. You will learn how to train and evaluate neural networks using automated machine learning tools, which is a valuable skill for Risk Managers.
Underwriter
Underwriter
Underwriters assess and manage risks for insurance companies. This course may be useful for Underwriters who want to learn more about machine learning and model evaluation. You will learn how to train and evaluate neural networks using automated machine learning tools, which is a valuable skill for Underwriters.
Auditor
Auditor
Auditors examine financial records to ensure accuracy and compliance. This course may be useful for Auditors who want to learn more about machine learning and model evaluation. You will learn how to train and evaluate neural networks using automated machine learning tools, which is a valuable skill for Auditors.
For more career information including salaries, visit:
OpenCourser.com/course/1z71nb/build
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Build A Model.
Provides a comprehensive overview of reinforcement learning, a type of machine learning that involves learning how to take actions in an environment to maximize a reward signal. It covers various reinforcement learning algorithms and applications in areas such as robotics and game playing.
Provides a comprehensive overview of pattern recognition and machine learning techniques, with a focus on statistical and probabilistic approaches. It covers topics such as supervised learning, unsupervised learning, and statistical models.
Provides a comprehensive overview of deep learning techniques for natural language processing tasks, such as text classification, language modeling, and machine translation. It covers various neural network architectures and training algorithms.
Provides a comprehensive overview of speech and language processing techniques, including speech recognition, natural language understanding, and speech synthesis. It covers various statistical and machine learning approaches.
Provides a практический guide to deep learning techniques for computer vision tasks, such as image classification, object detection, and image segmentation. It covers various neural network architectures and training algorithms.
By Francois Chollet, the creator of Keras, introduces deep learning concepts and techniques through practical examples using Python and the Keras library. It covers various neural network architectures, optimization algorithms, and applications in areas such as computer vision and natural language processing.
Save
Provides a comprehensive overview of computer vision techniques, including image processing, feature detection, and object recognition. It covers various algorithms and applications in areas such as robotics and autonomous driving.
Introduces machine learning concepts and algorithms using Python code examples. It covers topics such as data preprocessing, supervised learning, unsupervised learning, and model evaluation.
Save
Provides a comprehensive overview of generative adversarial networks (GANs), a class of deep learning models used for generating new data samples. It covers various GAN architectures and training algorithms, as well as applications in areas such as image generation and language modeling.
Provides a more теоретическая-driven approach to machine learning, emphasizing the probabilistic foundations of various machine learning algorithms. It covers topics such as Bayesian inference, graphical models, and reinforcement learning.
Provides a gentle introduction to machine learning concepts and algorithms, with a focus on practical examples and hands-on exercises. It covers topics such as supervised learning, unsupervised learning, and model evaluation.
Save
Provides a more algorithmic perspective on machine learning, with a focus on the underlying mathematical and computational techniques. It covers topics such as linear algebra, optimization, and statistical models.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1z71nb/build
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Time
30240 hours
Level
Introductory
Via
Udacity
Instructors
Kirsten Gokay
Meeta Dash
Alyssa Simpson-Rochwerger
Andrea Butkovic
Kiran Vajapey
Language
English
Good to know
Taught by industry renown instructors such as Kirsten Gokay, Meeta Dash, Alyssa Simpson-Rochwerger, Andrea Butkovic, and Kiran Vajapey
Provides foundational knowledge for beginners or intermediate learners to strengthen their understanding of machine learning
Delivers hands-on experience through the use of Google AutoML, an automated machine learning tool
Offers practical knowledge and skills for building and evaluating machine learning models in a real-world setting
Covers up-to-date industry practices, such as transfer learning, evaluation using machine learning tools, and model building for image classification
Requires a background in machine learning principles and familiarity with programming languages like Python
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