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Matt Maybeno
Explore the power of Machine Learning through our online training course. Learn via AWS SageMaker and delve into high-level concepts. Start learning today!
Prerequisite details
Read more
Explore the power of Machine Learning through our online training course. Learn via AWS SageMaker and delve into high-level concepts. Start learning today!
Prerequisite details
Read more
Explore the power of Machine Learning through our online training course. Learn via AWS SageMaker and delve into high-level concepts. Start learning today!
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Pandas
- Basic probability
- Intermediate Python
- Jupyter notebooks
- Basic Python
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/lx0699/introduction
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What's inside
Syllabus
Overview of key background around Machine Learning and preparing you to be successful in the rest of this course.
Use AWS SageMaker Studio to access S3 datasets and perform data analysis, feature engineering with Data Wrangler and Pandas. And finally label new data using SageMaker Ground Truth.
Read more
Syllabus
Overview of key background around Machine Learning and preparing you to be successful in the rest of this course.
Use AWS SageMaker Studio to access S3 datasets and perform data analysis, feature engineering with Data Wrangler and Pandas. And finally label new data using SageMaker Ground Truth.
Read more
In this lesson you'll learn about ML Lifecycles, how to differentiate between supervised vs. unsupervised ML, regression methods, and classification methods.
In this lesson you'll load a dataset, clean/create features, train a regression/classification model with scikit learn, evaluate a model and tune a model's hyperparameter.
In this lesson you'll train, test, and optimize on liner, tree-based, XGBoost, and AutoGluon Tabular models. And you will also create a model using SageMaker Jumpstart
Train a model using AutoGluon to predict bike sharing demand, and see how highly you can place in the competition!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches students how to use AWS Sagemaker to access data and perform data analysis, which is used extensively in the industry
Teaches students how to differentiate between supervised vs. unsupervised ML, regression methods, and classification methods, which are core concepts in machine learning
Develops skills in using AWS SageMaker JumpStart, which is a popular tool for building and deploying machine learning models
Courses taught by Matt Maybeno, a recognized expert in machine learning
Taught by Udacity, a reputable provider of online courses
Offers hands-on labs and interactive materials, which can help students learn the material more effectively
Save this course
Save Introduction to Machine Learning to your list so you can find it easily later:
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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 Introduction to Machine Learning with these
activities:
Review basic algebra and statistics
Show steps
Refresh your knowledge of algebra and statistics to enhance your understanding of the concepts covered in the course.
Browse courses on
Algebra
Show steps
-
Go over your notes or textbooks from previous algebra and statistics courses.
-
Practice solving algebra and statistics problems online or in a workbook.
-
Take an online quiz or assessment to test your understanding.
Practice Data Wrangler techniques
Show steps
Reinforce your understanding using Data Wrangler techniques and concepts that will be covered in the course.
Browse courses on
Data Wrangling
Show steps
-
Load a dataset in Data Wrangler
-
Clean the data using Data Wrangler transformations
-
Create features in Data Wrangler
-
Export the cleaned and transformed data
Discuss Machine Learning concepts with peers
Show steps
Deepen your understanding by engaging in peer discussions to clarify concepts and exchange perspectives related to Machine Learning, as covered in the course.
Show steps
-
Identify a topic from the course materials
-
Form a study group with classmates
-
Meet regularly to discuss the topic
Four other activities
Expand to see all activities and additional details
Show all seven activities
Code scikit-learn models
Show steps
Solidify your understanding of how to use scikit-learn to build and train various Machine Learning models, which will be covered in the course.
Browse courses on
scikit-learn
Show steps
-
Load a dataset in Python
-
Create a scikit-learn model object
-
Train the model using the training data
-
Evaluate the model on the test data
Optimize model hyperparameters with scikit-learn
Show steps
Gain a deeper understanding of how to optimize the hyperparameters of a scikit-learn model, a topic that will be covered in the course, to improve its performance.
Browse courses on
scikit-learn
Show steps
-
Load a dataset in Python
-
Create a scikit-learn model object
-
Use scikit-learn's grid search to find optimal hyperparameters
Build and deploy a model with SageMaker
Show steps
Gain practical experience in building and deploying Machine Learning models using SageMaker, which will be covered in the course, to enhance your hands-on skills.
Browse courses on
SageMaker
Show steps
-
Create a SageMaker notebook instance
-
Load a dataset into SageMaker
-
Train a model in SageMaker
-
Deploy the model as an endpoint
Participate in the bike sharing demand prediction competition
Show steps
Challenge yourself and showcase your skills in a practical setting by participating in the bike sharing demand prediction competition, which is a direct application of the concepts and tools covered in the course.
Browse courses on
AutoGluon
Show steps
-
Register for the competition
-
Load the competition dataset
-
Use AutoGluon or SageMaker Jumpstart to train and evaluate a model
-
Submit your predictions
Review basic algebra and statistics
Show steps
Refresh your knowledge of algebra and statistics to enhance your understanding of the concepts covered in the course.
Browse courses on
Algebra
Show steps
Show steps
Show steps
- Go over your notes or textbooks from previous algebra and statistics courses.
- Practice solving algebra and statistics problems online or in a workbook.
- Take an online quiz or assessment to test your understanding.
Practice Data Wrangler techniques
Show steps
Reinforce your understanding using Data Wrangler techniques and concepts that will be covered in the course.
Browse courses on
Data Wrangling
Show steps
Show steps
Show steps
- Load a dataset in Data Wrangler
- Clean the data using Data Wrangler transformations
- Create features in Data Wrangler
- Export the cleaned and transformed data
Discuss Machine Learning concepts with peers
Show steps
Deepen your understanding by engaging in peer discussions to clarify concepts and exchange perspectives related to Machine Learning, as covered in the course.
Show steps
Show steps
Show steps
- Identify a topic from the course materials
- Form a study group with classmates
- Meet regularly to discuss the topic
Four other activities
Expand to see all activities and additional details
Show all seven activities
Code scikit-learn models
Show steps
Solidify your understanding of how to use scikit-learn to build and train various Machine Learning models, which will be covered in the course.
Browse courses on
scikit-learn
Show steps
Show steps
Show steps
- Load a dataset in Python
- Create a scikit-learn model object
- Train the model using the training data
- Evaluate the model on the test data
Optimize model hyperparameters with scikit-learn
Show steps
Gain a deeper understanding of how to optimize the hyperparameters of a scikit-learn model, a topic that will be covered in the course, to improve its performance.
Browse courses on
scikit-learn
Show steps
Show steps
Show steps
- Load a dataset in Python
- Create a scikit-learn model object
- Use scikit-learn's grid search to find optimal hyperparameters
Build and deploy a model with SageMaker
Show steps
Gain practical experience in building and deploying Machine Learning models using SageMaker, which will be covered in the course, to enhance your hands-on skills.
Browse courses on
SageMaker
Show steps
Show steps
Show steps
- Create a SageMaker notebook instance
- Load a dataset into SageMaker
- Train a model in SageMaker
- Deploy the model as an endpoint
Participate in the bike sharing demand prediction competition
Show steps
Challenge yourself and showcase your skills in a practical setting by participating in the bike sharing demand prediction competition, which is a direct application of the concepts and tools covered in the course.
Browse courses on
AutoGluon
Show steps
Show steps
Show steps
- Register for the competition
- Load the competition dataset
- Use AutoGluon or SageMaker Jumpstart to train and evaluate a model
- Submit your predictions
Career center
Learners who complete Introduction to Machine Learning will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Enter the field of Machine Learning Engineering, where you can apply your skills to build and implement machine learning models. This course covers topics such as data analysis, feature engineering, model training, and hyperparameter tuning. These skills are essential for Machine Learning Engineers, and this course provides a solid foundation in these areas.
Data Scientist
Data Scientist
As a Data Scientist, you will use machine learning to solve business problems. This course will help you build a foundation in machine learning, data analysis, and feature engineering. With these skills, you will be able to prepare data for machine learning models, train and evaluate models, and communicate your findings to stakeholders.
Software Engineer
Software Engineer
In the role of Software Engineer, you can specialize in machine learning. This course will help you build a strong foundation in machine learning concepts and techniques. You will learn how to implement machine learning algorithms, train and evaluate models, and deploy models to production. With these skills, you will be able to develop innovative machine learning solutions.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use machine learning to analyze data and make predictions. This course will help you build a foundation in machine learning, data analysis, and statistical modeling. With these skills, you will be able to develop and implement machine learning models for financial applications.
Business Analyst
Business Analyst
Business Analysts use machine learning to solve business problems. This course will help you build a foundation in machine learning, data analysis, and business intelligence. With these skills, you will be able to identify business problems that can be solved with machine learning, develop and implement machine learning solutions, and communicate your findings to stakeholders.
Product Manager
Product Manager
Product Managers use machine learning to develop and improve products. This course will help you build a foundation in machine learning, data analysis, and product management. With these skills, you will be able to identify product opportunities that can be addressed with machine learning, develop and implement machine learning solutions, and measure the impact of machine learning on your products.
Data Engineer
Data Engineer
Data Engineers build and maintain the infrastructure that supports machine learning models. This course will help you build a foundation in machine learning, data engineering, and cloud computing. With these skills, you will be able to design and implement data pipelines for machine learning models, provision and manage cloud resources, and monitor and maintain machine learning systems.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers develop new machine learning algorithms and techniques. This course will help you build a strong foundation in machine learning theory and practice. You will learn about the latest advances in machine learning, and you will be able to conduct your own research in this field.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design and implement AI systems. This course will help you build a foundation in machine learning, AI, and software engineering. With these skills, you will be able to develop and implement AI solutions for a variety of applications.
Data Analyst
Data Analyst
Data Analysts use machine learning to analyze data and extract insights. This course will help you build a foundation in machine learning, data analysis, and data visualization. With these skills, you will be able to prepare data for machine learning models, train and evaluate models, and communicate your findings to stakeholders.
Statistician
Statistician
Statisticians use machine learning to analyze data and make predictions. This course will help you build a foundation in machine learning, statistics, and data analysis. With these skills, you will be able to develop and implement machine learning models for a variety of applications.
Financial Analyst
Financial Analyst
Financial Analysts use machine learning to analyze financial data and make investment decisions. This course will help you build a foundation in machine learning, finance, and data analysis. With these skills, you will be able to develop and implement machine learning models for financial applications.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use machine learning to solve complex business problems. This course will help you build a foundation in machine learning, operations research, and data analysis. With these skills, you will be able to develop and implement machine learning models for a variety of applications.
Marketing Analyst
Marketing Analyst
Marketing Analysts use machine learning to understand customer behavior and optimize marketing campaigns. This course will help you build a foundation in machine learning, marketing, and data analysis. With these skills, you will be able to develop and implement machine learning models for marketing applications.
Risk Analyst
Risk Analyst
Risk Analysts use machine learning to identify and mitigate risks. This course will help you build a foundation in machine learning, risk management, and data analysis. With these skills, you will be able to develop and implement machine learning models for risk management applications.
For more career information including salaries, visit:
OpenCourser.com/course/lx0699/introduction
Reading list
We've selected 11 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
Introduction to Machine Learning.
Comprehensive textbook on pattern recognition and machine learning. It covers a wide range of topics, including supervised and unsupervised learning, feature extraction, and model selection. It valuable resource for students and researchers who want to gain a deep understanding of machine learning.
Classic textbook on reinforcement learning. It provides a comprehensive overview of the field, covering topics such as Markov decision processes, dynamic programming, and Monte Carlo methods. It valuable resource for researchers and practitioners who want to understand the foundations of reinforcement learning.
Save
Provides a comprehensive overview of generative adversarial networks (GANs). It covers a wide range of topics, including GAN architectures, training techniques, and applications. It valuable resource for researchers and practitioners who want to understand the state-of-the-art in GANs.
Practical guide to machine learning with Python, covering topics such as data preprocessing, model training, and evaluation. It great resource for beginners who want to get started with machine learning.
Collection of recipes for solving machine learning problems with Python. It covers a wide range of topics, including data preprocessing, model training, and evaluation. It valuable resource for Python developers who want to learn how to use machine learning to solve real-world problems.
Practical guide to deep learning with Fastai and PyTorch. It covers topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for Python developers who want to learn how to use deep learning to solve real-world problems.
Provides a hands-on introduction to machine learning with Python libraries such as Scikit-Learn, Keras, and TensorFlow. It covers a wide range of topics, including data preprocessing, model training, and evaluation.
Practical guide to machine learning with R. It covers topics such as data preprocessing, model training, and evaluation, and provides hands-on exercises to help readers learn the concepts.
Practical guide to machine learning for hackers and data scientists. It covers topics such as data preprocessing, model training, and evaluation, and provides hands-on exercises to help readers learn the concepts.
Save
Gentle introduction to machine learning for beginners. It covers the basics of machine learning, including supervised and unsupervised learning, and provides hands-on exercises to help readers understand the concepts.
Save
Provides an overview of machine learning for lawyers. It covers topics such as the ethical and legal implications of machine learning, and provides guidance on how to use machine learning in the legal field. It valuable resource for lawyers who want to understand the potential of machine learning and how it can be used to improve legal practice.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/lx0699/introduction
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Effort
4 weeks
Via
Udacity
Instructor
Matt Maybeno
Language
English
Good to know
Teaches students how to use AWS Sagemaker to access data and perform data analysis, which is used extensively in the industry
Teaches students how to differentiate between supervised vs. unsupervised ML, regression methods, and classification methods, which are core concepts in machine learning
Develops skills in using AWS SageMaker JumpStart, which is a popular tool for building and deploying machine learning models
Courses taught by Matt Maybeno, a recognized expert in machine learning
Taught by Udacity, a reputable provider of online courses
Offers hands-on labs and interactive materials, which can help students learn the material more effectively
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