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David Carlson,
Timothy Dunn,
Kevin Liang,
and
Lawrence Carin
This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction. In addition, we have designed practice exercises that will give you hands-on experience implementing these data science models on data sets. These practice exercises will teach you how to implement machine learning algorithms with PyTorch, open source libraries used by leading tech companies in the machine learning field (e.g., Google, NVIDIA, CocaCola, eBay, Snapchat, Uber and many more).
Register for this course and see more details by visiting:
OpenCourser.com/course/i97eq2/introduction
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What's inside
Syllabus
Simple Introduction to Machine Learning
The focus of this module is to introduce the concepts of machine learning with as little mathematics as possible. We will introduce basic concepts in machine learning, including logistic regression, a simple but widely employed machine learning (ML) method. Also covered is multilayered perceptron (MLP), a fundamental neural network. The concept of deep learning is discussed, and also related to simpler models.
Read more
Syllabus
Simple Introduction to Machine Learning
The focus of this module is to introduce the concepts of machine learning with as little mathematics as possible. We will introduce basic concepts in machine learning, including logistic regression, a simple but widely employed machine learning (ML) method. Also covered is multilayered perceptron (MLP), a fundamental neural network. The concept of deep learning is discussed, and also related to simpler models.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Explores models utilized in numerous industries, enhancing learners' potential to solve complex problems
Taught by recognized experts in the field, providing learners with access to cutting-edge knowledge and industry insights
Emphasizes practical application through hands-on exercises, fostering learners' ability to implement machine learning algorithms
Covers a comprehensive range of machine learning models, from basic to advanced, ensuring a solid foundation for learners
Delves into natural language processing techniques, empowering learners to analyze and process textual data with proficiency
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Reviews summary
Foundational machine learning concepts & practice
According to learners, this course provides a solid foundation in core machine learning concepts, covering essential models from logistic regression to Transformer networks. Students frequently highlight the practical hands-on experience gained through well-designed programming exercises using PyTorch, which is seen as a major strength. The curriculum is described as comprehensive for an introductory course. However, some reviewers note that the course can be quite challenging, particularly for those without a strong mathematical or programming background, and suggest having prerequisites in place. Overall, the sentiment is largely positive, with many finding it a valuable step towards a career in the field.
Lectures are generally clear but sometimes dense.
"The lectures are informative and well-structured, explaining complex topics clearly."
"While the instructor is knowledgeable, some parts of the lectures felt a bit rushed or overly theoretical without enough practical context initially."
"I found the explanations of mathematical concepts in the lectures sometimes hard to follow."
"Generally good explanations, but reviewing materials multiple times was often necessary."
Content covers modern ML topics like Transformers.
"I was impressed that the course included modern topics like the Transformer network, which is very relevant today."
"It feels current and covers models that are actually used in the industry now."
"Good to see newer concepts integrated into an introductory course."
"The curriculum seems to be updated to include recent advancements in the field."
Hands-on labs and coding assignments are valuable.
"The hands-on coding and projects are the strongest part of the course for me. Working with PyTorch was invaluable."
"I appreciated the practical exercises that allowed me to implement the concepts learned in lectures."
"The assignments really reinforced the material and gave me confidence in my coding skills for ML."
"Implementing algorithms in PyTorch was a great way to apply the theory and see how it works."
Highly relevant for ML/Data Science career goals.
"This course is a great stepping stone for anyone looking to get into machine learning or data science careers."
"It provides the necessary theoretical background and practical skills needed for entry-level roles."
"I feel much more prepared for technical interviews after completing this course."
"Definitely recommend this if your goal is to work in the ML field."
Comprehensive introduction to core ML topics.
"This course covers a lot of ground, from basic regression to cutting-edge Transformers and RL. It gives a solid overview."
"I feel like I got a really good grasp of the fundamental concepts needed for machine learning from this course."
"The syllabus is impressive, touching upon key areas of ML necessary for beginners."
"It really helped solidify my understanding of various model types, covering the essentials well."
Can be difficult without prior math/coding.
"Be warned, this course is not easy if you don't have a strong background in math and Python programming."
"I struggled with some of the mathematical details and coding assignments, assuming more prerequisites."
"The pace is fast, and without solid fundamentals, it's easy to get lost."
"It requires dedication and possibly supplementary learning if you're completely new to the field."
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:
Python Review
Show steps
Review the fundamentals of Python programming to strengthen your foundation for the course.
Browse courses on
Python Programming
Show steps
-
Review basic data types and operators
-
Practice using control flow statements
-
Implement basic algorithms using Python syntax
Deep Learning with PyTorch
Show steps
Supplement your understanding of PyTorch and deep learning concepts by reviewing this comprehensive guide.
View
Deep Learning with PyTorch: Build, train, and...
on Amazon
Show steps
-
Read the introductory chapters to grasp the fundamentals
-
Review specific sections relevant to the course material
Study Group Discussions
Show steps
Engage in regular discussions with classmates to clarify concepts, share insights, and support each other's learning.
Browse courses on
Collaborative Learning
Show steps
-
Identify challenging topics and prepare questions
-
Form a study group with peers
-
Meet regularly to discuss course material and share perspectives
Five other activities
Expand to see all activities and additional details
Show all eight activities
Machine Learning Algorithm Implementation
Show steps
Practice implementing various machine learning algorithms in Python to enhance your understanding of their practical applications.
Browse courses on
Machine Learning Algorithms
Show steps
-
Implement a linear regression model using PyTorch
-
Implement a logistic regression model using PyTorch
-
Implement a simple convolutional neural network using PyTorch
Natural Language Processing with Transformers
Show steps
Explore advanced concepts in natural language processing by following tutorials on using transformers for text-related tasks.
Browse courses on
Natural Language Processing
Show steps
-
Follow a tutorial on using Hugging Face's Transformers library
-
Apply transformers to a text classification task
-
Investigate the inner workings of transformer models
Contribute to Open Source Machine Learning Projects
Show steps
Deepen your understanding of machine learning and connect with the community by contributing to open source projects.
Browse courses on
Collaborative Development
Show steps
-
Identify open source projects related to the course topics
-
Choose an issue or feature to work on
-
Submit a pull request with your contribution
Machine Learning Model Deployment
Show steps
Gain practical experience by deploying your trained machine learning model in a real-world environment.
Browse courses on
Model Deployment
Show steps
-
Select a suitable deployment platform
-
Prepare the model for deployment
-
Monitor and evaluate the deployed model
Machine Learning Project
Show steps
深化 your understanding of machine learning by applying it to a real-world problem through a comprehensive project.
Show steps
-
Define the problem statement and gather data
-
Explore different machine learning models and select the most appropriate one
-
Build, train, and evaluate the machine learning model
-
Deploy the model and analyze its performance
Python Review
Show steps
Review the fundamentals of Python programming to strengthen your foundation for the course.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Review basic data types and operators
- Practice using control flow statements
- Implement basic algorithms using Python syntax
Deep Learning with PyTorch
Show steps
Supplement your understanding of PyTorch and deep learning concepts by reviewing this comprehensive guide.
View
Deep Learning with PyTorch: Build, train, and...
on Amazon
Show steps
Show steps
Show steps
- Read the introductory chapters to grasp the fundamentals
- Review specific sections relevant to the course material
Study Group Discussions
Show steps
Engage in regular discussions with classmates to clarify concepts, share insights, and support each other's learning.
Browse courses on
Collaborative Learning
Show steps
Show steps
Show steps
- Identify challenging topics and prepare questions
- Form a study group with peers
- Meet regularly to discuss course material and share perspectives
Five other activities
Expand to see all activities and additional details
Show all eight activities
Machine Learning Algorithm Implementation
Show steps
Practice implementing various machine learning algorithms in Python to enhance your understanding of their practical applications.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Implement a linear regression model using PyTorch
- Implement a logistic regression model using PyTorch
- Implement a simple convolutional neural network using PyTorch
Natural Language Processing with Transformers
Show steps
Explore advanced concepts in natural language processing by following tutorials on using transformers for text-related tasks.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Follow a tutorial on using Hugging Face's Transformers library
- Apply transformers to a text classification task
- Investigate the inner workings of transformer models
Contribute to Open Source Machine Learning Projects
Show steps
Deepen your understanding of machine learning and connect with the community by contributing to open source projects.
Browse courses on
Collaborative Development
Show steps
Show steps
Show steps
- Identify open source projects related to the course topics
- Choose an issue or feature to work on
- Submit a pull request with your contribution
Machine Learning Model Deployment
Show steps
Gain practical experience by deploying your trained machine learning model in a real-world environment.
Browse courses on
Model Deployment
Show steps
Show steps
Show steps
- Select a suitable deployment platform
- Prepare the model for deployment
- Monitor and evaluate the deployed model
Machine Learning Project
Show steps
深化 your understanding of machine learning by applying it to a real-world problem through a comprehensive project.
Show steps
Show steps
Show steps
- Define the problem statement and gather data
- Explore different machine learning models and select the most appropriate one
- Build, train, and evaluate the machine learning model
- Deploy the model and analyze its performance
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
Machine Learning Engineers design, develop, deploy, and manage machine learning models and applications. They work on a variety of projects, from building self-driving cars to improving healthcare diagnostics. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.
Data Scientist
Data Scientist
Data Scientists use machine learning and other data analysis techniques to extract insights from data. They work in a variety of industries, including healthcare, finance, and零售业. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work in a variety of industries, including healthcare, finance, and零售业. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. They work in a variety of industries, including banking, hedge funds, and asset management. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve complex problems in business and industry. They work in a variety of industries, including manufacturing, transportation, and logistics. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.
Business Analyst
Business Analyst
Business Analysts use data analysis and modeling techniques to identify and solve business problems. They work in a variety of industries, including healthcare, finance, and零售业. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.
Consultant
Consultant
Consultants provide advice and expertise to businesses and organizations on a variety of topics, including management, strategy, and finance. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.
Teacher
Teacher
Teachers develop and deliver lesson plans and teach students in a variety of subjects. They work in a variety of settings, including schools, colleges, and universities. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.
Researcher
Researcher
Researchers conduct original research in a variety of fields, including science, engineering, and the social sciences. They work in a variety of settings, including universities, research institutes, and government agencies. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.
Product Manager
Product Manager
Product Managers develop and manage products for a variety of companies. They work in a variety of industries, including technology, healthcare, and manufacturing. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data and make investment recommendations. They work in a variety of industries, including banking, hedge funds, and asset management. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. They work in a variety of industries, including insurance, finance, and healthcare. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They work in a variety of industries, including healthcare, finance, and manufacturing. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.
Economist
Economist
Economists study the production, distribution, and consumption of goods and services. They work in a variety of industries, including government, academia, and the private sector. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.
Market Researcher
Market Researcher
Market Researchers conduct research to identify and understand consumer needs. They work in a variety of industries, including marketing, advertising, and product development. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.
For more career information including salaries, visit:
OpenCourser.com/course/i97eq2/introduction
Reading list
We've selected 13 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.
Save
Comprehensive guide to deep learning, covering the latest research and applications.
Comprehensive guide to machine learning from a probabilistic perspective, covering the latest research and applications.
Comprehensive guide to statistical learning, covering the latest research and applications.
Comprehensive guide to neural networks and deep learning, covering the latest research and applications.
Comprehensive guide to natural language processing with transformers, covering the latest research and applications.
Comprehensive guide to machine learning with Python, covering the latest research and applications.
Hands-on guide to machine learning, using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow.
Comprehensive guide to reinforcement learning, covering the latest research and applications.
Comprehensive guide to machine learning in finance, covering the latest research and applications.
Practical guide to deep learning for coders, using the Fastai and PyTorch libraries.
Comprehensive guide to machine learning with R, covering the latest research and applications.
Practical guide to machine learning, with a focus on real-world applications.
Beginner-friendly guide to machine learning, covering the latest research and applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/i97eq2/introduction
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Rating
4.7
Effort
Approx. 25 hours
Level
Intermediate
Via
Coursera
Institution
Duke University
Instructors
David Carlson
Timothy Dunn
Kevin Liang
Lawrence Carin
Language
English
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