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Dr. Nick Feamster
This course gives you a comprehensive introduction to both the theory and practice of machine learning. You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a wide variety of techniques. Those techniques include linear regression with ordinary least squares, logistic regression, support vector machines, decision trees and ensembles, clustering, principal component analysis, hidden Markov models, and deep learning.
Read more
This course gives you a comprehensive introduction to both the theory and practice of machine learning. You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a wide variety of techniques. Those techniques include linear regression with ordinary least squares, logistic regression, support vector machines, decision trees and ensembles, clustering, principal component analysis, hidden Markov models, and deep learning.
Read more
This course gives you a comprehensive introduction to both the theory and practice of machine learning. You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a wide variety of techniques. Those techniques include linear regression with ordinary least squares, logistic regression, support vector machines, decision trees and ensembles, clustering, principal component analysis, hidden Markov models, and deep learning.
A key feature of this course is that you not only learn how to apply these techniques, you also learn the conceptual basis underlying them so that you understand how they work, why you are doing what you are doing, and what your results mean. The course also features real-world datasets, drawn primarily from the realm of public policy. It is based on an introductory machine learning course offered to graduate students at the University of Chicago and will serve as a strong foundation for deeper and more specialized study.
Register for this course and see more details by visiting:
OpenCourser.com/course/ezm869/machine
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What's inside
Syllabus
Machine Learning and the Machine Learning Pipeline
In this module you will be introduced to the machine-learning pipeline and learn about the initial work on your data that you need to do prior to modeling. You will learn about how to ingest data using Pandas, a standard Python library for data exploration and preparation. Next, we turn to the first approach to modeling that we explore in this class, linear regression with ordinary least squares.
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Syllabus
Machine Learning and the Machine Learning Pipeline
In this module you will be introduced to the machine-learning pipeline and learn about the initial work on your data that you need to do prior to modeling. You will learn about how to ingest data using Pandas, a standard Python library for data exploration and preparation. Next, we turn to the first approach to modeling that we explore in this class, linear regression with ordinary least squares.
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Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Introduces learners to the conceptual underpinnings of machine learning, so they understand not only how to use techniques but why
Uses Pandas, a popular Python library in industry, for data prep, making skills very relevant to industry
Taught by a professor at the University of Chicago, a highly respected academic program in computer science, building confidence in the course materials
Develops skills and knowledge important to public policy, a field with growing demand for data analysis
Builds a strong foundation in machine learning for those who want to move on to more advanced studies
May not be suitable for beginners without some prior background knowledge in computer science and statistics
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Reviews summary
Rigorous machine learning: theory to application
According to students, this course offers a highly comprehensive and rigorous introduction to machine learning, effectively blending theoretical concepts with practical Python implementations. Learners consistently praise the instructors' ability to explain complex topics clearly, providing a strong conceptual understanding alongside the use of industry-standard libraries like Scikit-learn and TensorFlow. While many find the assignments challenging yet highly rewarding, some mention that the prerequisites for math and statistics may be higher than expected, making it potentially less suitable for absolute beginners. The course's use of real-world datasets from public policy is seen as both a strength for its relevance and a niche for those with different interests.
Utilizes real-world datasets, particularly from public policy, appealing to specific interests.
"The real-world datasets from public policy made the material incredibly relevant to my work."
"While the public policy datasets were unique, I personally found them less engaging for my finance interests."
"I appreciated the practical application focus, even if the public policy context wasn't directly my field."
Assignments are challenging and effective in solidifying learning.
"The assignments are challenging yet highly rewarding. They truly solidify the learning experience."
"The hands-on coding and projects are the strongest part of the course for me, practical and well-designed."
"I learned so much by actively implementing the concepts through the well-structured programming tasks."
Instructors excel at explaining complex topics with clarity and detail.
"The instructors explain complex topics clearly, and the lectures are incredibly detailed and well-structured."
"The instructor's passion for the subject shines through every lecture. A masterpiece for anyone serious about ML."
"I found the explanations of difficult concepts like maximum likelihood and regularization surprisingly easy to follow."
Masterfully combines conceptual understanding with hands-on application.
"The way the theoretical concepts are blended with practical Python implementations using TensorFlow and Scikit-learn is brilliant."
"The focus on conceptual understanding before diving into code is a game-changer. They make you understand the 'why' behind it."
"I deeply appreciate how the course ensures you grasp the 'how' and 'why' behind each algorithm before coding."
Varied opinions on pacing, with some finding it inconsistent or too academic.
"A decent introduction. Sometimes the theoretical explanations felt a bit rushed, and the pacing felt inconsistent."
"I found it to be quite dry and academic. While the content is solid, the delivery could be more engaging."
"Good for establishing concepts, but if you're looking for heavy coding practice, you might need extra work."
A rigorous course requiring solid foundational knowledge, not for true beginners.
"I struggled a lot. The prerequisites for math and statistics were significantly higher than stated, not suitable for true beginners."
"This course is tough but extremely valuable. If you lack a strong analytical background, be prepared to put in extra effort."
"While comprehensive, I felt the pace was challenging, especially if you're not already comfortable with advanced math."
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 Machine Learning: Concepts and Applications with these
activities:
Review different types of machine learning algorithms
Show steps
Identify and practice skills in different types of machine learning algorithms to prepare for this course's lecture materials.
Browse courses on
Machine Learning Algorithms
Show steps
-
Identify the main types of machine learning algorithms, such as supervised, unsupervised, and reinforcement learning.
-
Understand the differences between different types of supervised learning algorithms, such as linear regression, logistic regression, and decision trees.
-
Get familiar with different types of unsupervised learning algorithms, such as clustering, dimensionality reduction, and anomaly detection.
Practice Python coding
Show steps
Ensure familiarity and comfort with Python coding syntax, variable types, and data structures.
Browse courses on
Python
Show steps
-
Review Python syntax and data types
-
Solve coding exercises on platforms like HackerRank or CodeChef
Review basic probability and statistics
Show steps
Strengthen understanding of fundamental concepts like probability distributions, statistical inference, and hypothesis testing.
Browse courses on
Probability
Show steps
-
Review lecture notes or textbooks on basic probability and statistics
-
Practice solving probability and statistics problems
Six other activities
Expand to see all activities and additional details
Show all nine activities
Solve practice problems from the course textbook
Show steps
Apply course concepts and techniques to real-world scenarios, reinforcing understanding and identifying areas for improvement.
Show steps
-
Work through practice problems at the end of each textbook chapter
-
Compare solutions with classmates or online forums
Follow online tutorials on machine learning algorithms
Show steps
Gain hands-on experience implementing machine learning algorithms, enhancing practical skills and deepening conceptual understanding.
Show steps
-
Identify reputable online tutorials on specific machine learning algorithms
-
Follow the tutorials, implementing the algorithms and testing them on sample datasets
Participate in study groups with classmates
Show steps
Foster collaboration and improve understanding through group discussions, diverse perspectives, and peer support.
Show steps
-
Form a study group with 3-5 classmates
-
Meet regularly to discuss course material, solve problems, and quiz each other
Create visualizations to explain machine learning concepts
Show steps
Demonstrate understanding of machine learning concepts by creating visual representations, improving retention and communication skills.
Show steps
-
Choose a machine learning concept to explain
-
Design a visualization that effectively conveys the concept
-
Create the visualization using tools like Tableau or Power BI
Participate in Kaggle competitions
Show steps
Test skills and knowledge in a competitive environment, fostering problem-solving abilities and exposure to real-world machine learning applications.
Show steps
-
Identify a Kaggle competition that aligns with course topics
-
Build a machine learning model and submit predictions
-
Analyze results and compare them with other participants
Contribute to open-source machine learning projects
Show steps
Gain practical experience and contribute to the machine learning community by collaborating on open-source projects.
Show steps
-
Identify open-source machine learning projects that interest you
-
Find a way to contribute, such as fixing bugs, adding new features, or improving documentation
-
Submit your contributions to the project repository
Review different types of machine learning algorithms
Show steps
Identify and practice skills in different types of machine learning algorithms to prepare for this course's lecture materials.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Identify the main types of machine learning algorithms, such as supervised, unsupervised, and reinforcement learning.
- Understand the differences between different types of supervised learning algorithms, such as linear regression, logistic regression, and decision trees.
- Get familiar with different types of unsupervised learning algorithms, such as clustering, dimensionality reduction, and anomaly detection.
Practice Python coding
Show steps
Ensure familiarity and comfort with Python coding syntax, variable types, and data structures.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review Python syntax and data types
- Solve coding exercises on platforms like HackerRank or CodeChef
Review basic probability and statistics
Show steps
Strengthen understanding of fundamental concepts like probability distributions, statistical inference, and hypothesis testing.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review lecture notes or textbooks on basic probability and statistics
- Practice solving probability and statistics problems
Six other activities
Expand to see all activities and additional details
Show all nine activities
Solve practice problems from the course textbook
Show steps
Apply course concepts and techniques to real-world scenarios, reinforcing understanding and identifying areas for improvement.
Show steps
Show steps
Show steps
- Work through practice problems at the end of each textbook chapter
- Compare solutions with classmates or online forums
Follow online tutorials on machine learning algorithms
Show steps
Gain hands-on experience implementing machine learning algorithms, enhancing practical skills and deepening conceptual understanding.
Show steps
Show steps
Show steps
- Identify reputable online tutorials on specific machine learning algorithms
- Follow the tutorials, implementing the algorithms and testing them on sample datasets
Participate in study groups with classmates
Show steps
Foster collaboration and improve understanding through group discussions, diverse perspectives, and peer support.
Show steps
Show steps
Show steps
- Form a study group with 3-5 classmates
- Meet regularly to discuss course material, solve problems, and quiz each other
Create visualizations to explain machine learning concepts
Show steps
Demonstrate understanding of machine learning concepts by creating visual representations, improving retention and communication skills.
Show steps
Show steps
Show steps
- Choose a machine learning concept to explain
- Design a visualization that effectively conveys the concept
- Create the visualization using tools like Tableau or Power BI
Participate in Kaggle competitions
Show steps
Test skills and knowledge in a competitive environment, fostering problem-solving abilities and exposure to real-world machine learning applications.
Show steps
Show steps
Show steps
- Identify a Kaggle competition that aligns with course topics
- Build a machine learning model and submit predictions
- Analyze results and compare them with other participants
Contribute to open-source machine learning projects
Show steps
Gain practical experience and contribute to the machine learning community by collaborating on open-source projects.
Show steps
Show steps
Show steps
- Identify open-source machine learning projects that interest you
- Find a way to contribute, such as fixing bugs, adding new features, or improving documentation
- Submit your contributions to the project repository
Career center
Learners who complete Machine Learning: Concepts and Applications will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist uses machine learning to solve real-world problems. This course provides a comprehensive introduction to machine learning, including both the theory and practice. You will learn how to use Python along with industry-standard libraries and tools to ingest, explore, and prepare data for modeling, and then train and evaluate models using a wide variety of techniques.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models. This course provides a comprehensive introduction to machine learning, including both the theory and practice. You will learn how to use Python along with industry-standard libraries and tools to ingest, explore, and prepare data for modeling, and then train and evaluate models using a wide variety of techniques.
Data Analyst
Data Analyst
A Data Analyst uses data to solve business problems. This course provides a comprehensive introduction to machine learning, including both the theory and practice. You will learn how to use Python along with industry-standard libraries and tools to ingest, explore, and prepare data for modeling, and then train and evaluate models using a wide variety of techniques.
Business Analyst
Business Analyst
A Business Analyst uses data to identify and solve business problems. This course provides a comprehensive introduction to machine learning, including both the theory and practice. You will learn how to use Python along with industry-standard libraries and tools to ingest, explore, and prepare data for modeling, and then train and evaluate models using a wide variety of techniques.
Product Manager
Product Manager
A Product Manager uses data to make decisions about product development and marketing. This course provides a comprehensive introduction to machine learning, including both the theory and practice. You will learn how to use Python along with industry-standard libraries and tools to ingest, explore, and prepare data for modeling, and then train and evaluate models using a wide variety of techniques.
Consultant
Consultant
A Consultant uses data to help clients solve problems. This course provides a comprehensive introduction to machine learning, including both the theory and practice. You will learn how to use Python along with industry-standard libraries and tools to ingest, explore, and prepare data for modeling, and then train and evaluate models using a wide variety of techniques.
Statistician
Statistician
A Statistician uses data to solve problems. This course provides a comprehensive introduction to machine learning, including both the theory and practice. You will learn how to use Python along with industry-standard libraries and tools to ingest, explore, and prepare data for modeling, and then train and evaluate models using a wide variety of techniques.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and deploys software. This course provides a comprehensive introduction to machine learning, including both the theory and practice. You will learn how to use Python along with industry-standard libraries and tools to ingest, explore, and prepare data for modeling, and then train and evaluate models using a wide variety of techniques.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses data to make investment decisions. This course provides a comprehensive introduction to machine learning, including both the theory and practice. You will learn how to use Python along with industry-standard libraries and tools to ingest, explore, and prepare data for modeling, and then train and evaluate models using a wide variety of techniques.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses data to solve problems in business and industry. This course provides a comprehensive introduction to machine learning, including both the theory and practice. You will learn how to use Python along with industry-standard libraries and tools to ingest, explore, and prepare data for modeling, and then train and evaluate models using a wide variety of techniques.
Actuary
Actuary
An Actuary uses data to assess risk and uncertainty. This course provides a comprehensive introduction to machine learning, including both the theory and practice. You will learn how to use Python along with industry-standard libraries and tools to ingest, explore, and prepare data for modeling, and then train and evaluate models using a wide variety of techniques.
Economist
Economist
An Economist uses data to study the economy. This course provides a comprehensive introduction to machine learning, including both the theory and practice. You will learn how to use Python along with industry-standard libraries and tools to ingest, explore, and prepare data for modeling, and then train and evaluate models using a wide variety of techniques.
Financial Analyst
Financial Analyst
A Financial Analyst uses data to make investment decisions. This course provides a comprehensive introduction to machine learning, including both the theory and practice. You will learn how to use Python along with industry-standard libraries and tools to ingest, explore, and prepare data for modeling, and then train and evaluate models using a wide variety of techniques.
Market Research Analyst
Market Research Analyst
A Market Research Analyst uses data to understand consumer behavior. This course provides a comprehensive introduction to machine learning, including both the theory and practice. You will learn how to use Python along with industry-standard libraries and tools to ingest, explore, and prepare data for modeling, and then train and evaluate models using a wide variety of techniques.
User Experience Researcher
User Experience Researcher
A User Experience Researcher uses data to understand how people interact with products and services. This course provides a comprehensive introduction to machine learning, including both the theory and practice. You will learn how to use Python along with industry-standard libraries and tools to ingest, explore, and prepare data for modeling, and then train and evaluate models using a wide variety of techniques.
For more career information including salaries, visit:
OpenCourser.com/course/ezm869/machine
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
Machine Learning: Concepts and Applications.
Provides a comprehensive overview of statistical learning. It covers a wide range of topics from linear regression to support vector machines. It good resource for those who want to understand the theoretical foundations of statistical learning.
Save
Provides a comprehensive overview of deep learning. It covers a wide range of topics from neural networks to convolutional neural networks. It good resource for those who want to learn about the latest developments in deep learning.
Provides a comprehensive overview of machine learning with R. It covers a wide range of topics from supervised learning to unsupervised learning. It good resource for those who want to learn about the latest developments in machine learning with R.
Provides a comprehensive overview of machine learning. It covers a wide range of topics from supervised learning to unsupervised learning. It good resource for those who want to understand the theoretical foundations of machine learning.
Provides a comprehensive overview of deep learning with Python. It covers a wide range of topics from neural networks to convolutional neural networks. It good resource for those who want to learn about the latest developments in deep learning with Python.
Provides a probabilistic perspective on machine learning. It covers a wide range of topics from Bayesian inference to Gaussian processes. It good resource for those who want to understand the theoretical foundations of machine learning.
Provides a comprehensive overview of pattern recognition and machine learning. It covers a wide range of topics from discriminant analysis to Bayesian networks. It good resource for those who want to understand the theoretical foundations of machine learning used in pattern recognition.
Provides a comprehensive overview of machine learning with Scikit-Learn, Keras, and TensorFlow. It covers a wide range of topics from data preprocessing to model evaluation. It good resource for those who want to learn about the latest developments in machine learning with these popular frameworks.
Provides a practical introduction to machine learning. It covers a wide range of topics from data preprocessing to model evaluation. It good resource for those who want to get started with machine learning quickly.
Provides a practical introduction to machine learning. It covers a wide range of topics from data preprocessing to model evaluation. It good resource for those who want to get started with machine learning quickly.
Save
Provides a gentle introduction to machine learning. It covers a wide range of topics from supervised learning to unsupervised learning. It good resource for those who want to understand the basics of machine learning without getting too technical.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ezm869/machine
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Effort
5-8 hours/week
Level
Intermediate
Via
Coursera
Institution
The University of Chicago
Instructor
Dr. Nick Feamster
Language
English
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