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Kevyn Collins-Thompson
This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis.
Read more
This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis.
Read more
This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis.
This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.
Register for this course and see more details by visiting:
OpenCourser.com/course/dezsjg/applied
What's inside
Syllabus
Module 1: Fundamentals of Machine Learning - Intro to SciKit Learn
This module introduces basic machine learning concepts, tasks, and workflow using an example classification problem based on the K-nearest neighbors method, and implemented using the scikit-learn library.
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Syllabus
Module 1: Fundamentals of Machine Learning - Intro to SciKit Learn
This module introduces basic machine learning concepts, tasks, and workflow using an example classification problem based on the K-nearest neighbors method, and implemented using the scikit-learn library.
Read more
Module 2: Supervised Machine Learning - Part 1
This module delves into a wider variety of supervised learning methods for both classification and regression, learning about the connection between model complexity and generalization performance, the importance of proper feature scaling, and how to control model complexity by applying techniques like regularization to avoid overfitting. In addition to k-nearest neighbors, this week covers linear regression (least-squares, ridge, lasso, and polynomial regression), logistic regression, support vector machines, the use of cross-validation for model evaluation, and decision trees.
Module 3: Evaluation
This module covers evaluation and model selection methods that you can use to help understand and optimize the performance of your machine learning models.
Module 4: Supervised Machine Learning - Part 2
This module covers more advanced supervised learning methods that include ensembles of trees (random forests, gradient boosted trees), and neural networks (with an optional summary on deep learning). You will also learn about the critical problem of data leakage in machine learning and how to detect and avoid it.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides end-to-end training in applied machine learning
Suitable for those seeking practical skills in machine learning for project implementation
Is a part of a comprehensive Applied Data Science series of four courses
Focuses on the techniques and methods of machine learning rather than the underlying statistics
Taught by the well-regarded instructors Kevyn Collins-Thompson
Requires prior knowledge in data science and Python
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Reviews summary
Practical machine learning with python
learners say the Applied Machine Learning in Python course by the University of Michigan is largely positive. They say the course is well received and stands out because it focuses on the practical uses of different machine learning (ML) algorithms and techniques, using Python's scikit-learn library to implement them. This is done through a series of assignments, which many learners remark are challenging but effective in solidifying their understanding of the concepts. Moreover, learners comment favorably on the lectures, saying that they are clear, engaging, and provide a good balance between theory and practice. Additionally, learners appreciate the discussion forums, which they say are active and helpful, thanks to the participation of mentors. However, there are some areas learners feel could be improved. These mainly concern the assignments, which some learners say can be buggy and have errors, leading to frustration. Additionally, some learners wish the autograder provided more feedback to help them understand their mistakes. Others would like to see more in-depth coverage of certain topics, such as feature engineering and model selection. Despite these areas for improvement, learners largely find the Applied Machine Learning in Python course to be a valuable learning experience. They say it has helped them develop a practical understanding of ML algorithms and techniques and would recommend it to others interested in learning more about the field.
Discussion forums are active and helpful, thanks to the participation of mentors.
"The discussion forums are active and helpful, thanks to the participation of mentors."
Course focuses on the practical uses of different machine learning (ML) algorithms and techniques.
"The course focuses on the practical uses of different machine learning (ML) algorithms and techniques."
Lectures are clear, engaging, and provide a good balance between theory and practice.
"The lectures are clear and engaging."
"The lectures provide a good balance between theory and practice."
Assignments are challenging but effective in solidifying understanding of concepts.
"The assignments are challenging but effective in solidifying understanding of concepts."
Some learners wish to see more in-depth coverage of certain topics.
"Some learners wish to see more in-depth coverage of certain topics."
Autograder could provide more feedback to help learners understand mistakes.
"Autograder could provide more feedback to help learners understand mistakes."
Assignments can be buggy and have errors, leading to frustration.
"Assignments can be buggy and have errors, leading to frustration."
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 Applied Machine Learning in Python with these
activities:
Rifle scikit-learn tutorials
Show steps
Become familiar with the scikit-learn Python library.
Browse courses on
scikit-learn
Show steps
-
Follow along with basic scikit-learn tutorials to get started.
-
Try more advanced tutorials to deepen your understanding.
Read Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Show steps
Gain a comprehensive understanding of machine learning concepts and techniques.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
-
Read the book thoroughly and take notes on key concepts.
-
Work through the exercises and examples provided in the book.
-
Use the knowledge gained from the book to enhance your understanding of the course material.
Seek guidance from experienced machine learning professionals
Show steps
Gain personalized guidance and support from experts in the field.
Browse courses on
Mentorship
Show steps
-
Identify potential mentors through online platforms or professional networks.
-
Reach out to mentors and request their guidance.
-
Meet with mentors regularly to discuss your progress and challenges.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Follow online tutorials
Show steps
Follow online tutorials to learn new machine learning concepts and techniques.
Browse courses on
Machine Learning
Show steps
-
Find a tutorial that covers a topic you're interested in.
-
Follow the steps in the tutorial.
-
Make sure you understand the concepts and techniques being taught.
-
Apply what you've learned to your own projects.
Join a study group
Show steps
Join a study group to discuss machine learning concepts and techniques with other students.
Browse courses on
Machine Learning
Show steps
-
Find a study group that meets your needs.
-
Attend study group meetings regularly.
-
Participate in discussions and ask questions.
-
Help other students learn.
Apply supervised learning techniques in Python
Show steps
Practice applying supervised learning techniques using Python.
Browse courses on
Supervised Learning
Show steps
-
Complete coding exercises that involve building and evaluating supervised learning models.
-
Participate in online coding challenges to test your skills.
-
Create small projects that utilize supervised learning for practical applications.
Practice coding problems
Show steps
Practice coding problems to improve your understanding of machine learning algorithms and techniques.
Browse courses on
Machine Learning
Show steps
-
Find coding problems online or in a textbook.
-
Read the problem description and make sure you understand the problem.
-
Write a solution to the problem.
-
Test your solution to make sure it works.
-
Review your solution and identify any areas where you can improve.
Write a blog post or article
Show steps
Write a blog post or article to share your knowledge and insights about machine learning.
Browse courses on
Machine Learning
Show steps
-
Choose a topic to write about.
-
Research your topic and gather information.
-
Write a draft of your post or article.
-
Edit and revise your post or article.
-
Publish your post or article.
Build a machine learning model for a real-world problem
Show steps
Gain hands-on experience in applying machine learning to solve real-world problems.
Browse courses on
Machine Learning
Show steps
-
Identify a suitable problem and gather the necessary data.
-
Choose appropriate machine learning algorithms and train models.
-
Evaluate model performance and refine your approach.
-
Deploy the final model and share your results.
-
Document your work in a project report or portfolio.
Build a machine learning project
Show steps
Build a machine learning project to apply your knowledge and skills to a real-world problem.
Browse courses on
Machine Learning
Show steps
-
Define the problem you want to solve.
-
Gather the data you need.
-
Clean and prepare the data.
-
Build a machine learning model.
-
Evaluate and improve your model.
Contribute to open-source machine learning projects
Show steps
Gain practical experience and contribute to the machine learning community.
Browse courses on
Open Source
Show steps
-
Find open-source machine learning projects that align with your interests.
-
Review the project documentation and codebase.
-
Identify areas where you can contribute, such as bug fixes or feature enhancements.
-
Submit your contributions for review and merge.
-
Collaborate with other contributors and learn from their expertise.
Rifle scikit-learn tutorials
Show steps
Become familiar with the scikit-learn Python library.
Browse courses on
scikit-learn
Show steps
Show steps
Show steps
- Follow along with basic scikit-learn tutorials to get started.
- Try more advanced tutorials to deepen your understanding.
Read Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Show steps
Gain a comprehensive understanding of machine learning concepts and techniques.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
Show steps
Show steps
- Read the book thoroughly and take notes on key concepts.
- Work through the exercises and examples provided in the book.
- Use the knowledge gained from the book to enhance your understanding of the course material.
Seek guidance from experienced machine learning professionals
Show steps
Gain personalized guidance and support from experts in the field.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Identify potential mentors through online platforms or professional networks.
- Reach out to mentors and request their guidance.
- Meet with mentors regularly to discuss your progress and challenges.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Follow online tutorials
Show steps
Follow online tutorials to learn new machine learning concepts and techniques.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a tutorial that covers a topic you're interested in.
- Follow the steps in the tutorial.
- Make sure you understand the concepts and techniques being taught.
- Apply what you've learned to your own projects.
Join a study group
Show steps
Join a study group to discuss machine learning concepts and techniques with other students.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a study group that meets your needs.
- Attend study group meetings regularly.
- Participate in discussions and ask questions.
- Help other students learn.
Apply supervised learning techniques in Python
Show steps
Practice applying supervised learning techniques using Python.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Complete coding exercises that involve building and evaluating supervised learning models.
- Participate in online coding challenges to test your skills.
- Create small projects that utilize supervised learning for practical applications.
Practice coding problems
Show steps
Practice coding problems to improve your understanding of machine learning algorithms and techniques.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find coding problems online or in a textbook.
- Read the problem description and make sure you understand the problem.
- Write a solution to the problem.
- Test your solution to make sure it works.
- Review your solution and identify any areas where you can improve.
Write a blog post or article
Show steps
Write a blog post or article to share your knowledge and insights about machine learning.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a topic to write about.
- Research your topic and gather information.
- Write a draft of your post or article.
- Edit and revise your post or article.
- Publish your post or article.
Build a machine learning model for a real-world problem
Show steps
Gain hands-on experience in applying machine learning to solve real-world problems.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify a suitable problem and gather the necessary data.
- Choose appropriate machine learning algorithms and train models.
- Evaluate model performance and refine your approach.
- Deploy the final model and share your results.
- Document your work in a project report or portfolio.
Build a machine learning project
Show steps
Build a machine learning project to apply your knowledge and skills to a real-world problem.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Define the problem you want to solve.
- Gather the data you need.
- Clean and prepare the data.
- Build a machine learning model.
- Evaluate and improve your model.
Contribute to open-source machine learning projects
Show steps
Gain practical experience and contribute to the machine learning community.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Find open-source machine learning projects that align with your interests.
- Review the project documentation and codebase.
- Identify areas where you can contribute, such as bug fixes or feature enhancements.
- Submit your contributions for review and merge.
- Collaborate with other contributors and learn from their expertise.
Career center
Learners who complete Applied Machine Learning in Python will develop knowledge and skills
that may be useful to these careers:
Machine Learning Researcher
Machine Learning Researcher
The Applied Machine Learning in Python course offered by the University of Michigan can be a valuable resource for Machine Learning Researchers. This course provides a comprehensive overview of machine learning techniques, their mathematical foundations, and their application in real-world scenarios. By delving into advanced topics such as neural networks and ensembles, Machine Learning Researchers can enhance their understanding and ability to develop innovative machine learning algorithms and models.
Machine Learning Engineer
Machine Learning Engineer
Individuals pursuing a career as a Machine Learning Engineer will find the Applied Machine Learning in Python course highly beneficial. This course provides a solid foundation in machine learning concepts and techniques, enabling learners to design, implement, and evaluate machine learning solutions. The emphasis on supervised and unsupervised learning methods and model evaluation aligns well with the responsibilities of Machine Learning Engineers, who are tasked with developing and deploying machine learning systems.
Quantitative Analyst
Quantitative Analyst
Aspiring Quantitative Analysts can benefit from the Applied Machine Learning in Python course, which provides a solid foundation in machine learning techniques commonly used in financial analysis. This course covers supervised and unsupervised learning methods, model evaluation, and feature engineering, equipping learners with the skills needed to develop and implement machine learning models for financial applications, such as risk assessment, portfolio optimization, and algorithmic trading.
Data Scientist
Data Scientist
The Applied Machine Learning in Python course from the University of Michigan can be a valuable resource for aspiring Data Scientists. This course provides a comprehensive overview of machine learning techniques and methods, equipping learners with the skills needed to identify and apply appropriate algorithms to real-world data sets. The course's focus on practical applications and implementation using Python makes it particularly relevant for Data Scientists who need to develop and deploy machine learning models in various industries.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
The Applied Machine Learning in Python course can provide a solid foundation for aspiring Artificial Intelligence Engineers. This course introduces learners to machine learning concepts, techniques, and their implementation using Python. By gaining a comprehensive understanding of machine learning, Artificial Intelligence Engineers can develop and implement AI solutions to solve complex problems in various industries, such as healthcare, finance, and manufacturing.
Data Analyst
Data Analyst
The Applied Machine Learning in Python course offered by the University of Michigan can be a valuable asset for aspiring Data Analysts. This course introduces learners to machine learning techniques that are commonly used in data analysis, such as clustering, classification, and regression. By understanding these methods and their implementation using Python, Data Analysts can enhance their ability to extract meaningful insights from complex data sets and make data-driven decisions.
Actuary
Actuary
The Applied Machine Learning in Python course offered by the University of Michigan can be beneficial for Actuaries seeking to incorporate machine learning into their practice. This course provides a foundational understanding of machine learning techniques and their application in various domains, including insurance and finance. By learning how to develop and evaluate machine learning models, Actuaries can enhance their ability to assess risk, develop pricing models, and make data-driven decisions.
Natural Language Processing Engineer
Natural Language Processing Engineer
The Applied Machine Learning in Python course offered by the University of Michigan can be beneficial for Natural Language Processing Engineers. This course provides a solid foundation in machine learning techniques commonly used in NLP, such as text classification, sentiment analysis, and machine translation. By understanding how to develop and evaluate machine learning models for NLP tasks, Natural Language Processing Engineers can contribute to the development of innovative applications in fields such as chatbots, language translation, and text summarization.
Statistician
Statistician
The Applied Machine Learning in Python course can be a useful resource for Statisticians interested in expanding their knowledge and skills in machine learning. This course provides a practical introduction to machine learning techniques, with a focus on their application in real-world scenarios. By understanding how to leverage machine learning to analyze data and extract meaningful insights, Statisticians can enhance their ability to solve complex problems and contribute to data-driven decision-making.
Computer Vision Engineer
Computer Vision Engineer
The Applied Machine Learning in Python course can provide a foundation for individuals interested in pursuing a career as a Computer Vision Engineer. This course introduces learners to machine learning techniques commonly used in computer vision, such as image classification, object detection, and facial recognition. By understanding how to develop and evaluate machine learning models for computer vision tasks, Computer Vision Engineers can contribute to the development of innovative applications in fields such as autonomous driving, medical imaging, and robotics.
Business Analyst
Business Analyst
For individuals seeking a career as a Business Analyst, the Applied Machine Learning in Python course can provide a competitive edge. This course equips learners with the skills to apply machine learning techniques to business problems, such as customer segmentation, fraud detection, and predictive analytics. By understanding how to leverage machine learning to analyze data and identify trends, Business Analysts can add value to organizations by providing data-driven insights and recommendations.
Data Engineer
Data Engineer
The Applied Machine Learning in Python course can provide a valuable foundation for individuals pursuing a career as a Data Engineer. This course introduces learners to machine learning techniques and their implementation using Python, a widely used language in data engineering. By understanding how to leverage machine learning to automate data processing tasks, such as data cleaning, feature engineering, and model deployment, Data Engineers can enhance the efficiency and effectiveness of their data pipelines.
Product Manager
Product Manager
The Applied Machine Learning in Python course offered by the University of Michigan can be beneficial for Product Managers seeking to gain a foundational understanding of machine learning and its applications in product development. This course provides learners with the knowledge and skills to evaluate and incorporate machine learning models into products, enabling them to create innovative and data-driven solutions that meet customer needs.
Research Scientist
Research Scientist
The Applied Machine Learning in Python course may be useful for Research Scientists seeking to expand their knowledge and skills in machine learning. This course provides a comprehensive overview of machine learning techniques and their application in various research domains. By understanding how to develop and evaluate machine learning models, Research Scientists can enhance their ability to conduct data-driven research and make significant contributions to their respective fields.
Software Engineer
Software Engineer
The Applied Machine Learning in Python course may be beneficial for Software Engineers who are interested in incorporating machine learning into their software applications. This course offers a practical introduction to machine learning concepts and techniques, allowing Software Engineers to gain a foundational understanding of how to design, implement, and evaluate machine learning models. The course's focus on Python, a popular programming language used in software development, makes it particularly relevant for Software Engineers.
For more career information including salaries, visit:
OpenCourser.com/course/dezsjg/applied
Featured in The Course Notes
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The Course Notes. Read
one article that features
Applied Machine Learning in Python:
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Reading list
We've selected 25 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
Applied Machine Learning in Python.
Provides a comprehensive overview of machine learning from a probabilistic perspective, and covers topics such as supervised and unsupervised learning, model selection, and Bayesian inference.
Provides a comprehensive overview of pattern recognition and machine learning techniques, and covers topics such as supervised and unsupervised learning, model selection, and Bayesian inference.
Covers a range of foundational machine learning topics, from gaining knowledge about data preprocessing to evaluating and deploying learning algorithms. The second edition also gives an introduction to TensorFlow and Keras.
Provides a practical introduction to machine learning using the scikit-learn, Keras, and TensorFlow libraries, and covers topics such as data preprocessing, model training, and evaluation.
Highly practical and hands-on, this book offers comprehensive coverage of using the various machine learning libraries such as scikit-learn in Python.
Provides a comprehensive overview of statistical learning methods, and covers topics such as supervised and unsupervised learning, model selection, and regularization.
Provides a comprehensive overview of advanced machine learning techniques, and covers topics such as deep learning, reinforcement learning, and natural language processing.
A highly practical guide to machine learning and deep learning with Python, offering a comprehensive overview of the field.
A textbook-style book covering the fundamentals of machine learning in detail.
Concise and practical guide to ML and deep learning. It covers both the theoretical fundamentals and the practical aspects.
Provides a gentle introduction to machine learning using Python, and covers topics such as supervised and unsupervised learning, feature engineering, and model evaluation.
Save
Provides a comprehensive overview of data mining techniques, and covers topics such as data preprocessing, clustering, classification, and regression.
Provides a practical introduction to machine learning using the Python programming language, and covers topics such as data preprocessing, model training, and evaluation.
Provides a gentle introduction to machine learning using the Python programming language, and covers topics such as supervised and unsupervised learning, feature engineering, and model evaluation.
Provides a practical introduction to machine learning using the R programming language, and covers topics such as data preprocessing, model training, and evaluation.
Provides a practical introduction to deep learning using the Python programming language, and covers topics such as neural networks, convolutional neural networks, and recurrent neural networks.
Provides a practical introduction to natural language processing using the Python programming language, and covers topics such as text preprocessing, natural language understanding, and natural language generation.
Provides a practical introduction to machine learning for hackers, and covers topics such as data preprocessing, model training, and evaluation.
Despite not being directly aligned with this course, it provides valuable knowledge about feature engineering, an essential aspect of machine learning.
Save
Despite not being directly applicable to this course, this book provides a valuable reference for understanding deep learning techniques.
Provides a gentle introduction to machine learning for beginners, and covers topics such as supervised and unsupervised learning, feature engineering, and model evaluation.
Python lib for data manipulation, analysis and visualization.
Provides a solid foundation to solve common business problems through data science and ML.
Covers a wide range of machine learning algorithms and their mathematical foundations.
Focuses on training models with little or no human intervention. It discusses the AutoML techniques, including Hyperparameter Optimization, Neural Architecture Search, and Transfer Learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/dezsjg/applied
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This course belongs to a
collection
called
Applied Data Science with Python. It's comprised of five courses:
- Applied Text Mining in Python
- Introduction to Data Science in Python
- Applied Social Network Analysis in Python
- Applied Machine Learning in Python (viewing)
- Applied Plotting, Charting & Data Representation in Python
Good to know
Provides end-to-end training in applied machine learning
Suitable for those seeking practical skills in machine learning for project implementation
Is a part of a comprehensive Applied Data Science series of four courses
Focuses on the techniques and methods of machine learning rather than the underlying statistics
Taught by the well-regarded instructors Kevyn Collins-Thompson
Requires prior knowledge in data science and Python
Featured in The Course Notes
Read
one article that features
this course,
Applied Machine Learning in Python
from our blog, The Course Notes:
To read more articles from OpenCourser, visit:
OpenCourser.com/notes
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