We may earn an affiliate commission when you visit our partners.
Sanjoy Dasgupta
Do you want to build systems that learn from experience? Or exploit data to create simple predictive models of the world?
In this course, part of the Data Science MicroMasters program, you will learn a variety of supervised and unsupervised learning algorithms, and the theory behind those algorithms.
Using real-world case studies, you will learn how to classify images, identify salient topics in a corpus of documents, partition people according to personality profiles, and automatically capture the semantic structure of words and use it to categorize documents.
Read more
Do you want to build systems that learn from experience? Or exploit data to create simple predictive models of the world?
In this course, part of the Data Science MicroMasters program, you will learn a variety of supervised and unsupervised learning algorithms, and the theory behind those algorithms.
Using real-world case studies, you will learn how to classify images, identify salient topics in a corpus of documents, partition people according to personality profiles, and automatically capture the semantic structure of words and use it to categorize documents.
Read more
Do you want to build systems that learn from experience? Or exploit data to create simple predictive models of the world?
In this course, part of the Data Science MicroMasters program, you will learn a variety of supervised and unsupervised learning algorithms, and the theory behind those algorithms.
Using real-world case studies, you will learn how to classify images, identify salient topics in a corpus of documents, partition people according to personality profiles, and automatically capture the semantic structure of words and use it to categorize documents.
Armed with the knowledge from this course, you will be able to analyze many different types of data and to build descriptive and predictive models.
All programming examples and assignments will be in Python, using Jupyter notebooks.
What you'll learn
- Classification, regression, and conditional probability estimation
- Generative and discriminative models
- Linear models and extensions to nonlinearity using kernel methods
- Ensemble methods: boosting, bagging, random forests
- Representation learning: clustering, dimensionality reduction, autoencoders, deep nets
Register for this course and see more details by visiting:
OpenCourser.com/course/yoddqt/machine
What's inside
Learning objectives
- Classification, regression, and conditional probability estimation
- Generative and discriminative models
- Linear models and extensions to nonlinearity using kernel methods
- Ensemble methods: boosting, bagging, random forests
- Representation learning: clustering, dimensionality reduction, autoencoders, deep nets
- Classification, regression, and conditional probability estimation
- Generative and discriminative models
- Linear models and extensions to nonlinearity using kernel methods
- Ensemble methods: boosting, bagging, random forests
- Representation learning: clustering, dimensionality reduction, autoencoders, deep nets
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches fundamental concepts in supervised and unsupervised machine learning, which are foundational for data science
Utilizes a range of real-world case studies, providing practical applications of machine learning techniques
Emphasizes both theoretical foundations and practical implementation, equipping learners with a comprehensive understanding
Covers ensemble methods such as boosting, bagging, and random forests, which are essential techniques in machine learning
Explores representation learning techniques such as clustering, dimensionality reduction, autoencoders, and deep nets, which are cutting-edge approaches in the field
Save this course
Save Machine Learning Fundamentals to your list so you can find it easily later:
Save
Save
Reviews summary
Machine learning principles
According to students, this well-received course teaches the key concepts of machine learning well. Learners appreciate the engaging lectures and challenging assignments, but some note that the provided Python code could be improved and that there is limited staff support for verified learners.
Students appreciate the challenging and engaging nature of the assignments.
"This is probably the best one: ...There are lots of exercises."
"Overall the course is great and the instructor is awesome. Machine learning is fascinating and I now feel like I have a good foundation."
Sanjoy Dasgupta earns praise for his clear explanations and good material in his lectures.
"Professor Sanjoy Dasgupta you are a hero! Such a great teaching style."
"Instructor is top notch, good material, good videos and very clear explanations."
"The lectures content is very good as the main concepts of ML models are explained well by Sanjoy Dasgupta."
Students report a lack of staff support and engagement with the discussion forums.
"this course is brilliant for auditing (i.e. watching the lecture videos) but becoming a verified learner is not worth it due to lack of staff support."
"What makes it worse that the course staff do not monitor the discussion forum and those stuck with these assignments are unable to get help with their questions."
The course does not teach any Python code, and those who attempt to code in Python may have to work through some issues.
"the course does not teach any python code which is fair enough. however, it asks to do the programming assignments in python and these are quite challenging with no solutions provided."
"some of the projects had too much helper code where the student only needed to fill in a portion of the algorithm."
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 Fundamentals with these
activities:
Read 'Machine Learning For Dummies'
Show steps
Review the fundamental concepts of supervised and unsupervised learning covered in the course.
View
Data Analytics & Visualization All-in-One For...
on Amazon
Show steps
-
Locate a copy of the book.
-
Read the chapters relevant to the course.
-
Summarize the main concepts and techniques discussed in the book.
Follow online tutorials on machine learning techniques
Show steps
Expand your knowledge of machine learning by following online tutorials on specific techniques.
Browse courses on
Self-Directed Learning
Show steps
-
Find a reputable source for online machine learning tutorials.
-
Choose a tutorial that covers a technique you want to learn more about.
-
Follow the tutorial step-by-step.
Review key linear algebra concepts
Show steps
Reviewing foundational linear algebra concepts such as matrices, vectors, and transformations will help to build a strong foundation for the course material.
Browse courses on
Linear Algebra
Show steps
-
Revisit the basics of matrix algebra, including operations and properties of matrices.
-
Brush up on vector spaces, including concepts like linear independence and span.
-
Review linear transformations, focusing on their properties and representation using matrices.
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Attend meetups or conferences related to machine learning
Show steps
Connect with other professionals in the field of machine learning to exchange ideas and learn about the latest trends.
Browse courses on
Networking
Show steps
-
Find a meetup or conference related to machine learning.
-
Attend the event.
-
Introduce yourself to other attendees.
Solve practice problems on machine learning algorithms
Show steps
Strengthen your understanding of machine learning algorithms by solving practice problems.
Browse courses on
Problem Solving
Show steps
-
Find a set of practice problems on machine learning algorithms.
-
Attempt to solve the problems on your own.
-
Check your solutions against the provided answer key.
Solve classification and regression problems
Show steps
Improve understanding of classification and regression algorithms by solving practice problems.
Browse courses on
Classification
Show steps
-
Review lecture notes and textbook on classification and regression.
-
Solve a set of practice problems related to classification.
-
Solve a set of practice problems related to regression.
-
Compare your solutions to provided answer key.
Solve practice problems on supervised learning algorithms
Show steps
Engaging in regular practice drills on supervised learning algorithms, such as linear regression and logistic regression, will strengthen understanding and improve problem-solving skills.
Browse courses on
Supervised Learning
Show steps
-
Identify a set of practice problems covering various supervised learning algorithms.
-
Solve the problems, focusing on understanding the underlying concepts and applying them effectively.
-
Review solutions and identify areas for improvement.
Develop a simple machine learning model
Show steps
Gain hands-on experience applying the machine learning algorithms covered in the course to solve real-world problems.
Browse courses on
Classification
Show steps
-
Identify a suitable dataset for your project.
-
Choose the appropriate machine learning algorithm.
-
Train and evaluate your model.
-
Deploy your model and make predictions.
Follow tutorials on ensemble methods
Show steps
Gain hands-on experience with ensemble methods and boost understanding of their underlying principles.
Browse courses on
Ensemble Methods
Show steps
-
Find online tutorials on ensemble methods.
-
Follow the tutorials and complete the provided exercises.
-
Implement a simple ensemble method from scratch.
-
Apply the implemented method to a real-world dataset.
Create a presentation on a machine learning research paper
Show steps
Develop your research and presentation skills by analyzing and presenting a recent machine learning research paper.
Browse courses on
Research
Show steps
-
Find a recent machine learning research paper.
-
Read and understand the paper.
-
Create a presentation that summarizes the paper's main findings.
-
Present your findings to others.
Attend a machine learning workshop
Show steps
Gain hands-on experience with machine learning tools and techniques through a structured workshop.
Show steps
-
Find a machine learning workshop that aligns with your interests.
-
Register for the workshop.
-
Attend the workshop and actively participate in the activities.
Create a visual representation of clustering results
Show steps
Develop data visualization skills and gain insights into clustering results by creating visual representations.
Browse courses on
Clustering
Show steps
-
Cluster a dataset using a clustering algorithm.
-
Choose an appropriate data visualization technique for the clustering results.
-
Create a visual representation of the clustering results.
-
Analyze the visualization and draw insights from the clustering.
Develop a visual representation of an unsupervised learning algorithm
Show steps
Creating a visual representation of an unsupervised learning algorithm, such as k-means clustering or PCA, will provide a deeper understanding of its functionality and application.
Browse courses on
Unsupervised Learning
Show steps
-
Choose an unsupervised learning algorithm to visualize.
-
Gather data relevant to the algorithm.
-
Develop a visual representation using appropriate tools, such as graphs or charts.
-
Analyze the visualization to identify patterns and insights.
Create a tutorial on a machine learning technique
Show steps
Solidify your understanding of a specific machine learning technique by explaining it to others.
Browse courses on
Teaching
Show steps
-
Choose a machine learning technique to focus on.
-
Write a detailed explanation of the technique.
-
Create visual aids or examples to illustrate the technique.
-
Share your tutorial with others.
Build a simple predictive model
Show steps
Practice data analysis and model building skills by creating a predictive model for a given dataset.
Browse courses on
Predictive Modeling
Show steps
-
Choose a dataset and define a predictive modeling problem.
-
Explore the data and identify relevant features.
-
Select and train a predictive model.
-
Evaluate the model's performance and make improvements.
-
Document the modeling process and findings in a report.
Read 'Machine Learning For Dummies'
Show steps
Review the fundamental concepts of supervised and unsupervised learning covered in the course.
View
Data Analytics & Visualization All-in-One For...
on Amazon
Show steps
Show steps
Show steps
- Locate a copy of the book.
- Read the chapters relevant to the course.
- Summarize the main concepts and techniques discussed in the book.
Follow online tutorials on machine learning techniques
Show steps
Expand your knowledge of machine learning by following online tutorials on specific techniques.
Browse courses on
Self-Directed Learning
Show steps
Show steps
Show steps
- Find a reputable source for online machine learning tutorials.
- Choose a tutorial that covers a technique you want to learn more about.
- Follow the tutorial step-by-step.
Review key linear algebra concepts
Show steps
Reviewing foundational linear algebra concepts such as matrices, vectors, and transformations will help to build a strong foundation for the course material.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Revisit the basics of matrix algebra, including operations and properties of matrices.
- Brush up on vector spaces, including concepts like linear independence and span.
- Review linear transformations, focusing on their properties and representation using matrices.
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Attend meetups or conferences related to machine learning
Show steps
Connect with other professionals in the field of machine learning to exchange ideas and learn about the latest trends.
Browse courses on
Networking
Show steps
Show steps
Show steps
- Find a meetup or conference related to machine learning.
- Attend the event.
- Introduce yourself to other attendees.
Solve practice problems on machine learning algorithms
Show steps
Strengthen your understanding of machine learning algorithms by solving practice problems.
Browse courses on
Problem Solving
Show steps
Show steps
Show steps
- Find a set of practice problems on machine learning algorithms.
- Attempt to solve the problems on your own.
- Check your solutions against the provided answer key.
Solve classification and regression problems
Show steps
Improve understanding of classification and regression algorithms by solving practice problems.
Browse courses on
Classification
Show steps
Show steps
Show steps
- Review lecture notes and textbook on classification and regression.
- Solve a set of practice problems related to classification.
- Solve a set of practice problems related to regression.
- Compare your solutions to provided answer key.
Solve practice problems on supervised learning algorithms
Show steps
Engaging in regular practice drills on supervised learning algorithms, such as linear regression and logistic regression, will strengthen understanding and improve problem-solving skills.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Identify a set of practice problems covering various supervised learning algorithms.
- Solve the problems, focusing on understanding the underlying concepts and applying them effectively.
- Review solutions and identify areas for improvement.
Develop a simple machine learning model
Show steps
Gain hands-on experience applying the machine learning algorithms covered in the course to solve real-world problems.
Browse courses on
Classification
Show steps
Show steps
Show steps
- Identify a suitable dataset for your project.
- Choose the appropriate machine learning algorithm.
- Train and evaluate your model.
- Deploy your model and make predictions.
Follow tutorials on ensemble methods
Show steps
Gain hands-on experience with ensemble methods and boost understanding of their underlying principles.
Browse courses on
Ensemble Methods
Show steps
Show steps
Show steps
- Find online tutorials on ensemble methods.
- Follow the tutorials and complete the provided exercises.
- Implement a simple ensemble method from scratch.
- Apply the implemented method to a real-world dataset.
Create a presentation on a machine learning research paper
Show steps
Develop your research and presentation skills by analyzing and presenting a recent machine learning research paper.
Browse courses on
Research
Show steps
Show steps
Show steps
- Find a recent machine learning research paper.
- Read and understand the paper.
- Create a presentation that summarizes the paper's main findings.
- Present your findings to others.
Attend a machine learning workshop
Show steps
Gain hands-on experience with machine learning tools and techniques through a structured workshop.
Show steps
Show steps
Show steps
- Find a machine learning workshop that aligns with your interests.
- Register for the workshop.
- Attend the workshop and actively participate in the activities.
Create a visual representation of clustering results
Show steps
Develop data visualization skills and gain insights into clustering results by creating visual representations.
Browse courses on
Clustering
Show steps
Show steps
Show steps
- Cluster a dataset using a clustering algorithm.
- Choose an appropriate data visualization technique for the clustering results.
- Create a visual representation of the clustering results.
- Analyze the visualization and draw insights from the clustering.
Develop a visual representation of an unsupervised learning algorithm
Show steps
Creating a visual representation of an unsupervised learning algorithm, such as k-means clustering or PCA, will provide a deeper understanding of its functionality and application.
Browse courses on
Unsupervised Learning
Show steps
Show steps
Show steps
- Choose an unsupervised learning algorithm to visualize.
- Gather data relevant to the algorithm.
- Develop a visual representation using appropriate tools, such as graphs or charts.
- Analyze the visualization to identify patterns and insights.
Create a tutorial on a machine learning technique
Show steps
Solidify your understanding of a specific machine learning technique by explaining it to others.
Browse courses on
Teaching
Show steps
Show steps
Show steps
- Choose a machine learning technique to focus on.
- Write a detailed explanation of the technique.
- Create visual aids or examples to illustrate the technique.
- Share your tutorial with others.
Build a simple predictive model
Show steps
Practice data analysis and model building skills by creating a predictive model for a given dataset.
Browse courses on
Predictive Modeling
Show steps
Show steps
Show steps
- Choose a dataset and define a predictive modeling problem.
- Explore the data and identify relevant features.
- Select and train a predictive model.
- Evaluate the model's performance and make improvements.
- Document the modeling process and findings in a report.
Career center
Learners who complete Machine Learning Fundamentals will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their knowledge of machine learning, statistics, and data analysis to extract insights from data. They work with businesses to identify problems that can be solved with data, and then they develop and implement the solutions. This course will help you build a foundation in machine learning, which is essential for a successful career as a Data Scientist. You will learn about the different types of machine learning algorithms, how to train and evaluate them, and how to apply them to real-world problems.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning models. They work with data scientists to identify and solve problems that can be solved with machine learning, and then they develop and implement the models that will solve those problems. They also monitor and maintain the models to ensure that they are performing as expected. This course will help you build a foundation in machine learning, which is essential for a successful career as a Machine Learning Engineer. You will learn about the different types of machine learning algorithms, how to train and evaluate them, and how to apply them to real-world problems.
Data Analyst
Data Analyst
Data Analysts use their knowledge of data analysis, statistics, and computer programming to extract insights from data. They work with businesses to identify problems that can be solved with data, and then they develop and implement the solutions. This course will help you build a foundation in machine learning, which is increasingly being used to solve problems in data analysis. Data Analysts who have a strong understanding of machine learning will be more valuable to their employers.
Sales Manager
Sales Manager
Sales Managers are responsible for leading and managing sales teams. They work with sales representatives to develop and execute sales strategies that achieve sales goals. This course may be helpful for Sales Managers who want to learn more about machine learning. Machine learning is increasingly being used to develop and execute sales strategies, and Sales Managers who have a strong understanding of machine learning will be more valuable to their employers.
Business Analyst
Business Analyst
Business Analysts use their knowledge of business, data analysis, and computer programming to identify and solve problems in businesses. They work with businesses to identify and solve problems that can be solved with data, and then they develop and implement the solutions. This course may be helpful for Business Analysts who want to learn more about machine learning. Machine learning is increasingly being used to solve problems in business, and Business Analysts who have a strong understanding of machine learning will be more valuable to their employers.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work with customers to identify and solve problems that can be solved with software, and then they develop and implement the software that will solve those problems. This course may be helpful for Software Engineers who want to learn more about machine learning. Machine learning is increasingly being used to solve problems in software development, and Software Engineers who have a strong understanding of machine learning will be more valuable to their employers.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to bring new products to market. This course may be helpful for Product Managers who want to learn more about machine learning. Machine learning is increasingly being used to develop new products, and Product Managers who have a strong understanding of machine learning will be more valuable to their employers.
Operations Manager
Operations Manager
Operations Managers are responsible for the day-to-day operations of a business. They work with employees, customers, and suppliers to ensure that the business runs smoothly. This course may be helpful for Operations Managers who want to learn more about machine learning. Machine learning is increasingly being used to improve the efficiency and effectiveness of business operations, and Operations Managers who have a strong understanding of machine learning will be more valuable to their employers.
Consultant
Consultant
Consultants provide advice and guidance to businesses on a variety of topics. They work with businesses to identify and solve problems, and to develop and implement solutions. This course may be helpful for Consultants who want to learn more about machine learning. Machine learning is increasingly being used to solve problems in a variety of industries, and Consultants who have a strong understanding of machine learning will be more valuable to their clients.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their knowledge of mathematics, statistics, and computer programming to analyze financial data. They work with investment banks, hedge funds, and other financial institutions to identify and exploit opportunities in the financial markets. This course may be helpful for Quantitative Analysts who want to learn more about machine learning. Machine learning is increasingly being used to analyze financial data, and Quantitative Analysts who have a strong understanding of machine learning will be more valuable to their employers.
Entrepreneur
Entrepreneur
Entrepreneurs are individuals who start and run their own businesses. They work to develop and launch new products and services, and to build and grow their businesses. This course may be helpful for Entrepreneurs who want to learn more about machine learning. Machine learning is increasingly being used to develop and launch new products and services, and Entrepreneurs who have a strong understanding of machine learning will be more successful in their businesses.
Human Resources Manager
Human Resources Manager
Human Resources Managers are responsible for the human resources function of a business. They work with employees, managers, and executives to develop and implement human resources policies and programs that support the business's goals. This course may be helpful for Human Resources Managers who want to learn more about machine learning. Machine learning is increasingly being used to improve the efficiency and effectiveness of human resources functions, and Human Resources Managers who have a strong understanding of machine learning will be more valuable to their employers.
Financial Manager
Financial Manager
Financial Managers are responsible for the financial planning and management of a business. They work with senior management to develop and implement financial plans that achieve the business's financial goals. This course may be helpful for Financial Managers who want to learn more about machine learning. Machine learning is increasingly being used to improve the accuracy and efficiency of financial planning and management, and Financial Managers who have a strong understanding of machine learning will be more valuable to their employers.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. They work with creative teams, media buyers, and other marketing professionals to develop and execute marketing campaigns that reach target audiences. This course may be helpful for Marketing Managers who want to learn more about machine learning. Machine learning is increasingly being used to develop and execute marketing campaigns, and Marketing Managers who have a strong understanding of machine learning will be more valuable to their employers.
Project Manager
Project Manager
Project Managers are responsible for the planning, execution, and closure of projects. They work with project teams to develop and execute project plans that achieve project goals. This course may be helpful for Project Managers who want to learn more about machine learning. Machine learning is increasingly being used to improve the efficiency and effectiveness of project management, and Project Managers who have a strong understanding of machine learning will be more valuable to their employers.
For more career information including salaries, visit:
OpenCourser.com/course/yoddqt/machine
Reading list
We've selected 22 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 Fundamentals.
Provides a comprehensive introduction to statistical learning, covering topics such as supervised and unsupervised learning, model selection, and evaluation. It valuable resource for anyone interested in learning about statistical learning.
Save
Comprehensive guide to deep learning, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for anyone interested in learning about deep learning.
Provides a comprehensive introduction to machine learning, covering topics such as supervised and unsupervised learning, model selection, and evaluation. It valuable resource for anyone interested in learning about machine learning.
Save
Provides a comprehensive introduction to pattern recognition and machine learning, covering topics such as supervised and unsupervised learning, model selection, and evaluation. It valuable resource for anyone interested in learning about pattern recognition and machine learning.
Provides a comprehensive introduction to machine learning, covering topics such as supervised and unsupervised learning, model selection, and evaluation. It valuable resource for anyone interested in learning about machine learning.
Provides a comprehensive introduction to statistical learning, covering topics such as supervised and unsupervised learning, model selection, and evaluation. It valuable resource for anyone interested in learning about statistical learning.
Provides a comprehensive introduction to machine learning, covering topics such as supervised and unsupervised learning, model selection, and evaluation. It valuable resource for anyone interested in learning about machine learning.
Provides a comprehensive introduction to machine learning, covering topics such as supervised and unsupervised learning, model selection, and evaluation. It valuable resource for anyone interested in learning about machine learning.
Provides a comprehensive introduction to machine learning, covering topics such as supervised and unsupervised learning, model selection, and evaluation. It valuable resource for anyone interested in learning about machine learning.
Provides a comprehensive introduction to machine learning, covering topics such as supervised and unsupervised learning, model selection, and evaluation. It valuable resource for anyone interested in learning about machine learning.
Provides a comprehensive introduction to machine learning, covering topics such as supervised and unsupervised learning, model selection, and evaluation. It valuable resource for anyone interested in learning about machine learning.
Provides an introduction to reinforcement learning, covering topics such as Markov decision processes, value functions, and policy gradients. It valuable resource for learners who want to understand the basics of reinforcement learning.
Provides a comprehensive introduction to machine learning, covering topics such as supervised and unsupervised learning, model selection, and evaluation. It valuable resource for anyone interested in learning about machine learning.
Provides a comprehensive overview of statistical learning, covering topics such as linear regression, logistic regression, and decision trees. It valuable resource for learners who want to understand the foundations of machine learning.
Provides a comprehensive introduction to machine learning, covering topics such as supervised and unsupervised learning, model selection, and evaluation. It valuable resource for anyone interested in learning about machine learning.
Provides a comprehensive introduction to machine learning, covering topics such as supervised and unsupervised learning, model selection, and evaluation. It valuable resource for anyone interested in learning about machine learning.
Provides a practical introduction to machine learning for programmers. It covers a variety of topics, including data preprocessing, feature engineering, and model evaluation. It good choice for learners who want to quickly get started with machine learning.
Provides a comprehensive introduction to machine learning using the Python programming language. It covers a variety of topics, including data preprocessing, feature engineering, and model evaluation. It good choice for learners who want to use Python for machine learning.
Save
Provides a comprehensive overview of data mining, covering topics such as data preprocessing, feature selection, and model evaluation. It valuable resource for learners who want to gain a deeper understanding of data mining.
Provides a comprehensive overview of pattern recognition and machine learning. It covers a variety of topics, including supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for learners who want to gain a deeper understanding of pattern recognition and machine learning.
Save
Provides a comprehensive overview of machine learning from an algorithmic perspective. It covers a variety of topics, including supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for learners who want to gain a deeper understanding of the algorithms used in machine learning.
Provides a comprehensive overview of the theory and practice of machine learning. It covers a variety of topics, including supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for learners who want to gain a deeper understanding of the theoretical foundations of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/yoddqt/machine
Share
Similar courses
Here are nine courses similar to
Machine Learning Fundamentals.
Code Free Data Science
OpenCourser.com/course/nzxnd7/code
Code Free Data Science
Most relevant
Data Analytics Methods
OpenCourser.com/course/ptu5or/data
Data Analytics Methods
Most relevant
Advanced Machine Learning Algorithms
OpenCourser.com/course/a5ojdd/advanced
Advanced Machine Learning Algorithms
Most relevant
Predictive Analysis with SQL Server
OpenCourser.com/course/7to644/predictive
Predictive Analysis with SQL Server
Most relevant
Machine Learning: Clustering & Retrieval
OpenCourser.com/course/a60amb/machine
Machine Learning: Clustering & Retrieval
Most relevant
Predictive Analytics Using Apache Spark MLlib on Databricks
OpenCourser.com/course/ugnndd/predictive
Predictive Analytics Using Apache Spark MLlib on...
Most relevant
Predictive Analytics for Business Planning: Time-Series Forecasting
OpenCourser.com/course/0ifgcg/predictive
Predictive Analytics for Business Planning: Time-Series...
Predictive Analytics with PyTorch
OpenCourser.com/course/zw2d2q/predictive
Predictive Analytics with PyTorch
Regression & Forecasting for Data Scientists using Python
OpenCourser.com/course/q3l1vx/regression
Regression & Forecasting for Data Scientists using Python
Effort
8 to 10 hours per week for 10 weeks
Level
Introductory
Via
edX
Institution
The University of California, San Diego
Instructor
Sanjoy Dasgupta
Session
March 26, 2024
- July 12, 2024
Language
English
Transcript
English
Good to know
Teaches fundamental concepts in supervised and unsupervised machine learning, which are foundational for data science
Utilizes a range of real-world case studies, providing practical applications of machine learning techniques
Emphasizes both theoretical foundations and practical implementation, equipping learners with a comprehensive understanding
Covers ensemble methods such as boosting, bagging, and random forests, which are essential techniques in machine learning
Explores representation learning techniques such as clustering, dimensionality reduction, autoencoders, and deep nets, which are cutting-edge approaches in the field
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser