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Mat Leonard,
Parnian Barekatain,
Eddy Shyu,
Brok Bucholtz,
Elizabeth Otto Hamel,
Cindy Lin,
Cezanne Camacho,
Arpan Chakraborty,
Luis Serrano,
and
Juan Delgado
Register for this course and see more details by visiting:
OpenCourser.com/course/c47szk/machine
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What's inside
Syllabus
Linear regression is a very effective algorithm to predict numerical data.
Naive Bayesian Algorithms are powerful tools for creating classifiers for incoming labeled data.
Read more
Syllabus
Linear regression is a very effective algorithm to predict numerical data.
Naive Bayesian Algorithms are powerful tools for creating classifiers for incoming labeled data.
Read more
Clustering is one of the most common methods of unsupervised learning. Here, we'll discuss the K-means clustering algorithm.
Decision trees are a structure for decision-making where each decision leads to a set of consequences or additional decisions.
Learn the intuition behind the Kalman Filter, a vehicle tracking algorithm, and implement a one-dimensional tracker of your own.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Instructors include Mat Leonard, Parnian Barekatain, Eddy Shyu, Brok Bucholtz, Elizabeth Otto Hamel, Cindy Lin, Cezanne Camacho, Arpan Chakraborty, Luis Serrano, and Juan Delgado; some of these instructors may be recognized for their work
Develops core skills for decision-making, pattern recognition, and modeling
Taught by top-tier instructors from the field
Understand the intuition behind the Kalman Filter, a core machine learning algorithm
The course is offered through the provider Udacity
Save this course
Save Machine Learning to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Machine Learning with these
activities:
Explore Decision Tree Algorithms
Show steps
Broaden your understanding of decision tree algorithms by exploring different resources and tutorials.
Browse courses on
Decision Trees
Show steps
-
Watch online videos or read articles about decision tree algorithms.
-
Follow along with interactive tutorials to build your own decision trees.
Review Linear Regression
Show steps
Reinforce your understanding of linear regression by working through practice problems.
Browse courses on
Linear Regression
Show steps
-
Solve practice problems from the course textbook or online resources.
-
Create your own practice problems and solve them.
-
Participate in online forums or discussion groups to discuss linear regression concepts and solve problems with other students.
Peer-to-Peer Tutoring
Show steps
Strengthen your own understanding while assisting others by participating in peer-to-peer tutoring.
Browse courses on
Machine Learning
Show steps
-
Join a study group or online forum where you can connect with other students.
-
Offer to help classmates with concepts they may be struggling with.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Implement Naive Bayesian Classifier
Show steps
Develop a deeper understanding of Naive Bayesian classifiers by implementing one from scratch.
Show steps
-
Choose a dataset to work with.
-
Write Python code to implement the Naive Bayesian algorithm.
-
Evaluate the performance of your classifier on the dataset.
Build a K-means Clustering Model
Show steps
Apply your knowledge of K-means clustering by building a model for a real-world dataset.
Browse courses on
K-Means Clustering
Show steps
-
Gather and prepare a dataset for clustering.
-
Choose the number of clusters and implement the K-means algorithm in Python.
-
Evaluate the performance of your model using metrics such as the silhouette score or Davies-Bouldin index.
Create a Kalman Filter Simulation
Show steps
Deepen your understanding of the Kalman filter by implementing a simulation in Python.
Browse courses on
Kalman Filter
Show steps
-
Learn the theoretical concepts behind the Kalman filter.
-
Write Python code to implement a one-dimensional Kalman filter.
-
Simulate the tracking of a moving object using your Kalman filter.
Contribute to Open Source Machine Learning Projects
Show steps
Gain practical experience and contribute to the wider community by participating in open source machine learning projects.
Browse courses on
Machine Learning
Show steps
-
Identify open source machine learning projects that align with your interests.
-
Read the project documentation and contribute code, documentation, or bug fixes.
Explore Decision Tree Algorithms
Show steps
Broaden your understanding of decision tree algorithms by exploring different resources and tutorials.
Browse courses on
Decision Trees
Show steps
Show steps
Show steps
- Watch online videos or read articles about decision tree algorithms.
- Follow along with interactive tutorials to build your own decision trees.
Review Linear Regression
Show steps
Reinforce your understanding of linear regression by working through practice problems.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Solve practice problems from the course textbook or online resources.
- Create your own practice problems and solve them.
- Participate in online forums or discussion groups to discuss linear regression concepts and solve problems with other students.
Peer-to-Peer Tutoring
Show steps
Strengthen your own understanding while assisting others by participating in peer-to-peer tutoring.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Join a study group or online forum where you can connect with other students.
- Offer to help classmates with concepts they may be struggling with.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Implement Naive Bayesian Classifier
Show steps
Develop a deeper understanding of Naive Bayesian classifiers by implementing one from scratch.
Show steps
Show steps
Show steps
- Choose a dataset to work with.
- Write Python code to implement the Naive Bayesian algorithm.
- Evaluate the performance of your classifier on the dataset.
Build a K-means Clustering Model
Show steps
Apply your knowledge of K-means clustering by building a model for a real-world dataset.
Browse courses on
K-Means Clustering
Show steps
Show steps
Show steps
- Gather and prepare a dataset for clustering.
- Choose the number of clusters and implement the K-means algorithm in Python.
- Evaluate the performance of your model using metrics such as the silhouette score or Davies-Bouldin index.
Create a Kalman Filter Simulation
Show steps
Deepen your understanding of the Kalman filter by implementing a simulation in Python.
Browse courses on
Kalman Filter
Show steps
Show steps
Show steps
- Learn the theoretical concepts behind the Kalman filter.
- Write Python code to implement a one-dimensional Kalman filter.
- Simulate the tracking of a moving object using your Kalman filter.
Contribute to Open Source Machine Learning Projects
Show steps
Gain practical experience and contribute to the wider community by participating in open source machine learning projects.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify open source machine learning projects that align with your interests.
- Read the project documentation and contribute code, documentation, or bug fixes.
Career center
Learners who complete Machine Learning will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. This course can build a foundation in the core algorithms that power nearly every machine learning system. Supervised learning is essential for classification and regression tasks. Unsupervised learning is essential for clustering and anomaly detection. This course also covers the Kalman Filter, which is used for vehicle tracking, robotics, navigation, control systems, and more.
Researcher
Researcher
Researchers in the field of artificial intelligence will benefit from this course. Supervised learning is commonly used for tasks like classification and regression. Unsupervised learning is commonly used for tasks like clustering, anomaly detection, and dimensionality reduction. Decision trees are commonly used for tasks like decision support systems, predictive modeling, and expert systems.
Statistician
Statistician
Statisticians use machine learning algorithms for a variety of applications in statistics. This course can provide a foundation for understanding supervised and unsupervised machine learning. Linear regression is essential for tasks like forecasting and risk modeling. Naive Bayes and decision trees are essential for tasks like classification and fraud detection.
Machine Learning Consultant
Machine Learning Consultant
Machine Learning Consultants help businesses implement machine learning solutions. This course can provide a foundation for understanding the core algorithms that businesses use for machine learning. Supervised learning is commonly used for tasks like classification and regression. Unsupervised learning is commonly used for tasks like clustering, anomaly detection, and dimensionality reduction. Familiarity with these algorithms is essential for success as a Machine Learning Consultant.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use machine learning algorithms for a variety of applications in operations research. This course can provide a foundation for understanding supervised and unsupervised machine learning. Linear regression is essential for tasks like forecasting and optimization. Naive Bayes and decision trees are essential for tasks like classification and fraud detection.
Financial Analyst
Financial Analyst
Financial Analysts use machine learning algorithms for a variety of applications in finance. This course can provide a foundation for understanding supervised and unsupervised machine learning. Linear regression is essential for tasks like forecasting and risk modeling. Naive Bayes and decision trees are essential for tasks like credit scoring and fraud detection.
Data Engineer
Data Engineer
Data Engineers who work with machine learning systems will benefit from this course. This course will expose one to the core algorithms that power machine learning systems. Supervised learning is essential for tasks like classification and regression. Unsupervised learning is critical for tasks like clustering, anomaly detection, and dimensionality reduction. Decision trees are critical for tasks like decision support systems, predictive modeling, and expert systems.
Actuary
Actuary
Actuaries use machine learning algorithms for a variety of applications in insurance and finance. This course can provide a foundation for understanding supervised and unsupervised machine learning. Linear regression is essential for tasks like forecasting and risk modeling. Naive Bayes and decision trees are essential for tasks like credit scoring and fraud detection.
Risk Manager
Risk Manager
Risk Managers use machine learning algorithms for a variety of applications in risk management. This course can provide a foundation for understanding supervised and unsupervised machine learning. Linear regression is essential for tasks like forecasting and risk modeling. Naive Bayes and decision trees are essential for tasks like fraud detection and credit scoring.
Quant
Quant
Quantitative Analysts use machine learning algorithms for a variety of financial applications. This course can provide a foundation for understanding supervised and unsupervised machine learning. Linear regression is essential for tasks like forecasting, risk modeling, and portfolio optimization. Naive Bayes and decision trees are essential for tasks like credit scoring, fraud detection, and market prediction.
Data Analyst
Data Analyst
Data Analysts use machine learning algorithms to explore and analyze data. This course can help build a foundation in the core algorithms that provide the basis for data analysis. Supervised learning is essential for classification and regression tasks, both of which are critical for data analysis. Unsupervised learning is also essential for tasks like clustering, anomaly detection, and dimensionality reduction.
Data Scientist
Data Scientist
Data Scientists utilize machine learning algorithms to help businesses make informed decisions. This course can help one build a foundation in supervised and unsupervised machine learning. Topics like linear regression, Naive Bayes, clustering, and decision trees are all essential to the toolkit of any Data Scientist. This course may also provide a foundation for understanding more advanced topics like time-series analysis and natural language processing, which are extremely valuable for Data Scientists.
Software Engineer
Software Engineer
Software Engineers who work on machine learning systems will benefit from this course. Supervised learning algorithms are commonly used for spam filtering, face detection, and digit recognition. Clustering algorithms are used for customer segmentation, fraud detection, and image recognition. Decision trees are critical for decision support systems, predictive modeling, and expert systems. Familiarity with these algorithms is critical for success as a Software Engineer.
Product Manager
Product Manager
Product Managers who work on machine learning products will benefit from this course. Supervised learning is commonly used for spam filtering, face detection, and digit recognition. Clustering algorithms are used for customer segmentation, fraud detection, and image recognition. Decision trees are critical for decision support systems, predictive modeling, and expert systems. Familiarity with these algorithms is critical for success as a Product Manager.
Business Analyst
Business Analyst
Business Analysts use machine learning to improve business and organizational decision making. This course provides exposure to supervised and unsupervised learning, both of which have applications in business analytics. Supervised learning is commonly used for customer churn prediction, lead scoring, and fraud detection. Unsupervised learning is commonly used for customer segmentation, market basket analysis, and anomaly detection.
For more career information including salaries, visit:
OpenCourser.com/course/c47szk/machine
Reading list
We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Machine Learning.
Save
Comprehensive reference on deep learning, covering the latest advancements and techniques in the field.
Provides a practical guide to machine learning using popular Python libraries, making it an excellent resource for hands-on learning.
Foundational text on reinforcement learning, providing a comprehensive overview of the field.
Provides a comprehensive overview of probabilistic graphical models, which are a powerful tool for modeling complex data distributions.
Classic in the field of pattern recognition and machine learning, providing a comprehensive overview of the theoretical underpinnings.
Provides a comprehensive overview of machine learning algorithms and techniques, making it a valuable resource for both beginners and experienced practitioners.
Provides a comprehensive overview of Bayesian reasoning and its applications in machine learning.
Provides a practical introduction to machine learning for people with a programming background.
Save
Takes an algorithmic approach to machine learning, providing a deeper understanding of the underlying mathematical concepts.
Provides a comprehensive overview of machine learning with R, making it a valuable resource for those who prefer the R programming language.
Provides a probabilistic foundation for machine learning, offering a different perspective on the field.
Comprehensive overview of machine learning, written in a clear and concise style that is accessible to beginners.
Save
Provides a gentle introduction to machine learning for beginners.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/c47szk/machine
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Time
550 hours
Level
Advanced
Via
Udacity
Instructors
Mat Leonard
Parnian Barekatain
Eddy Shyu
Brok Bucholtz
Elizabeth Otto Hamel
Cindy Lin
Cezanne Camacho
Arpan Chakraborty
Luis Serrano
Juan Delgado
Language
English
Good to know
Instructors include Mat Leonard, Parnian Barekatain, Eddy Shyu, Brok Bucholtz, Elizabeth Otto Hamel, Cindy Lin, Cezanne Camacho, Arpan Chakraborty, Luis Serrano, and Juan Delgado; some of these instructors may be recognized for their work
Develops core skills for decision-making, pattern recognition, and modeling
Taught by top-tier instructors from the field
Understand the intuition behind the Kalman Filter, a core machine learning algorithm
The course is offered through the provider Udacity
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