Be introduced to the most common Machine Learning algorithms used in supervised and unsupervised learning models.
Understanding the complexities of model construction, training, and testing is an essential skill for today’s Machine Learning Engineer. Students completing this second course in the Machine Learning certificate program will gain a working knowledge of the most common models used in both supervised and unsupervised learning algorithms, including Regression, Naive Bayes, K-nearest neighbors, K-means, and DBSCAN. Students will also utilize dimension reduction techniques such as Principal Component Analysis, and Linear Discriminant Analysis to pre-process datasets prior to model training.
What You Will Learn
- Create Python programming language scripts in the Google Collaboratory development environment to pre-process a dataset using standard Machine Learning libraries.
- Implement and analyze regression models including simple and multiple regression, polynomial, lasso, and logistic regression.
- Implement and analyze “supervised” classification algorithms including Naive Bayes and K nearest neighbors (KNN).
- Implement and analyze “unsupervised” clustering algorithms including K-means, and density-based spatial (DBSCAN) clustering.
- Implement and analyze dimensionality reduction techniques including linear discriminant analysis (LDA) and Principal Component Analysis (PCA).
- Write and test working Python programs from a generic problem statement through algorithm development, design and implementation, unit test, integration, and deployment.
Return to the Futures website
here.
Course Number: CSE-90160
Credit: 3.00 unit(s)
+ Expand All
-
7/10/2023 - 7/30/2023
$350
Online
-
-
-
CLASS TYPE:
Online Asynchronous.
This course is entirely web-based and to be completed asynchronously between the published course start and end dates. Synchronous attendance is NOT required.
You will have access to your online course on the published start date OR 1 business day after your enrollment is confirmed if you enroll on or after the published start date.
-
TEXTBOOKS:
No information available at this time.
-
POLICIES:
No refunds after: 7/3/2023.
-
7/10/2023 - 7/30/2023
extensioncanvas.ucsd.edu
You will have access to your course materials on the published start date OR 1 business day after your enrollment is confirmed if you enroll on or after the published start date.
-
1/3/2024 - 3/13/2024
$350
Online
-
-
-
CLASS TYPE:
Online Asynchronous.
This course is entirely web-based and to be completed asynchronously between the published course start and end dates. Synchronous attendance is NOT required.
You will have access to your online course on the published start date OR 1 business day after your enrollment is confirmed if you enroll on or after the published start date.
-
TEXTBOOKS:
No information available at this time.
-
POLICIES:
Early enrollment advised.
No UCSD parking permit required.
No visitors permitted.
Pre-enrollment required.
No refunds after: 12/27/2022.
-
1/3/2024 - 3/13/2024
extensioncanvas.ucsd.edu
You will have access to your course materials on the published start date OR 1 business day after your enrollment is confirmed if you enroll on or after the published start date.
There are no sections of this course currently scheduled. Please contact the Pre-College Programs department at 858-534-0804 or precollege@ucsd.edu for information about when this course will be offered again.