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Machine Learning Specialist - Unsupervised Machine Learning, Time Series and Survival Analysis
Course Description
Overview
This course introduces you to one of the main types of Machine Learning: Unsupervised Learning as well as additional topics in Machine Learning that complement essential tasks, including forecasting and analyzing censored data. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. You will learn how to find and analyze data with a time component and censored data that needs outcome inference. You will learn a few techniques for Time Series Analysis and Survival Analysis. The hands-on section of this course focuses on using best practices for unsupervised learning and verifying assumptions derived from Statistical learning.
IBM Customers and Sellers: If you are interested in this course, consider purchasing it as part of one of these Individual or Enterprise Subscriptions:
- IBM Learning for Data and AI Individual Subscription (SUBR022G)
- IBM Learning for Data and AI Enterprise Subscription (SUBR004G)
- IBM Learning Individual Subscription with Red Hat Learning Services (SUBR023G)
Objectives
By the end of this course you should be able to:
Explain the kinds of problems suitable for Unsupervised Learning approaches. Explain the curse of dimensionality, and how it makes clustering difficult with many features. Describe and use common clustering and dimensionality-reduction algorithms. Try clustering points where appropriate, compare the performance of per-cluster models. Understand metrics relevant for characterizing clusters. Identify common modeling challenges with time series data. Explain how to decompose Time Series data: trend, seasonality, and residuals. Explain how autoregressive, moving average, and ARIMA models work. Understand how to select and implement various Time Series models. Describe hazard and survival modeling approaches. Identify types of problems suitable for survival analysis.
Audience
This course targets aspiring data scientists interested in acquiring hands-on experience with Unsupervised Machine Learning techniques in a business setting and those interested in acquiring hands-on experience with Time Series Analysis and Survival Analysis.
Prerequisites
In order to be successful, you should have knowledge of:
To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.
Topics
1. Introduction to Unsupervised Learning and K Means
2. Selecting a clustering algorithm
3. Dimensionality Reduction
4. Introduction to Time Series Analysis
5. Stationarity and Time Series Smoothing
6. ARMA and ARIMA Models
7. Deep Learning and Survival Analysis Forecasts
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Self-Paced Training Info
Learn at your own pace with anytime, anywhere training
- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
- Learn more about e-Learning
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Self-Paced Training Terms & Conditions
THIS IS A SELF-PACED VIRTUAL CLASS. AFTER YOU REGISTER, YOU HAVE 365 DAYS TO ACCESS THE COURSE.
This is a Self-Paced virtual class; it is intended for students who do not need the support of a classroom instructor. If you feel you would better benefit from having access to a Subject Matter Expert, please enroll in the Instructor-Led version instead. Minimal technical support is provided to address issues with accessing the platform or problems within the lab environment.
Before you enroll, review the system requirements to ensure that your system meets the minimum requirements for this course. AFTER YOU ARE ENROLLED IN THIS COURSE, YOU WILL NOT BE ABLE TO CANCEL YOUR ENROLLMENT. You are billed for the course when you submit the enrollment form. Self-Paced Virtual Classes are non-refundable. Once you purchase a Self-Paced Virtual Class, you will be charged the full price.
After you receive confirmation that you are enrolled, you will be sent further instructions to access your course material and remote labs. A confirmation email will contain your online link, your ID and password, and additional instructions for starting the course.
Upon receipt of the Order Confirmation Letter which includes your Enrollment Key (Access code); the course begins its twelve (12) month access period. IMPORTANT!!! If your course provides access to a hands-on lab (Virtual Lab Environment), you will have a specific number of days (typically 30 days) on the remote lab platform to complete your hands-on labs. Do not start your lab until you are ready to use your lab time effectively. Time allotted in the virtual lab environment will be indicated once you apply the enrollment key. The self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours a day.
If the course requires a remote lab system, the lab system access is allocated on a first-come, first-served basis. When you are not using the elab system, ensure that you suspend your elab to maximize your hours available to use the elab system. Note: This does not add additional days to your Lab Environment time frame.
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Click the Skytap Connectivity Documentation button to read about the hardware, software and internet connection requirements.
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