Machine Learning Module 1:
Fundamental Machine Learning

This course provides a comprehensive overview of machine learning by covering key algorithms, functions and components of machine learning systems, along with common real-world demands, such as scalability, runtime processing requirements and utilization by search engines. Also covered are the relationships between machine learning systems and deep learning systems and artificial intelligence.

The following primary topics are covered:
– A Brief History of Machine Learning
– Understanding Machine Learning and Deep Learning
– Benefits and Challenges of Machine Learning
– Machine Learning Languages
– Machine Learning and Data Science, Artificial Intelligence
– Machine Learning for Data Mining and Pattern Recognition
– Machine Learning for Recommendation Systems and Match Making
– Natural Language Processing (NLP) and Search Engines
– Supervised, Unsupervised and Semi-Supervised Learning
– Reinforcement Learning
– Open Source and Proprietary Machine Learning Frameworks
– HPC (High Performance Computing)
– Machine Learning Libraries and Scalability Dimensions
– Machine Learning Architectures and Algorithms
– Data Processing with Machine Learning
– Decision Trees and Regression
– Decision Tree Algorithm and Classification and Regression Tree (CART)
– Iterative Dichotomiser 3 (ID3) and C4.5/C5.0
– Chi-squared Automatic Interaction Detection (CHAID), Decision Stump and M5
– Conditional Decision Trees
– Linear, Logistic, Stepwise and Ordinary Least Squares Regression (OLSR)
– Multivariate Adaptive Regression Splines (MARS) and Locally Estimated
– Scatterplot Smoothing (LOESS)

Duration: 1 Day

Taking the Course at a Workshop

This course can be taken as part of instructor-led workshops taught by Arcitura Certified Trainers. These workshops can be open for public registration or delivered privately for a specific organization. Certified Trainers can teach workshops in-person at a specific location or virtually using a video-enabled remote system, such as WebEx. Visit the Workshop Calendar page to view the current calendar of public workshops or visit the Private Training page to learn more about Arcitura’s worldwide private workshop delivery options.

Below are the base materials provided to public and private workshop participants. 

Note that as a workshop participant, you may be eligible for discounts on the purchase of the Study Kit for this course.

Taking the Course using a Study Kit

This course can be completed via self-study by purchasing a Study Kit, which includes the base course materials as well as additional supplements and resources designed specifically for self-paced study and exam preparation. 

Visit the Machine Learning Module 1 Study Kit page for pricing information and for details. Also, visit the Study Kits Overview page for information regarding discounted Certification Study Kit Bundles for individual certification tracks.

The following materials are provided in the Study Kit for this course:

Taking the Course using an eLearning Study Kit

This course can be completed via self-study by purchasing an eLearning Study Kit subscription, which includes online access to the base course materials as well as additional supplements and resources designed for self-paced study and exam preparation.

Visit the  Machine Learning Module 1 eLearning Study Kit page for pricing information and details. Also, visit the eLearning Study Kits Overview page for information regarding discounted Certification eLearning Study Kit Bundles for individual certification tracks.

This eLearning Study Kit provides access to the following materials:

Study Kits and Study Bundles can be purchased using the online store. By purchasing and registering this Study Kit, you may be eligible for discounts on the registration of this course as part of a public workshop.

Certification

This course is one of three courses that are used to prepare for Exam ML90.01. A passing grade on this exam is required to achieve the Machine Learning Specialist certification.

Vendor-Neutral Topic Overview

Machine Learning course modules are focused on vendor-neutral topics and therefore do not provide detailed coverage of any vendor-specific tools. The courses are intentionally authored this way so as to provide an unambiguous and objective understanding of practices and technology that can be further complemented with product-specific training.

Fact Sheet

Download a printable PDF document with information about this course module and its corresponding Study Kit.

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