Machine Learning Module 2:
Advanced Machine Learning

This course delves into the many aspects, algorithms and models of contemporary machine learning environments, including analysis techniques, system design and processing considerations, constructing and working with models and key principles.

The following primary topics are covered:
– Understanding Machine Learning Algorithms
– Key Machine Learning Principles
– Machine Learning System Design
– Classification (Logistic Regression, K-Nearest Neighbors (K-NN)
– Support Vector Machine (SVM)
– Kernel SVM, Decision Tree and Random Forest Classification)
– Clustering (K-Means, K-Medians, Expectation maximization, Hierarchical Clustering)
– Rule Systems (OneR, ZeroR, Cubist)
– Repeated Incremental Pruning to Procedure Error Reduction (RIPPER)
– Improving Results Accuracy with Dimension Reduction (PCA, PCR, PLSR, Sammon Mapping, Projection Pursuit and Multidimensional Scaling) (MDS)
– Linear, Mixture, Quadratic and Flexible Discriminant Analyses
– Solving Classification and Regression Problems using Bayesian models (Naïve Bayes, Gaussian and Multinomial, AODE, BN and BBN)
– Constructing Hypotheses using Instance-based Models (kNN, LVQ, SOM and LWL)
– Building Artificial Neural Network Constructs with Deep Learning (DBM, DBN, CNN and Stacked Auto-Encoders)
– Constructing machine learning models using Neural Networks (Perceptron, Back-Propagation, Hopfield Network, RBFN)
– Combining Independently Trained Models and Generating Predictions using
– Ensemble Models (Bagging, AdaBoost, Blending, GBM, GBRT and Random Forest)
– Solving Overfitting Problems with Regulation Models (Ridge Regression, LASSO, Elastic Net and LARS)

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 2 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 2 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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