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Machine Learning Specialist

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Workshops not currently scheduled

We are currently planning the workshops for this course. You may purchase an eLearning kit for immediate access or contact us for further details.



USD $393.15 $196.58 eLearning Kit only

Let’s face it, with the amount of data that is being generated today, it is impossible for any human to analyse in a timely manner. The variety of data also presents the problem that by the time we understand the data and formulate a way to measure its importance, we are already behind. This is where machine learning comes in.

In this course you’ll learn what Machine Learning is, the specific learning approaches, languages and models as well as system design and Neural Networks.

The eLearning kit bundle provides access to a 3 Module course bundle that will be available for 1 year from the date of purchase. You can also take the on-line exam to get a Digital Badge to show your expertise in this field.

  • Module 1: Fundamental Machine Learning
  • Module 2: Advanced Machine Learning
  • Module 3: Machine Learning Technology Lab

Further description of the contents are included in the Details tab.


Intended for
Data Scientists, BI Specialists, Data Architects, Solution Architects and other IT Professionals that are responsible for working with and analysing data.


Pre-requisites
An understanding of Data Analytics or analytical mathematics and IT systems is recommended.


Learning Outcomes
A comprehensive understanding of Machine Learning, its concepts, uses and techniques. The following topics are some of the ones that are covered during the course:

  • 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 Recommendation Systems and Match Making
  • Natural Language Processing (NLP) and Search Engines
  • Supervised, Unsupervised and Semi-Supervised Learning
  • Open Source and Proprietary Machine Learning Frameworks
  • Machine Learning Libraries and Scalability Dimensions
  • Machine Learning Architectures and Algorithms
  • Data Processing with Machine Learning
  • 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)
  • Understanding Machine Learning Algorithms
  • Machine Learning System Design
  • Classification
  • Clustering
  • Rule Systems
  • Mapping, Projection Pursuit and Multidimensional Scaling) (MDS)
  • Linear, Mixture, Quadratic and Flexible Discriminant Analyses
  • Constructing Hypotheses using Instance-based Models
  • Building Artificial Neural Network Constructs with Deep Learning
  • Constructing Machine Learning Models using Neural Networks

And more…


 

Exams & Certification

  • The exams required for certification can be taken at any Pearson VUE testing center in the world or online via Pearson VUE Online Proctoring.
  • Please ensure you purchase the correct exam voucher as they are not exchangable.

 

Note: All quoted pricing is excluding GST. For customers in Australia GST will be added during the check-out process.

Topics Covered 
The following topics are covered during the course 

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)

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)

Module 3: Machnie Learning Lab
This course module presents participants with a series of exercises and problems that are designed to test their ability to apply their knowledge of topics covered in previous courses. Completing this lab will help highlight areas that require further attention and will further prove proficiency in machine learning systems and techniques, as they are applied and combined to solve real-world problems.

The Trainer works closely with participants to ensure that all exercises are carried out completely and accurately. Attendees can voluntarily have exercises reviewed. 


 

Note: All quoted pricing is excluding GST. For customers with a billing address in Australia GST will be added during the check-out process.



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