Professional Course

Machine Learning Foundation | Working With Statistics, Algorithms, Neural Networks & More (With Best Practices)

3 days
3 days
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Course description

Machine Learning Foundation | Working With Statistics, Algorithms, Neural Networks & More (With Best Practices)

Machine Learning Foundation is a hands-on primer on the mathematics and algorithms used in Data Science, as well as creating the foundation and building the intuition necessary for solving complex machine learning problems. The course provides a good kick start in several core areas with the intent on continued, deeper learning as a follow on. This course is a foundation-level machine learning class for Intermediate skilled team members.

This course reviews key foundational mathematics and introduces students to the algorithms of Data Science. Working in a hands-on learning environment, students will explore:

  • Popular machine learning algorithms, their applicability and limitations
  • Practical application of these methods in a machine learning environment
  • Practical use cases and limitations of algorithms
  • Core machine learning mathematics and statistics
  • Supervised Learning vs. Unsupervised Learning
  • Classification Algorithms including Support Vector Machines, Discriminant Analysis, Naïve Bayes, and Nearest Neighbor
  • Regression Algorithms including Linear and Logistic Regression, Generalized Linear Modeling, Support Vector Regression, Decision Trees, k-Nearest Neighbors (KNN)
  • Clustering Algorithms including k-Means, Fuzzy clustering, Gaussian Mixture
  • Neural Networks including Hidden Markov (HMM), Recurrent (RNN) and Long-Short Term Memory (LSTM)
  • Dimensionality Reduction, Single Value Decomposition (SVD), Principle Component Analysis (PCA)
  • How to choose an algorithm for a given problem
  • How to choose parameters and activation functions
  • Ensemble methods

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Who should attend?

This course is geared for Data Science Analysts, Programmers, Administrators, Architects, and Managers interested in a deeper exploration of common algorithms and best practices in machine learning.

Attending students should have:

  • Strong foundational mathematics skills in Linear Algebra and Probability, to start learning about and using basic machine learning algorithms and concepts
  • Basic Python Skills. Attendees without Python background may view labs as follow along exercises or team with others to complete them. (NOTE: This course is also offered in R or Scala – please inquire for details)
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su

Training content

Section: Core Machine Learning Mathematics Review

  • Statistics Overview and Review
  • Mean, Median, Variance, and deviation
  • Normal / Gaussian Distribution

Section: Probability Review

  • Probability Theory
  • Discrete Probability Distributions
  • Continuous Probability Distributions
  • Measure-Theoretic Probability Theory
  • Central Limit and Normal Distribution
  • Probability Density Function
  • Probability in Machine Learning

Section: Supervised Learning

  • Supervised Learning Explained
  • Classification vs. Regression
  • Examples of Supervised Learning
  • Key supervised algorithms

Section: Unsupervised Learning

  • Unsupervised Learning
  • Clustering
  • Examples of Unsupervised Learning
  • Key unsupervised algorithms (overview)

Section: Regression Algorithms

  • Linear Regression
  • Logistic Regression
  • Support Vector Regression
  • Decision Trees
  • Random Forests

Section: Classification Algorithms

  • Bayes Theorem and the Naïve Bayes classifier
  • Support Vector Machines
  • Discriminant Analysis
  • k-Nearest Neighbor (KNN)

Section: Clustering Algorithms

  • k-Means Clustering
  • Fuzzy Clustering
  • Gaussian Mixture Models

Section: Neural Networks

  • Neural Network Basics
  • Hidden Markov Models (HMM)
  • Recurrent Neural Networks (RNN)
  • Long-Short Term Memory Networks (LSTM)

Section: Ensemble Methods

  • Ensemble Theory and Methods
  • Ensemble Classifiers
  • Bucket of Models
  • Boosting
  • Stacking


  • Price: $2,195.00
  • Discounted Price: $1,426.75

Why choose Trivera Technologies LLC?

Over 25 years of technology training expertise.

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Trivera Technologies LLC
7862 West Irlo Bronson Highway
STE 626
Kissimmee FL 34747

Trivera Technologies

Trivera Technologies is a IT education services & courseware firm that offers a range of wide professional technical education services including: end to end IT training development and delivery, skills-based mentoring programs,new hire training and re-skilling services, courseware licensing and...

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