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

Trivera Technologies LLC
Training overview
Professional Course
3 days
From 2,195 USD
Start dates
Online
2,195 USD
3/15/2021

Online
2,195 USD
5/3/2021

Online
2,195 USD
6/14/2021

Online
2,195 USD
7/26/2021

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

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

Costs

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

Why choose Trivera Technologies LLC?

Over 25 years of technology training expertise.

Robust portfolio of over 1,000 leading edge technology courses.

Guaranteed to run courses and flexible learning options.

About Trivera Technologies LLC

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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Contact info

Trivera Technologies LLC

7862 West Irlo Bronson Highway
STE 626
Kissimmee FL 34747

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www.triveratech.com

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