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Machine Learning Foundations | Math Emphasis

Learn about the math, statistics, probability, and algorithms in machine learning.

This foundation-level hands-on course focuses on the mathematics and algorithms used in Data Science. You’ll learn core skills and explore machine learning algorithms along with their practical application and limitations. With this knowledge, you’ll build the intuition necessary to solve complex machine learning problems.

GK# 7603 Vendor# TTML5504
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Who Should Attend?

Experienced Data Scientists, Data Analysts, Developers, Administrators, Architects, and Managers interested in a deeper exploration of common algorithms and best practices in machine learning.

This course focuses on the mathematics aspect of machine learning as opposed to more general skills and concepts. It is also offered using R or Scala – please inquire for details.

What You'll Learn

Join an engaging hands-on learning environment, where you’ll learn:

  • 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, and k-Nearest Neighbors (KNN)
  • Clustering Algorithms including k-Means, Fuzzy clustering, and Gaussian Mixture
  • Neural Networks including Hidden Markov (HMM), Recurrent (RNN), and Long-Short Term Memory (LSTM)
  • Dimensionality Reduction, Single Value Decomposition (SVD), and Principle Component Analysis (PCA)
  • How to choose an algorithm for a given problem
  • How to choose parameters and activation functions
  • Ensemble methods

This course has a 50% hands-on labs to 50% lecture ratio with engaging instruction, demos, group discussions, labs, and project work.

Course Outline

Core Machine Learning Mathematics Review

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

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

Supervised Learning

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

Unsupervised Learning

  • Unsupervised Learning
  • Clustering
  • Examples of Unsupervised Learning
  • Key unsupervised algorithms

Regression Algorithms

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

Classification Algorithms

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

Clustering Algorithms

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

Neural Networks

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

Ensemble Methods

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

Prerequisites

Before attending this course, you should have:

  • Strong foundational mathematics skills in Linear Algebra and Probability
  • Basic Python skills
  • Basic Linux skills
  • Familiarity with command line options such as ls, cd, cp, and su

This course is for intermediate skilled professional. This is not a basic class.

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