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
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
- 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
- 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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About Trivera Technologies LLC
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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