Machine Learning Foundations | Working with Statistics, Algorithms, and Neural Networks | Math Empha
Course Description
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.
Highlights
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, Na?ve 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.
Target Audience
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.
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 Na?ve 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.
ALL ACCESS PASS FROM
$3,995
per license
Sign Up for All Access Pass and gain 12-month access up to 300+ Live instructor-led courses.
BUY NOWUpcoming Courses
Contact us to book a training.
We can plan a specific customized training session tailored to you or your organization's requirements,
or we can sign up several students for a future class.
We’re Ready Lets Talk
Do You Have More Questions? We're delighted to assist you!