$350 – $500

Machine Learning Training | IT Training | Disruptive Technologies | Artific...

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Instructor Led Online | Video Conference

Bellevue, WA

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Will be sent after registration and payment


Course Overview

This course includes decision tree learning, artificial neural networks, Bayesian networks, instance-based learning, analytic learning, reinforcement, learning, genetic programming, deep learning, etc. This class also uses real life example applications on machine learning and methods for designing systems that iteratively learn from data and improve qualitatively with cumulative experience.


About this course

Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. This area is also concerned with issues both theoretical and practical. In this course, we will present algorithms and approaches in such a way that grounds them in larger systems as you learn about a variety of topics, including:

  • Statistical Supervised and Unsupervised learning methods
  • Randomized Search Algorithms
  • Bayesian learning methods
  • Reinforcement Learning

By the end of this course, you should have a strong understanding of machine learning so that you can pursue any further and more advanced learning.

  • Reviewing the types of problems that can be solved
  • Understanding building blocks
  • Learning the fundamentals of building models in machine learning
  • Exploring key algorithms
  • Supervised learning algorithms
  • Key concepts like under- and over-fitting, regularization, and cross-validation
  • How to identify the type of problem to be solved, choose the right algorithm, tune parameters, and validate a model


What you will learn in this course?

  • Demonstrate the knowledge of decision tree learning, artificial neural networks, Bayesian networks, instance-based learning, analytic learning, reinforcement learning, genetic programming, deep learning, etc.
  • Demonstrate the knowledge of MLlib on Spark, or other machine learning libraries.
  • Practice examples of machine learning programming and open source machine learning tools, and implement example machine learning applications.


What are the Prerequisites?

Data Structures and Computer Algorithms, Statistics, Probability, Linear Algebra, Calculus


Detailed Course Outline

1. Introduction to Machine Learning
2. Working with Python or R
3. Machine Learning Techniques
- Types of Learning
- Applying Machine Learning
- Machine learning system, design
4. Types of Machine Learning Algorithms
- Supervised Learning: Regression; Classification
- Unsupervised Learning: Clustering; Recommendation
- Deep Learning
- Semi-Supervised Learning
- Reinforcement
5. Supervised Learning Regression
6.Supervised Learning Classification
7.Unsupervised Learning
- Clustering
- Recommendation
- Deep Learning
8. Spark Core and MLLib
- Spark Core
- Spark Architecture
- Working with RDDs
- Machine learning with Spark – Mllib
9.Common Machine Learning Algorithms
- Linear Regression
- Logistic Regression
- Decision Tree
- SVM
- Naive Bayes
- KNN
- KMeans
- Random Forest
- Dimensionality Reduction Algorithms
- Gradient Boost & Adaboost
- Artificial Neural Networks
- Bayesian Networks
- Instance-Based Learning
- Analytic Learning
- Reinforcement
- Genetic Programming


Training Dates

Begin December 18 and end January 18, 2018

Times: Every Week, Monday and Wednesday 7-9 (Pacific Standard Time) each day

Each session will be recorded and the recordings will be shared after each session with students

Refund Policy

1. There are no refunds.
2. If for any reason the course has not been taken, class is cancelled or rescheduled, the payment can be applied towards any future course by Omni212.


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Date and Time

Location

Instructor Led Online | Video Conference

Bellevue, WA

View Map

Refund Policy

No Refunds

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