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Hands-On Machine Learning

New – Overcome real-world complexities of modern machine learning with innovative techniques in this self-paced course.

GK# 7729

Course Overview

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This course explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and real-world examples. While machine learning can be highly theoretical, this course offers a refreshing hands-on approach without losing sight of the underlying principles. Inside, a full exploration of the various algorithms gives you high-quality guidance so you can begin to see just how effective machine learning is at tackling real-world data problems. You will also explore the cutting-edge advancements in machine learning, with worked examples and guidance on identifying behavior patterns using machine learning, deep learning and reinforcement learning.

Schedule

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What You'll Learn

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  • Feature Normalization and Categorical Variables.
  • Identifying Patterns using in time-series data using GMM.
  • Explore explanation of Hidden Markov Model Explanation.
  • Get acquainted with Deep learning and find out how neural networks are being used at the cutting-edge of machine learning
  • Explore the future of machine learning and dive deeper into polyglot persistence, semantic data, and more

Outline

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On-Demand Outline

Anomaly Detection with K-means Clustering

Finding Patterns in Time Series Data

Finding Anomalies in Data Using Graphs

Machine Learning tools, Libraries, and frameworks

Clustering based learning

Reinforcement learning

Modern data architectures for Machine Learning

Who Should Attend

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This course has been created for data scientists who want to see machine learning in action and explore its real-world application. With guidance on everything from the fundamentals of machine learning and predictive analytics to the latest innovations set to lead the big data revolution into the future, this is an unmissable resource for anyone dedicated to tackling current big data challenges. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately.

Course Delivery

This course is available in the following formats:

On-Demand

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