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Data Analytics and Machine Learning Fundamentals
LiveLessons Video Training

by Jerome Henry
Publisher: Addison-Wesley Professional
Release Date: April 2019
ISBN: 9780135557358

Lecture Size: 92x150
Lecture Date: 04.2019

Lecture Link: https://learning.oreilly.com/

| More than 7.5 Hours of Video Instruction

| Overview
| Nearly every company in the world is evaluating its digital
| strategy and looking for ways to capitalize on the promise of
| digitization. Big data analytics and machine learning are central
| to this strategy. Understanding the fundamentals of data
| processing and artificial intelligence is becoming required
| knowledge for executives, digital architects, IT administrators,
| and operational telecom (OT) professionals in nearly every
| industry.

| In Data Analytics and Machine Learning Fundamentals LiveLessons,
| experienced CCIEs Robert Barton and Jerome Henry provide more than
| 7 1/2 hours of personal instruction exploring the principles of
| big data analytics, supervised learning, unsupervised learning,
| and neural networks. In addition to delving into the fundamental
| concepts, Barton and Henry address sample big data and machine
| learning use cases in different industries and present demos
| featuring the most common tools (such as Hadoop, TensorFlow,
| Matlab/Octave, R, and Python) in various fields used by data
| scientists and researchers.
| At the conclusion of this video course, you will be armed with
| knowledge and application skills required to become proficient in
| articulating big data analytics and machine learning principles
| and possibilities.

| Skill Level
| Beginner to intermediate data analytics/machine learning knowledge

| Learn How To
Understand how static and real-time streaming data is collected,
| analyzed, and used
Understand the key tools and methods that enable machines to
| learn and mimic human thinking
Bring together unstructured data in preparation for analysis and
| visualization
Compare and contrast the various big data architectures
Apply supervised learning/linear regression, data fitting, and
| reinforcement learning to machines to yield the information
| results you're looking for
Apply classification techniques to machine learning to better
| analyze your data
Exploit the benefits of unsupervised learning to glean data you
| didn't even know you were looking for
Understand how artificial neural networks (ANNs) perform deep
| learning with surprising (and useful) results
Apply principal components analysis (PCA) to improve the
| management of data analysis
Understand the key approaches to implementing machine learning
| on real systems and the considerations you must make when
| undertaking a machine learning project

| Who Should Take This Course
Anyone who wants to learn about machine learning, AI, and big
| data analytics, the basics of the algorithms, the tools, and their
| applications
Executives, digital architects, IT administrators, and
| operational technology (OT) professionals in nearly every industry
| where big data analytics has become an integral part of the
| business

| Course Requirements
| Requires basic knowledge of big data analysis/machine learning.