English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 7h 40m | 3.84 GB
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.
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
Table of Contents
01 Data Analytics and Machine Learning Fundamentals LiveLessons Video Training – Introduction
02 Module 1 – introduction
03 Learning objectives
04 1.1 Understanding Big Data Analytics and Why You Should Care
05 Learning objectives
06 2.1 Understanding What Machine Learning and Artificial Intelligence Are
07 2.2 Understanding the Machine Learning Landscape
08 2.3 Understanding Machine Learning and the Big Data Ecosystem
09 Module 2 – introduction
10 Learning objectives
11 3.1 Understanding the Fundamentals of Big Data Analytics Systems
12 3.2 Connecting to Data with Brokers and Data Messaging
13 3.3 Comparing Batch and Real-Time Streaming Analytics Systems
14 Learning objectives
15 4.1 Comparing Big Data Architectures
16 4.2 Understanding the Basics of Hadoop
17 4.3 Exploring YARN
18 Module 3 – introduction
19 Learning objectives
20 5.1 Understanding Supervised Learning
21 5.2 Examining Linear Regression
22 5.3 Fitting the Data
23 5.4 Exploring Reinforcement Learning
24 Learning objectives
25 6.1 Understanding Classification Fundamentals
26 6.2 Understanding Support Vector Machines (SVM)
27 6.3 Exploring Random Forests
28 6.4 Classification Demo
29 Learning objectives
30 7.1 Understanding Unsupervised Learning Fundamentals
31 7.2 Examining Other Clustering Algorithms
32 7.3 Clustering Demo
33 Module 4 – introduction
34 Learning objectives
35 8.1 Understanding Artificial Neural Network (ANN) Fundamentals
36 8.2 Diving Deeper into Deep Learning
37 8.3 Applying Deep Learning – Image Analytics – Convolutional Neural Network
38 8.4 Exploring an Example Use Case of Deep Learning in Edge Computing
39 Learning objectives
40 9.1 Examining Principal Component Analysis (PCA)
41 9.2 Examining Bayesian Learning
42 Learning objectives
43 10.1 Surveying Machine Learning Frameworks
44 10.2 Understanding ML Hardware Acceleration Technologies
45 10.3 Future Directions of Machine Learning
46 Summary
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