Data Analytics and Machine Learning Fundamentals
Data Analytics and Machine Learning Fundamentals
MP4 | Video: AVC 1280 x 720 | Audio: AAC 48KHz 2ch | Duration: 06:20:45 | 10.73 GB
Genre: eLearning | Language: English
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.
Lesson descriptions
Lesson 1, "Big Data Analytics Overview," looks at the overall big data landscape. Here, you learn what big data is and how it contrasts with the data types that are stored in traditional relational database systems. You examine the unique requirements of big data management systems and understand exactly what the roles of the data analyst and the data scientist are.
Lesson 2, "Machine Learning Overview," provides a 30,000-foot overview of the world of machine learning and sets the stage for the rest of the video course to come. This lesson discusses artificial intelligence and the various families of machine learning and how they relate to the world of big data analytics.
Lesson 3, "Fundamental Concepts of Big Data Analytics," takes you into the world of big data management systems. In this lesson, you look at what is required to connect to your data, prepare it, and ultimately move it into the data lake where it can be analyzed by ML algorithms and then visualized. This lesson also looks at some of the major classes of data analytics. This includes an investigation into some of the popular frameworks for real-time streaming analytics and a look at how this branch of analytics works and interfaces with more traditional data management systems.
Lesson 4, "Big Data Architectures," delves into one of the best-known big data management systems, Hadoop. You see how Hadoop stores data and how the processing engine, MapReduce, actually works. This lesson also looks at how YARN, often referred to as the second generation of Hadoop, opens up the ecosystem to other open-source frameworks for machine learning.
Lesson 5, "Regression," jumps into the mechanics, and also the math and programming, of machine learning techniques, starting with what is called supervised learning and its most famous representative, linear regression. You do not need to be a mathematician or a computer programmer to understand this lesson and the following ones, but you will learn the tools to really understand what machine learning does and how it does it, so you can explain it or implement the techniques yourself.
Lesson 6, "Classification," goes a little bit deeper and looks at cases where you are just asking the machine to help you organize your data in groups: Are these cherries ripe or not? Is this mysterious cake a muffin or something else? Is this next customer likely to buy a product from your store or not? These are the types of questions this lesson tackles, with classification techniques such as the Sigmoid function and support vector machines, as well as a technique called random forests.
Lesson 7, "Unsupervised Learning," examines issues where you do not know the answer. There is some data, and you want the machine to help you know what there is to know about this data. This kind of technique is called unsupervised learning because you cannot do much to help the machine find the useful information in the data. However, the machine can help you find patterns, things that look alike, and this is what unsupervised learning is about. This lesson looks at K-means, DBSCAN, and a few other of these unsupervised techniques, as usual giving you the principles, some elements of the math, and some implementation examples to guide you.
Lesson 8, "Deep Learning," covers deep learning and neural networks. These names are hyped in the media, and in this lesson you learn what they really do, how they really work, and why they are successful, so you can start implementing artificial neural networks and convolutional networks to recognize images and other things.
Lesson 9, "Advanced Algorithms," explores some advanced machine learning algorithms that are designed to solve two of the key problems faced by data scientists: how to deal with data in a huge number of dimensions and how to train your model when you have only a small amount of data. This lesson explores the principal components analysis algorithm and Bayesian inference, or statistical learning.
Lesson 10, "Deployment Considerations and Future Directions," outlines what you need to know to implement an ML project. This lesson explores some of the common and popular frameworks, including TensorFlow and Pytorch, as well as how to implement ML workloads on physical hardware by exploring hardware acceleration techniques using graphical processor units, or GPUs. This lesson closes out the video course with a look at some of the future directions of AI and ML and a glimpse of where it will take us in the future.
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