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The Fundamentals of Machine Learning and Data Analytics

Rob Barton
Jerome Henry

Machine Learning and Data Analytics are at the heart of a new technological movement that is disrupting almost every industry and business. Businesses are realizing that to compete and drive greater efficiencies they need to harness available data and use it to make critical decisions. This is the field of data science, with Machine Learning and Big Data Analytics being the key enablers of this technology.

Nearly every company in the world is evaluating their digital strategy and is 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 professionals in nearly every industry.

This course will introduce you to the fundamental concepts of both Data Analytics as well as Machine Learning, and will help you understand not only the basics and building blocks, but also and key components this technology. The course will explore the background of data science, and will have a focus on some of the key Machine Learning algorithms, including Supervised Learning, Unsupervised Learning and Neural Networks. You will learn when and why to use these various algorithms, and the common platforms to build your own Artificial Intelligence or Machine Learning project.

What you'll learn-and how you can apply it

  • Building blocks of data analytics, and data analytics platforms, including Hadoop and YARN
  • Core components of Machine Learning, including Supervised and Unsupervised Learning, Neural Networks (Deep Learning)
  • What a Machine Learning project looks like
  • Common tools of AI and Machine Learning, including TensorFlow, GPUs, and others

This training course is for you because...

  • You want to learn about machine learning, AI, and big data analytics, the basics of the algorithms, the tools, and their applications
  • You are an executive, digital architect, IT administrator, or operational technology (OT) professional

Prerequisites

This course is meant for the beginner and will discuss AI and Machine Learning from the ground up. It would be good to have some basic knowledge of programming and some basic of mathematics and statistics.

Recommended Follow-up

Pearson LiveLessons Video: “Understanding the Fundamentals of Big Data Analytics and Machine Learning” by Robert Barton and Jerome Henry

About your instructor

  • Rob Barton, CCIE #6660 (R&S and Security), CCDE 2013::6 is a Principal Systems Engineer working in Cisco's Digital Transformation and Innovation group. Rob is a registered Professional Engineer (P. Eng) and has worked in the IT industry for over 20 years, the last 17 of which have been at Cisco. Rob Graduated from the University of British Columbia with a degree in Engineering Physics. Rob is a Cisco Press published author, with titles including QoS, Wireless, and IoT. Over the past five years, Rob has worked on some of the largest IoT deployments in the world, and is an expert in several IoT-related industries, such as utilities. His areas of interest include wireless communications, IPv6, IoT, and industrial control systems. Rob is also a multi-year recipient of the Cisco Live Distinguished speaker award.

  • Jerome Henry is Principal Engineer in the Enterprise Infrastructure and Solutions Group at Cisco systems. Jerome has more than 15 years experience teaching technical Cisco courses in more than 15 different countries and 4 different languages, to audiences ranging from Bachelor degree students to networking professionals and Cisco internal system engineers. Focusing on his wireless experience, Jerome joined Cisco in 2012. Before that time, he was consulting and teaching Heterogeneous Networks and Wireless Integration with the European Airespace team, which was later acquired by Cisco to become their main wireless solution. He then spent several years with a Cisco Learning partner, developing technical courses, and working on training material for new technologies. He is certified wireless networking expert (CWNE #45), CCIE Wireless (#24750), CCNP Wireless, developed several Cisco courses focusing on wireless topics and authored several books and video courses on Wireless, IoT and networking. Jerome is also an IEEE member, where he was elevated to the grade of Senior Member in 2013, and also participates to Wi-Fi Alliance working groups. With more than 10000 hours in the classroom, Jerome was awarded the IT Training Award best Instructor silver medal. He is based in RTP, NC.

Schedule

The timeframes are only estimates and may vary according to how the class is progressing

An Introduction to Data Analytics and Machine Learning (25 min)

  • What is Data Analytics, defining data
  • Defining Artificial Intelligence and Machine Learning
  • The role of Machine Learning in the world of Data Analytics
  • An overview of the AI/ML and data analytics landscape

Data Analytics 101 (25 min)

  • Big Data Architectures
  • Hadoop and YARN
  • The role of data brokers

BREAK (10 min)

Machine Learning 101 – Supervised Learning (55 minutes)

  • Linear Regression
  • Logistic Regression
  • Random Forests
  • Support Vector Machine (SVM)

BREAK (10 min)

Semi-Supervised Learning (20 min)

  • Differences between supervised and semi-supervised learning
  • Reinforcement Learning
  • Applications and use cases

Unsupervised Learning (30 min)

  • An introduction to unsupervised learning
  • An overview of common algorithms
  • Explaining the K-Means algorithms

BREAK (10 min)

Deep Learning and Neural Networks (50 min)

  • An introduction to Deep Learning and Neural Networks
  • How the algorithm works
  • Applications of Deep Learning for Video Analytics and NLP

BREAK (10 min)

Principal Components Analysis (20 min)

  • The need for dimensionality reduction
  • An explanation of the PCA algorithm

How to Build an AI / Machine Learning Project (20 min)

  • Components of a Machine Learning Project
  • Workflow of a Machine Learning Project

An Introduction to ML Software Tools (30 min)

  • An overview of Machine Learning toolkits
  • A deeper look at TensorFlow

BREAK (10 min)

Hardware Accelerators for Machine Learning (20 min)

  • The requirements for hardware acceleration in Machine Learning
  • An introduction to Graphical Processing Units (GPSs)

Future Directions in AI and Machine Learning (20 min)

  • What AI can and cannot do
  • Ethical questions of AI and Machine Learning
  • The Singularity
  • Current research work in Machine Learning