Intro To Big Data, Data Science And Artificial Intelligence

Big Data Technology & Tools for Non-Technical Leaders. Industry expert insights on IoT, AI and Machine Learning for all.

What you'll learn

Examples of Big Data and Data Science in Practice (Healthcare, Logistics & Transportation, Manufacturing, and Real Estate & Property Management industries)

Big Data Definition and Data Sources. Why we need to be data and technology savvy.

Introduction to Data Science and Skillset required for working with Big Data

Technological Breakthroughs which Enable Big Data Solutions (Connectivity, Cloud, Open Source, Hadoop and NoSQL)

Big Data Technology Architecture and most popular technology tools used for each Architecture Layer

Beginner's Introduction to Data Analysis, Artificial Intelligence and Machine Learning

Simplified Overview of Machine Learning Algorithms and Neural Networks

Requirements

Curiosity about business and technology

There are no special requirements or prerequisites. Anyone can learn from this course.

Description

This course is designed for anyone who is new to big data projects, and would like to get better understanding what machine learning and artificial intelligence mean in practice. It is not a technical course, it does not involve coding, but it will make you feel confident when working in teams with data scientists and programmers. It will bring you up to speed with the data science, ML and AI terminology.  The course is also designed for people who are generally interested in modern technologies and their applications - we have included case studies covering oil&gas predictive maintenance, use of AI in healthcare, application of sensor and other digital technologies  in buildings and construction, the role of machine learning in transport and logistics and many more.You will learn about big data, Internet of Things (IoT), data science, big data technologies, artificial intelligence (AI), machine learning (ML) algorithms, neural networks, and why this could be relevant to you even if you don't have technology or data science background. Please note that this is NOT TECHNICAL TRAINING and it does NOT teach Coding/Development or Statistics, but it is suitable for technical professionals.  I am proud to say that this course was purchased by a large oil&gas company in Asia to educate their field engineers about machine learning as part of their digitalisation strategy. The course includes the interviews with industry experts that cover  big data developments in Real Estate, Logistics & Transportation and Healthcare industries.  You will learn how machine learning is used to predict engine failures, how artificial intelligence is used in anti-ageing, cancer treatment and clinical diagnosis, you will find out what technology is used in managing smart buildings and smart cities including Hudson Yards in New York.  We have got fantastic guest speakers who are the experts in their areas:- WAEL ELRIFAI - Global VP of Solution Engineering - Big Data, IoT & AI at Hitachi Vantara with over 15 years of experience in the field of machine learning and IoT. Wael is also a Co-Authour of the book "The Future of IoT".- ED GODBER - Healthcare Strategist with over 20 years of experience in Healthcare, Pharmaceuticals and start-ups specialising in Artificial Intelligence.- YULIA PAK - Real Estate and Portfolio Strategy Consultant with over 12 years of experience in Commercial Real Estate advisory, currently working with clients who deploy IoT technologies to improve management of their real estate portfolio.Hope you will enjoy the course and let me know  in the comments of each section how I can improve the course!  Please follow me on social media (Shortlisted Productions) - you can find the links on my profile page - just click on my name at the bottom of the page just before the reviews.  And please check out my other courses on Climate Change.

Overview

Section 1: Course overview and Introduction to big data

Lecture 1 Course Introduction

Lecture 2 Guest Speakers

Lecture 3 BEFORE YOU START

Lecture 4 Why learn about big data?

Lecture 5 Big data definition and Sources of data

Section 2: Big Data in Practice - LOGISTICS & TRANSPORTATION

Lecture 6 Section introduction

Lecture 7 Logistics & Transportation: Social Impact of Artificial Intelligence & IoT

Lecture 8 Logistics & Transportation: Predictive & Prescriptive Maintenance

Lecture 9 Logistics & Transportation: Prepositioning of Goods and Just in Time inventory

Lecture 10 Logistics & Transportation: Route Optimisation

Lecture 11 Logistics & Transportation: Warehouse Optimisation and order picking

Lecture 12 Logistics & Transportation: The Future of the industry

Section 3: Big Data in Practice - PREDICTIVE MAINTENANCE IN MANUFACTURING

Lecture 13 Predictive Maintenance in Manufacturing - Case Study SIBUR

Section 4: Big Data in Practice: REAL ESTATE & PROPERTY MANAGEMENT

Lecture 14 Real Estate: Introduction to big data in real estate

Lecture 15 Real Estate: Business Drivers for Using Big Data

Lecture 16 Real Estate & Property Management: Technological Enablers

Lecture 17 Real Estate: Building Asset Management and Building Information Modelling

Lecture 18 Real Estate: Big Data and IoT in Building Maintenance and Management - examples

Lecture 19 Real Estate: Smart Buildings

Lecture 20 Additional Resources to Lecture on Smart Buildings

Lecture 21 Real Estate: Smart Cities (examples - Los Angeles and Hudson Yards in New York)

Lecture 22 Additional resources on Smart Cities

Lecture 23 Real Estate: Smart Technologies Cost and Government Subsidies (example - Norway)

Lecture 24 Real Estate: Data Driven Future

Section 5: Big Data in Practice: HEALTHCARE

Lecture 25 Healthcare: Data Challenges in Healthcare Industry

Lecture 26 Healthcare: Transforming Role of AI and Data Measurement Technologies

Lecture 27 Healthcare: Artificial Intelligence in Disease Prevention

Lecture 28 Healthcare: Artificial Intelligence in Anti-Ageing

Lecture 29 Healthcare: AI in Clinical Decision Making and Cancer Treatment

Lecture 30 Healthcare: Clash of AI and Traditional Healthcare Science

Lecture 31 Healthcare: Final Remarks - Value of Artificial Intellegence to Consumers

Lecture 32 BIG DATA IN PRACTICE: SECTION WRAP-UP

Section 6: Data Science and Required Skillset

Lecture 33 Data Science Definition and Required Skillset

Lecture 34 Guest Speakers importance of working in teams & understanding business objective

Lecture 35 Data Science Skillset: Section Wrap-Up

Lecture 36 Handouts

Section 7: Introduction to Big Data Technologies

Lecture 37 Key Technological Advances and Enablers

Lecture 38 Wide Adoption of Cloud Computing

Lecture 39 Data Management Technological Breakthroughs (e.g. NoSQL, Hadoop)

Lecture 40 Open Source and Open APIs

Lecture 41 Additional Resources and Handouts

Lecture 42 Big Data Technology Architecture (including examples of popular technologies)

Lecture 43 Additional Resources and Handouts

Section 8: Introduction to data analysis, Artificial Intelligence and Machine Learning

Lecture 44 Why to be data and tech savvy

Lecture 45 Big Data Analytics and Artificial Intelligence Definitions

Lecture 46 Machine Learning Workflow and Training a Model

Lecture 47 Model Accuracy and Ability to Generalise

Lecture 48 Machine Learning Components: DATA

Lecture 49 Machine Learning Components: FEATURES

Lecture 50 Machine Learning Components: ALGORITHMS

Lecture 51 Additional Resources and Handouts

Section 9: Simplified Overview of Machine Learning Algorithms

Lecture 52 Classical Machine Learning: Supervised and Unsupervised Learning

Lecture 53 SUPERVISED LEARNING: Classification

Lecture 54 Classification: Naive Bayes

Lecture 55 Classification: Decision Trees

Lecture 56 Classification: Support Vector Machines (SVM)

Lecture 57 Classification: Logistic Regression

Lecture 58 Classification: K Nearest Neighbour

Lecture 59 Classification: Anomaly Detection

Lecture 60 SUPERVISED LEARNING: Regression

Lecture 61 Classical Machine Learning: Unsupervised Learning

Lecture 62 UNSUPERVISED LEARNING: Clustering

Lecture 63 Clustering: K-Means

Lecture 64 Clustering: Mean-Shift

Lecture 65 Clustering: DBSCAN

Lecture 66 Clustering: Anomaly Detection

Lecture 67 UNSUPERVISED LEARNING: Dimensionality Reduction

Lecture 68 UNSUPERVISED LEARNING: Association Rule

Lecture 69 CLASSICAL MACHINE LEARNING - Section Wrap Up

Lecture 70 REINFORCEMENT LEARNING

Lecture 71 ENSEMBLES

Section 10: Introduction to Deep Learning and Neural Networks

Lecture 72 DEEP LEARNING AND NEURAL NETWORKS

Lecture 73 NEURAL NETWORKS: Convolutional Neural Network

Lecture 74 NEURAL NETWORKS: Recurrent Neural Network

Lecture 75 NEURAL NETWORKS: Generative Adversarial Network (GAN)

Lecture 76 Additional Resources

Section 11: Machine Learning Sections Wrap-up

Lecture 77 Choosing AI algorithms

Lecture 78 Additional Resources and Handouts

Lecture 79 Course Wrap up

Lecture 80 Your feedback and more resources

Non-technical leaders and managers,Anyone who is interested in big data, machine learning and artificial intelligence,Professionals considering career switch,People with technical background who want to gain insights in real life applications of data science skills,Anyone who works with coders, data engineers and data scientists and wants to learn basics about big data technology and tools,People without maths or computer science background, but who want to understand how Machine Learning algorithms work