Intro To Big Data, Data Science And Artificial Intelligence
Last updated 12/2021
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 4.05 GB | Duration: 3h 28m
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
Homepage
https://www.udemy.com/course/introduction-to-big-data-data-science/
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