There are several informative data science podcasts out there right now, giving you everything you need to stay up to date on what’s happening. We previously covered many of the best podcasts in this blog, but there are lots more that you should be checking out. Here are 10 more excellent podcasts to try out.
1. Analytics Power Hour
Every week hosts, Michael Helbling, Tin Wilson, and Moe Kiss cover a different analytics topic that you may want to know about. The show was founded on the premise that the best discussions always happen at drinks after a conference or show.
Recent episodes have covered topics like analytics job interviews, data as a product, and owning vs. helping in analytics. There are a lot to learn here, so they’re well worth a listen.
This podcast is hosted by DataCamp, and in it, you’ll get interviews with some of the top leaders in data. “These interviews cover the entire range of data as an industry, looking at its past, present, and future. The guests are from both the industry and academia sides of the data spectrum too” says Graham Pierson, a tech writer at Ox Essays and UK Top Writers.
There are lots of episodes to dive into, such as ones on building talent strategy, what makes data training programs successful, and more.
3. Lex Fridman Podcast
If you want a bigger picture of data science, then listen to this show. The show doesn’t exclusively cover data science anymore, but there’s plenty here that will give you what you’re looking for.
You’ll find a broader view of data, covering how data fits in with our current worldview. There are interviews with data experts so you can get the best view of what’s happening in data right now.
4. The Artists of Data Science
This podcast is geared toward those who are looking to develop their career in data science. If you’re just starting, or are looking to move up the ladder, this is for you. There’s lots of highly useful info in the show that you can use to get ahead.
There are two types of episodes that the show releases. One is advice from experts, and the others are ‘happy hours, where you can send in your questions and get answers from professionals.
5. Not So Standard Deviations
This podcast comes from two experts in data science. Roger Peng is a professor of biostatistics at John Hopkins School of Public Health, and Hilary Parker is a data scientist at Stitch Fix. They cover all the latest industry news while bringing their own experience to the discussion.
Their recent episodes have covered subjects like QR codes, the basics of data science, and limited liability algorithms.
Released twice a month, this podcast will give you all the ins and outs of machine learning, showing you how this tech is used in real-life situations. That allows you to see how it’s being used to solve problems and create solutions that we couldn’t have before.
Recent episodes have covered high-stress scenarios, experience management, and autonomous checkouts.
7. In Machines We Trust
This is another podcast that covers machine learning. It describes itself as covering ‘the automation of everything, so if that’s something you’re interested in, you’ll want to make sure you tune in.
“You’ll get a sense of what machine learning is being used for right now, and how it impacts our daily lives,” says Yvonne Richards, a data science blogger at Paper Fellows and Boom Essays. The episodes are around 30 mins long each, so it won’t take long to listen and get the latest info that you’re looking for.
8. More or Less
This podcast covers the topic of statistics through noticeably short episodes, usually 8 minutes or less each. You’ll get episodes that cover everything you could ever want to know about statistics and how they work.
For example, you can find out how many swimming pools of vaccines would be needed to give everyone a dose, see the one in two cancers claim debunked, and how data science has doubled life expectancy.
9. Data Engineering Podcast
This show is for anyone who’s a data engineer or is hoping to become one in the future. You’ll find lots of useful info in the podcast, including the techniques they use, and the difficulties they face.
Ensure you listen to this show if you want to learn more about your role, as you’ll pick up a lot of helpful tips.
10. Data viz Today
This show doesn’t need a lot of commitment from you, as they release 30 min episodes monthly. The podcast covers data visualization, and how this helps to tell a story and get the most out of data no matter what industry you work in.
Share with us exciting Data Science podcasts
These are all great podcasts that you can check out to learn more about data science. If you want to know more, you can check out Data Science Dojo’s informative sessions on YouTube. If we missed any of your favorite podcasts, do share them with us in the comments!
In this blog, we have gathered the top 10 machine learning books. Learning this subject is a challenge for beginners. Take your learning experience one step ahead with these top-rated ML books on Amazon.
1. Machine Learning: 4 Books in 1
Machine Learning: 4 Books in 1 is a complete guide for beginners to master the basics of Python programming and understand how to
build artificial intelligence through data science. This book includes four books: Introduction to Machine Learning, Python Programming for
Beginners, Data Science for Beginners, and Artificial Intelligence for Beginners. It covers everything you need to know about machine learning, including supervised and unsupervised learning, regression and classification, feature engineering, model selection, and more. Muhammad Junaid – Marketing manager, BTIP
With clear explanations and practical examples, this book will help you quickly learn the essentials of machine learning and start building your own AI applications.
2. Mathematics for Machine Learning
Mathematics for Machine Learning is a tool that helps you understand the mathematical foundations of machine learning, so that you
can build better models and algorithms. It covers topics such as linear algebra, probability, optimization, and statistics. With this book, you
will be able to learn the mathematics needed to develop machine learning models and algorithms. Daniel – Founder, Gadget FAQs
This book is excellent for brushing up your mathematics knowledge required for ML. It is very concise while still providing enough details to help readers determine important parts. This is the go-to if you need to review some concepts or brush up on my knowledge in general.
This book is not recommended if you have absolutely no prior math experience though as it can be hard to digest and sometimes, they would skip parts here and there in proofs and examples. Especially for the probability section, the concepts will be very hard to grasp without prior knowledge
3. Linear Algebra and Optimization for Machine Learning
This textbook provides a comprehensive introduction to linear algebra and optimization, two fundamental topics in machine learning. It
covers both theory and applications and is suitable for students with little or no background in mathematics. Allan McNabb, VP – Image Building Media
The book begins with a review of basic linear algebra, before moving on to more advanced topics such as matrix decompositions, eigenvalues and eigenvectors, singular value decomposition, and least squares methods. Optimization techniques are then introduced, including gradient descent, Newton’s Method, conjugate gradient methods, and interior point methods.
4. The Hundred-Page Machine Learning Book
If we have to teach machine learning to someone in juts few weeks, it is a lot better not to bother starting from scratch, instead hand over this book to the learners, because no doubt Andriy Burkov does a better job than we could do to quickly teach this vast subject in a limited time.
The book has a litany of rave reviews from some of the biggest names in tech, with scores more five-star reviews to boot, and you can see why. Burkov keeps his lessons concise and as easy to understand as possible given the subject matter, but still drills down into the details where necessary. Overall, the book excels at linking together complicated and sometimes seemingly unrelated concepts into a coherent whole. Peter, CEO and founder – Lantech
The book is very well organized, giving the reader an introduction and discussion on the mathematical notation used, a well written chapter that discusses several quite common algorithms, talks about best practices (like feature engineering, breaking up the data into multiple sets, and tuning the model’s hyperparameters), digs deeper into supervised learning, discusses unsupervised learning, and gives you a taste of a variety of other related topics.
This is a well-rounded book, far more so than most books I’ve read on machine learning or artificial intelligence. After reading through this, you will feel like you can competently discuss the subject, read one of the simpler machine learning research papers, and not be totally lost on the mathematics involved. The language used is concise and reads very well, showing very tight editing
5. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
It’s good for new programmers without over-simplifying. I’d recommend it for really getting into practice exercises. It’s a book you need to take your time with, but you’ll learn a lot from it. One thing observed by the learners of this book as a con is that the quality of the print varies, but the quality of its content makes it more than worth it. Chris Martinez – Founder of Idiomatic
This book covers many topics of ML and explains them with good examples. However, it should be a little bit tough for a beginner. Similarly, it could not be the best book for an advanced reader because it gives pointers for advanced topics but does not go in-depth like mathematical explanation. In summary, it is an excellent book if you are looking for real-life examples with python code and you have a good basic idea in ML.
6. Machine Learning for Absolute Beginners by Oliver Theobald
Machine Learning is easy only when you have the right teacher and an appropriate reference book. Most of us fail to understand the importance of simple concepts that help us understand complex ones. Therefore, I recommend using Oliver Theobald’s *Machine Learning for Absolute Beginners *as the base reference book. Layla Acharya – Owner at Edwize
This book uses simple language to explain to the reader and teaches Machine learning from the scratch. Although non-technical people will find this book more relatable, people wanting to make a career in the machine learning field can benefit equally. It also has good references that can help a person who wants to learn like an expert.
7. Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD by Jeremy Howard and Sylvain Gugger
This book is very well-rated and it’s helped me a lot in understanding the basics of deep learning.
The main reason readers suggest this book is because it’s very accessible and easy to follow. As the authors themselves say, you don’t need a PhD to understand and use the concepts in the book, and it follows a top-down approach (starting with the applications and working backwards to the theory). So, you’ll first have fun with building cool applications and then gradually learn the underlying theory as you go. Ed Shway – Owner & Writer at ByteXD.com
Fast AI have kept updating their courses and library, so you might want to check out their website (https://www.fast.ai/) for the latest and greatest Just this July they released a latest version of the course that the book is associated with (https://course.fast.ai/).
Furthermore, the book also comes in a free online version https://github.com/fastai/fastbook. Since the *Fast AI team put all this effort and made every resource available for free, you can be sure they’re in it for the love of the game and to help the community*, rather than to make a quick buck. So, this book is definitely worth your time.
The first practical applications it teaches you is in computer vision – you’ll build an image classifier, which you can use to tell apart different
kinds of images. For example, you can use it to distinguish between different kinds of animals. It will be very easy to follow along and build
this classifier yourself.
8. Bayesian Reasoning and Machine Learning by David Barber
It’s a real must-have for beginners interested in deepening their knowledge of machine learning in an engaging way. The book covers topics such as dynamic and probabilistic models, approximate interference, graphical models, Naive Bayes algorithms, and more. What makes it worth checking out is the fact that the book is full of examples and exercises, which makes it a hands-on guide full of useful practice rather than dry theoretical frameworks. Marcin Gwizdala – Chief Technical Officer – Tidio
For relative beginners, Bayesian techniques began in the 1700s to model how a degree of belief should be modified to account for new evidence. The techniques and formulas were largely discounted and ignored until the modern era of computing, pattern recognition and AI, now machine learning.
The formula answers how the probabilities of two events are related when represented inversely, and more broadly, gives a precise mathematical model for the inference process itself (under uncertainty), where deductive reasoning and logic becomes a subset (under certainty, or when values can resolve to 0/1 or true/false, yes/no etc. In “odds” terms (useful in many fields including optimal expected utility functions in decision theory), posterior odds = prior odds * the Bayes Factor.
9. Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools by Eli Stevens, Luca Antiga, Thomas Viehmann
This book provides a good and fairly complete description of the basic principles and abstractions of one of the most popular frameworks for
Machine Learning – PyTorch.
It’s great that this book is written by the creator and key contributors of PyTorch, unlike many books that claim to be a definitive treatise, it is not overloaded with non-essential details, the emphasis is on making the book practical. The book gives a reader a deep understanding of the framework and methods for building and training models on it (with advanced best practices) describing what is under the hood. Vitalii Kudelia, TUTU – Machine Learning Scientist
There is an example of solving a real-world problem in this book, it analyzes the problem of searching for malignant tumors on a computer
diagram with an analysis of approaches, possible errors, options for improvements, and provides code examples.
It also includes options for translating the model into production, using the models in other programming languages, and on mobile devices.
As a result, the book is highly useful for understanding and mastering the framework. Mastering PyTorch helps not only in computer vision, but also in other areas of deep learning, such as, for example, natural language processing.
10. Introduction to Machine Learning by Ethem Alpaydin
This comprehensive text covers everything from the basics of linear algebra to more advanced topics like support vector machines. In addition to being an excellent resource for students, Alpaydin’s book is also very accessible for practitioners who want to learn more about this exciting field. Rajesh Namase – Co-Founder and Tech Blogger
For learners, this is the best book for machine learning for a number of reasons. First, the book provides a clear and concise introduction to the basics of machine learning. Second, it covers a wide range of topics in machine learning, including supervised and unsupervised learning, feature selection, and model selection.
Third, the book is well-written and easy to understand. Finally, the book includes exercises and solutions at the end of each
chapter, which is extremely helpful for readers who want to learn more about machine learning.
Share more machine learning books with us
If you have read any other interesting machine learning books, share with us in the comments below and let us help the learners to begin with computer vision.
What can be a better way to spend your days listening to interesting bits about trending AI and Machine learning topics? Here’s a list of the 10 best AI and ML podcasts.
1. The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Artificial intelligence and machine learning are fundamentally altering how organizations run and how individuals live. It is important to discuss the latest innovations in these fields to gain the most benefit from technology. The TWIML AI Podcast outreaches a large and significant audience of ML/AI academics, data scientists, engineers, tech-savvy business, and IT (Information Technology) leaders, as well as the best minds and gather the best concepts from the area of ML and AI.
The podcast is hosted by a renowned industry analyst, speaker, commentator, and thought leader Sam Charrington. Artificial intelligence, deep learning, natural language processing, neural networks, analytics, computer science, data science, and other technologies are discussed.
2. The AI Podcast
One individual, one interview, one account. This podcast examines the effects of AI on our world. The AI podcast creates a real-time oral history of AI that has amassed 3.4 million listens and has been hailed as one of the best AI and machine learning podcasts. They always bring you a new story and a new 25-minute interview every two weeks. Consequently, regardless of the difficulties, you are facing in marketing, mathematics, astrophysics, paleo history, or simply trying to discover an automated way to sort out your kid’s growing Lego pile, listen in and get inspired.
3. Data Skeptic
Data Skeptic launched as a podcast in 2014. Hundreds of interviews and tens of millions of downloads later, we are a widely recognized authoritative source on data science, artificial intelligence, machine learning, and related topics.
Data Skeptic runs in seasons. By speaking with active scholars and business leaders who are somehow involved in our season’s subject, we probe it.
We carefully choose each of our visitors using a system internally. Since we do not cooperate with PR firms, we are unable to reply to the daily stream of unsolicited submissions. Publishing quality research to the arxiv is the greatest approach to getting on the show. It is crawled. We will locate you.
Data Skeptic is a boutique consulting company in addition to its podcast. Kyle participates directly in each project our team undertakes. Our work primarily focuses on end-to-end machine learning, cloud infrastructure, and algorithmic design.
The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.
Podcast.ai is entirely generated by artificial intelligence. Every week, they explore a new topic in-depth, and listeners can suggest topics or even guests and hosts for future episodes. Whether you are a machine learning enthusiast, just want to hear your favorite topics covered in a new way or even just want to listen to voices from the past brought back to life, this is the podcast for you.
The podcast aims to put incremental advances into a broader context and consider the global implications of developing technology. AI is about to change your world, so pay attention.
5. The Talking Machines
Talking machines is a podcast hosted by Katherine Gorman and Neil Lawrence. The objective of this show is to bring you clear conversations with experts in the field of machine learning, insightful discussions of industry news, and useful answers to your questions. Machine learning is changing the questions we can ask of the world around us, here we explore how to ask the best questions and what to do with the answers.
6. Linear Digressions
If you are interested in learning about unusual applications of machine learning and data science. In each episode of linear digressions, your hosts explore machine learning and data science through interesting apps. Ben Jaffe and Katie Malone host the show, they assure themselves to produce the most exciting additions in the industry such as AI-driven medical assistants, open policing data, causal trees, the grammar of graphics and a lot more.
7. Practical AI: Machine Learning, Data Science
Making artificial intelligence practical, productive, and accessible to everyone. Practical AI is a show in which technology professionals, businesspeople, students, enthusiasts, and expert guests engage in lively discussions about Artificial Intelligence and related topics (Machine Learning, Deep Learning, Neural Networks, GANs (Generative adversarial networks), MLOps (machine learning operations) (machine learning operations), AIOps, and more).
The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the latest advances in AI, while keeping one foot in the real world, then this is the show for you!
8. Data Stories
Enrico Bertini and Moritz Stefaner discuss the latest developments in data analytics, visualization, and related topics. The data stories podcast consists of regular new episodes on a range of discussion topics related to data visualization. It shares the importance of data stories in different fields including statistics, finance, medicine, computer science, and a lot more to name. The podcast’s hosts Enrico and Moritz invite industry leaders, experienced professionals, and instructors in data visualization to share the stories and the importance of representation of data visuals into appealing charts and graphs.
9. The Artificial Intelligence Podcast
The Artificial intelligence podcast is hosted by Dr. Tony Hoang. This podcast talks about the latest innovations in the artificial intelligence and machine learning industry. The recent episode of the podcast discusses text-to-image generator, Robot dog, soft robotics, voice bot options, and a lot more.
10. Learning Machines 101
Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. The objective of this podcast series is to inform students and instructors about the advanced technologies introduced by AI and the following:
How do these devices work?
Where do they come from?
How can we make them even smarter?
And how can we make them even more human-like?
Have we missed any of your favorite podcasts?
Do not forget to share in comments the names of your most favorite AI and ML podcasts. Read this amazing blog if you want to know about Data Science podcasts.
In this blog, we have gathered the top 7 computer vision books. Learning this subject is a challenge for beginners. Take your learning experience one step ahead with these seven computer vision books. Explore a range of topics, from Computer vision to Python.
1. Learning openCV 4 computer vision with Python 3 book by Joe Minichino and Joseph Howse:
This book will teach you how to create a computer vision system using Python. You will learn how to use the OpenCV library, which is a cross-platform library that has been used in many research and commercial projects.Joe and Joseph in this book introduces computer vision and OpenCV with Python programming language.
Both novices and seasoned pros alike will find something of use in this book’s extensive coverage of the subject of CV. It explains how to use Open CV 4 and Python 3 across several platforms to execute tasks like image processing and video analysis and comprehension. Machine learning algorithms and their many uses will be covered in this course. With these ideas in hand, you may design your image and video object detectors! ~ Adam Crossling, Marketing manager at Zenzero
2. Multiple view geometry in computer vision book by Richard Hartley:
This book discusses the use of geometry and algebra in image reconstruction, with applications to computer vision. In this book, Richard discusses the geometry of images and how they are processed in this area. The book covers topics such as image formation, camera models, image geometry, and shape from shading.
The main goal of this book is to provide a comprehensive introduction to computer vision by focusing on the geometric aspects of images. This article describes a wide variety of tactics, from traditional to innovative, to make it very evident when particular approaches are being employed.
Camera projection matrices, basic matrices (which project an image into 2D), and the trifocal tensor are all introduced, along with their algebraic representations, in this book. It explains how to create a 3D model using a series of photographs taken at various times or in different sequences.
3. Principles, algorithms, applications, learning book by E. R. Davies:
New developments in technology have given rise to an exciting academic discipline: computer vision. The goal of this field is to understand information about objects and their environment by creating a mathematical model from digital images or videos, which can be used to extract meaningful data for analysis or classification purposes.
This book teaches its readers not just the basics of the subject but also how it may be put to use and gives real-world scenarios in which it might be of benefit.
4. Deep learning for vision systems by Mohamed Elgendy:
This book should be the go-to text for anyone looking to learn about how machine learning works in AI (Artificial Intelligence) and, fundamentally, how the computer sees the world. By using only the simplest algebra a high school student would be able to understand, they can demonstrate some overly complicated topics within the AI engineering world.
Through illustrations as well as Elgendy’s expertise, the book is the most accurate yet simplest way to understand computer vision for the modern day. ~ Founder & CEO of Lantech
5. Digital image processing by Rafael C. GONZALES and Richard E. Woods:
Image processing is one of the topics that form the core of Computer Vision and DIP by Gonzalez is one of the leading books on the topic. It provides the user with a detailed explanation of not just the basics like feature extraction and image morphing but also more advanced concepts like wavelets and superpixels. It is good for both beginners and people who need to refresh their basics.
It also comes with MATLAB exercises to help the reader understand the concepts practically. Senior Machine Learning Developer, AltaMLRafael C. GONZALES and Richard E. Woods wrote this book to provide an introduction to digital image processing for undergraduate students and professionals who are interested in this field.
The book covers the fundamentals of image formation, sampling and quantization, the design of analog-to-digital converters, image enhancement techniques such as filtering and edge detection, image compression techniques such as JPEG and MPEG, digital watermarking techniques for copyright protection purposes and more advanced topics like fractal analysis in texture synthesis.
6. Practical machine learning for computer vision: End-to-end machine by Martin Görner, Ryan Gillard, and Valliappa Lakshmanan:
Learning for Images. This tutorial shows how to extract information from images using machine learning models. ML (Machine Learning) engineers and data scientists will learn how to use proven ML techniques such as classification, object detection, autoencoders, image generation, counting, and captioning to solve a variety of image problems.
You will find all aspects of deep learning from start to finish, including dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Valliappa Lakshmanan, Martin Görner, and Ryan Gillard of Google show how to use robust ML architecture to develop accurate and explainable computer vision ML models and put them into large-scale production in a flexible and maintainable manner. You will learn how to use TensorFlow or Keras to design, train, evaluate, and predict models. Senior IT Director at Propnex
Further, this book provides a great introduction to deep end-to-end learning for computer vision, including how to design, train, and deploy models. You will learn how to select appropriate models for various tasks, preprocess images for better learnability, and incorporate responsible AI best practices. The book also covers how to monitor and manage image models after deployment. You will also learn how to put your models into large-scale production using robust ML architecture. The authors are Google engineers with extensive experience in the field, so you can be confident you are learning from the best. – Will Cannon, CEO, and Founder of Uplead
7. Computer vision by Richard Szeliski:
This book is all about algorithms and applications. This book is perfect for undergraduate students in computer science as it aims to provide a comprehensive course in computer vision. It is also known as the bible of computer vision. The focus of this book is on the algorithm, application, and techniques for image processing and recognition in CV.
It also helps one to get an understanding of the real-based applications and further discuss the implementation and practical challenges of techniques in computer vision. Co-Founder at Twiz LLC
If you are interested in teaching senior-level courses in this subject, then this book is for you as it can help you to learn more techniques and enhance your knowledge about computer vision.
Share more computer vision books with us
If you have read any other interesting computer vision book, share with us in the comments below and let us help the learners to begin with computer vision.
Are you interested in learning more about IoT? Do you want to network with people working in IoT? Here is a list of 10 IoT conferences and events that can help you learn more about the new research and developments, and help you network and meet recruiters or project owners.
The ongoing development of the Internet of Things (IoT) is a major driver of digital transformation and cutting-edge latest innovations.
Data processing, data visualization, and many other techniques are just a few of the innovative technologies that may be combined to create new possibilities and solutions that demand improved integration and collaboration.
1. 4th Asia IoT Technologies Conference– Beijing, China
Scheduled to be held in Beijing, China, from 6th to 8th January 2023, the Asia IoT Technologies Conference, sponsored by Beijing Huaxia Rongzhi Institute of Blockchain (BJIB), co-sponsored by Beijing University of Technology (China) and the Faculty of Information Technology (BJUT, China).
The conference will focus on core technologies and IoT solutions and IoT applications to promote the integration of IoT and the economy for industrial and economic purposes.
The conference features a broad range of programs and talks on the latest developments in the field. The main aim of the conference is to deepen the understanding of the masses and take the necessary actions to accelerate the adoption of IoT with an emphasis on diverse topics across the IoT landscape.
More details regarding the conference can be found here.
2. International Conference on Innovations in Data Analytics ICIDA – Kolkata, India
Taking place on November 29-30, 2022, the International Conference on Innovations in Data Analytics ICIDA will be organized by International Knowledge Research Foundation in collaboration with Eminent College of Management and Technology (ECMT), West Bengal, India.
The main aim of the conference is to bring together innovators, academics, and business specialists in the fields of Computing and Communication at one place. The conference also aims to inspire young scholars to learn newly created avenues of research at an international academic forum.
More details regarding the conference can be found here.
3. Asia IoT Business Platform – Southeast Asia
Taking place in different cities in Southeast Asia from October to December, the Asia IoT Business Platform aims to serve public and private organizations to enable their access and exchange of knowledge on development and innovation in the B2B sector.
The conferences help create partnerships within the tech and IoT sectors and help provide better collaborations between public and private organizations.
The AIBP conferences and exhibitions also promote market research and access gained via the creation and implementation of business growth strategies.
The IoT India Expo will be held from the 27th to the 29th of March 2023 and will feature numerous companies working in IT, enabling them to enter a new market more quickly and with more accurate data through the adoption of modern technologies.
For anyone in the IT sector, the event is a good place to network and talk about the future of technology because it is the premier enterprise event for IoT, Blockchain, AI, Big Data, Cyber Security, and Cloud.
More details regarding the expo can be found here.
5. Cloud Expo Asia – Marina Bay Sands, Singapore
One of the leading IoT events in Asia, Cloud Expo Asia Is expected to be held from 12th to 13th October 2022.
The main aim of the event is to connect people from academia and professionals with experts in the field to find sustainable solutions and services that can help accelerate digital transformation.
With multiple conferences, shows, speaker sessions, and much more lined up, the event focuses on a large variety of topics.
6. IEEE 8th World Forum on Internet of Things (WF-IoT) – Yokohama, Japan
One of the events organized by the Multi-Society IEEE IoT Initiative WF-IOT will take place from the 26th of October till the 11th of November.
The conference highlights the latest developments in IoT, business, and private and public sectors.
The main aim of the forum is to promote the development and promotion of IoT for society’s and humanity’s benefit, as well as to promote the ethical and responsible use of IoT applications and solutions to improve human lives.
The theme of the event this year is ‘Sustainability and the Internet of Things.’
9. International Conference on Internet of Medical Things (ICIMT) – UAE
The International Conference on the Internet of Medical Things (ICIMT) focuses on exchanging experiences and research findings within the Internet of Medical Things.
The conference gives researchers, practitioners, and educators a world-class interdisciplinary forum on which to present and debate the most recent advancements, concerns, and trends on the Internet of Medical Things.