Is The Buckingham Murders based on a true story? Inspiration behind the Netflix film

Arts & Culture, Film & TV

Crime thriller The Buckingham Murders has landed on Netflix, and viewers have been left asking whether the movie is based on real events.The Buckingham murders premiered at the 67th BFI London Film Festival in October 2023, and is fresh from a theatrical release. Now the crime movie has landed on Netflix, it’s really gained traction and attention from global audiences. The story follows British-Indian detective Jass Bhamra, who is grieving the death of her child and takes a job transfer to Buckinghamshire.Once there, where she is assigned to investigate the the case of missing Indian child, Ishpreet. Navigating unfamiliar investigative protocols and clashes among the local Muslim and Hindu communities, the film tackles a multitude of heavy themes alongside the central story of a missing child. Many recent additions to Netflix are based on real events, including Woman of The Hour, and A Confession. This has left viewers also asking if The Buckingham Murders is based on a true story, and here’s what we’ve found out.Is The Buckingham Murders based on a true story?The Buckingham Murders isn’t based on a true story, but is inspired by elements of real events. It also takes themes from the Kate Winslet-led series, Mare of Easttown. However, lead actress Kareen Kapoor who plays Jass, wants the film to remain separate from the series.Speaking to Mashable, she wants viewers to know, “There’s no harm in actors drawing inspiration because every actor needs some sort of motivation. If that motivation comes from another actor, then so be it. Absolutely. It’s flattering to be compared to Mare of Easttown, but this is not Mare of Easttown. It’s adapted from it, yes, but this is a completely different story and film.”The film’s conflict between Muslim and Hindu communities is said to have taken inspiration from the 2022 Leicester riots, that saw tensions run to boiling point between young men in British Muslim and British Hindu communities with Indian heritage.(Image credit: IMDB)Speaking about this ethnic clash with Majestic Disorder, the film’s director, Hansal Mehta, said, “The polarisation and the search for identity has always been a theme in my films from the beginning. It’s a lot about identity and that somewhere people who are searching for their identity and yet have to live life on a day-to-day basis.”Sign up to our free daily email for the latest royal and entertainment news, interesting opinion, expert advice on styling and beauty trends, and no-nonsense guides to the health and wellness questions you want answered.He also spoke about the story involving a lot of Indian actors with knowledge of Indian policing, but the story being set in England. Mehta added “Our initial interpretation of the story was based on procedures that we had seen either in films or what we would follow in India. So it was a mix of that, and I realised midway through that, we need to get this checked.As we went checking the procedural elements, we realised that it’s very different. A detective is not a cop, that’s one of the big differences. So to keep [Kareena’s character] a detective, to show her investigating and yet not being a regular cop, in a uniform with a gun. To have a thriller is more mental than physical.”

The Buckingham Murders | Official Trailer – Hindi| Kareena Kapoor K, Ektaa R Kapoor,Hansal M|Sept 13 – YouTube

Watch On
The Buckingham Murders: CastKareena Kapoor Khan as Jasmeet “Jass” BhamraKeith Allen as MillerRanveer Brar as Daljeet KohliPrabhleen Sandhu as Preeti KohliSarah-Jane Dias as Indrani RaiManish Gandhi as PrithviAsh Tandon as DI Hardik PatelKapil Redekar as Saquib ChaudharyRahul Sidhu as NavedSanjeev Mehra as Kamalpreet BhamraJonathan Nyati as DS CowdenDarren Kemp as DS Simon ClarkCharles Craddock as James ThomasRukku Nahar as HarleenHaqi Ali as ImamAdwoa Akoto as DS Sharon MarkKareena Kapoor who play Jass, has spoken out about her character being a grieving mother, and how this inspired her performance. She told the press, “I think a mother’s love has no language. It’s a feeling. So, I think being a mother I understand that a mother’s love has no specific language. It’s in her eyes – her love, her pain, you can see it in her eyes. That’s important.”

The fastest film to earn Rs 8,43,73,05 from pre-sale in US, its not Kanguva, Singham Again, Bhool Bhulaiyaa 3, Animal, or Stree 2, movie is…

Arts & Culture, Film & TV

Home Entertainment The fastest film to earn Rs 8,43,73,05 from pre-sale in US, its not Kanguva, Singham Again, Bhool Bhulaiyaa 3, Animal, or Stree 2, movie is… This film is set to break multiple records on an international level. Ahead of its release this film has already earned a whopping amount from pre-sales in US.…

AUKUS Nations to Test ‘Offensive and Defensive’ Hypersonic Technologies

Technology

The US Department of Defense has signed an agreement with Australia and the UK to collaborate on “offensive and defensive” hypersonic technologies.
The partnership aims to accelerate the development, testing, and evaluation of cutting-edge hypersonic vehicles and technologies, including long-range missiles capable of traveling considerably faster than the speed of sound.
Known as the Hypersonic Flight Test and Experimentation Project (HyFliTE), the program involves conducting up to six joint flight test campaigns by 2028. The collaboration also focuses on sharing resources, knowledge, and expertise among the three nations. 
The initiative operates under a total funding pool of $252 million.
“This work will keep us ahead of our adversaries on the battlefield, enhance our collective security, and contribute to maintaining peace and stability in an increasingly complex and dangerous world,” UK Defence Secretary John Healey said.
The project includes accelerating the development of crucial technologies such as high-temperature materials, advanced propulsion systems, and guidance and control mechanisms.
“Each of these technologies is integral to the performance of hypersonic weapon systems and provide enhanced operational capability,” explained Heidi Shyu, Under Secretary of Defense for Research and Engineering.
AUKUS Partners
Launched in 2021, the trilateral security partnership between Canberra, London, and Washington supports joint testing and exercise initiatives under two main programs.
The first program focuses on equipping the Royal Australian Navy with nuclear-powered submarines.
The second aims to strengthen operational integration and interoperability, emphasizing advancements in cyber defense, artificial intelligence, quantum technologies, and undersea systems.
Latest Tests
Earlier this year, AUKUS partners conducted several tests to enhance their systems’ interoperability.
Last month, Australia, the UK, and the US remotely operated unmanned vessels across a distance of over 10,000 miles (16,093 kilometers), controlling ships in Australian waters from a command station in Portugal.
In August, the three partners announced the successful tests of AI-enabled unmanned aerial vehicles capable of intercepting, disabling, and destroying enemy assets with precision.

4 Free eBooks to Kickstart Your Data Science Journey

Science and Health

Embarking on a data science journey can feel overwhelming with the plethora of resources available online. However, nothing beats a well-curated book for diving deep into a subject. The best part? You don’t have to break the bank to access high-quality learning materials.
Here are four free eBooks that will provide you with a strong foundation in data science, helping you understand key concepts, practice critical skills, and ultimately start your journey towards mastery.
1. An Introduction to Statistical Learning, by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
An Introduction to Statistical Learning (ISLR) is often regarded as the quintessential textbook for beginners in data science and machine learning. The book provides a broad overview of the key statistical concepts and machine learning techniques that form the core of data science. 
Whether you’re new to linear regression or want to dive into the intricacies of decision trees and support vector machines, ISLR offers a solid introduction in a clear, engaging manner.
One of the standout features of ISLR is its accessibility to readers who may not have a deep background in statistics or mathematics. Each chapter breaks down complex methods with intuitive examples, and the authors provide practical code in both R and Python that helps you get hands-on experience.
This eBook is ideal for anyone looking to build a foundation in data analysis and machine learning without getting lost in heavy mathematical jargon. 
2. Think Stats: Probability and Statistics for Programmers, by Allen B. Downey
If you’re interested in understanding statistics with a programming-first approach, Think Stats is an excellent choice. Written specifically for people who are already familiar with basic data science-related programming such as document annotation and scraping, this book teaches key probability and statistical concepts by encouraging readers to experiment with code.
Allen Downey’s book emphasizes practical application rather than theoretical formulas. Each concept comes with exercises that push you to use Python to explore datasets and get an intuitive grasp of statistical relationships. This focus on applying what you learn through Python makes it a standout option for anyone keen to blend their programming skills with a data-centric mindset.
The book also introduces readers to NumPy and SciPy, two critical libraries for data science in Python, providing a hands-on experience that directly benefits your skills in real-world data analysis. You can access this eBook for free via Green Tea Press.
3. Python Data Science Handbook, by Jake VanderPlas
The Python Data Science Handbook by Jake VanderPlas is a must-read for those who want to delve deep into the Python ecosystem for data science. This book serves as a comprehensive guide to some of the most powerful tools available for data analysis, including Pandas, NumPy, Matplotlib, Scikit-Learn, and more.
The Handbook is designed to be practical and hands-on. Each chapter dives straight into problem-solving mode, providing code snippets and real datasets that you can work on. From data wrangling and visualization to machine learning, this book covers the key steps that make up the data science workflow.
The greatest benefit of the Python Data Science Handbook is how it helps you develop an intimate understanding of Python’s data-centric libraries, empowering you to work on your own projects confidently. If you’re a beginner or intermediate learner looking to cement your Python data science skills, this book is an invaluable resource. 
5. Machine Learning Yearning, by Andrew Ng
Andrew Ng is a prominent figure in the field of machine learning, and his eBook, Machine Learning Yearning, is aimed at helping data scientists and machine learning engineers understand how to structure machine learning projects effectively. While the book doesn’t dive deeply into the mathematics behind algorithms, it provides an invaluable perspective on the practical aspects of implementing machine learning solutions in real-world scenarios.
Machine Learning Yearning is written in an easy-to-read format, with short chapters that make it accessible to readers with any level of experience. It discusses key topics like how to choose the right evaluation metric, strategies for improving model performance, and the importance of iterating on a problem rather than diving headfirst into complex techniques.
This book is perfect if you want to gain a strategic understanding of machine learning projects, especially if your goal is to work in a team environment where structuring projects efficiently is key. You can download the book for free from Andrew Ng’s website.
Getting the Most Out of These eBooks
While having access to these free eBooks is fantastic, the key to success in data science is a combination of consistent practice, curiosity, and hands-on experimentation. Here are a few tips to make the most of these resources:

Set a learning plan: Begin with the basics of statistics and Python, such as Think Stats and Python Data Science Handbook, and gradually move on to more complex topics like those in The Elements of Statistical Learning.
Practice regularly: Theory alone won’t make you a data scientist. Practice coding and solving problems regularly, especially the exercises provided in these books.
Work on projects: Choose datasets that interest you, and use the knowledge gained from these books to create small projects. Platforms like Kaggle are also great places to find inspiration.
Join a community: Engaging with other learners can provide motivation and support. Consider joining online forums, study groups, or social media communities dedicated to data science.

Final Thoughts
The world of data science can appear complex, with numerous subfields and skills required to become proficient. These four eBooks offer a well-rounded introduction to some of the most important concepts, tools, and techniques in data science, giving you a clear path forward in your learning journey.
Whether you’re interested in honing your Python skills, learning statistical foundations, or structuring machine learning projects effectively, these free resources have you covered. Grab these eBooks, start experimenting, and you’ll soon find yourself equipped with the skills and knowledge to tackle more advanced topics in the fascinating world of data science.

4 Free eBooks to Kickstart Your Data Science Journey

Science and Health

Embarking on a data science journey can feel overwhelming with the plethora of resources available online. However, nothing beats a well-curated book for diving deep into a subject. The best part? You don’t have to break the bank to access high-quality learning materials.
Here are four free eBooks that will provide you with a strong foundation in data science, helping you understand key concepts, practice critical skills, and ultimately start your journey towards mastery.
1. An Introduction to Statistical Learning, by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
An Introduction to Statistical Learning (ISLR) is often regarded as the quintessential textbook for beginners in data science and machine learning. The book provides a broad overview of the key statistical concepts and machine learning techniques that form the core of data science. 
Whether you’re new to linear regression or want to dive into the intricacies of decision trees and support vector machines, ISLR offers a solid introduction in a clear, engaging manner.
One of the standout features of ISLR is its accessibility to readers who may not have a deep background in statistics or mathematics. Each chapter breaks down complex methods with intuitive examples, and the authors provide practical code in both R and Python that helps you get hands-on experience.
This eBook is ideal for anyone looking to build a foundation in data analysis and machine learning without getting lost in heavy mathematical jargon. 
2. Think Stats: Probability and Statistics for Programmers, by Allen B. Downey
If you’re interested in understanding statistics with a programming-first approach, Think Stats is an excellent choice. Written specifically for people who are already familiar with basic data science-related programming such as document annotation and scraping, this book teaches key probability and statistical concepts by encouraging readers to experiment with code.
Allen Downey’s book emphasizes practical application rather than theoretical formulas. Each concept comes with exercises that push you to use Python to explore datasets and get an intuitive grasp of statistical relationships. This focus on applying what you learn through Python makes it a standout option for anyone keen to blend their programming skills with a data-centric mindset.
The book also introduces readers to NumPy and SciPy, two critical libraries for data science in Python, providing a hands-on experience that directly benefits your skills in real-world data analysis. You can access this eBook for free via Green Tea Press.
3. Python Data Science Handbook, by Jake VanderPlas
The Python Data Science Handbook by Jake VanderPlas is a must-read for those who want to delve deep into the Python ecosystem for data science. This book serves as a comprehensive guide to some of the most powerful tools available for data analysis, including Pandas, NumPy, Matplotlib, Scikit-Learn, and more.
The Handbook is designed to be practical and hands-on. Each chapter dives straight into problem-solving mode, providing code snippets and real datasets that you can work on. From data wrangling and visualization to machine learning, this book covers the key steps that make up the data science workflow.
The greatest benefit of the Python Data Science Handbook is how it helps you develop an intimate understanding of Python’s data-centric libraries, empowering you to work on your own projects confidently. If you’re a beginner or intermediate learner looking to cement your Python data science skills, this book is an invaluable resource. 
5. Machine Learning Yearning, by Andrew Ng
Andrew Ng is a prominent figure in the field of machine learning, and his eBook, Machine Learning Yearning, is aimed at helping data scientists and machine learning engineers understand how to structure machine learning projects effectively. While the book doesn’t dive deeply into the mathematics behind algorithms, it provides an invaluable perspective on the practical aspects of implementing machine learning solutions in real-world scenarios.
Machine Learning Yearning is written in an easy-to-read format, with short chapters that make it accessible to readers with any level of experience. It discusses key topics like how to choose the right evaluation metric, strategies for improving model performance, and the importance of iterating on a problem rather than diving headfirst into complex techniques.
This book is perfect if you want to gain a strategic understanding of machine learning projects, especially if your goal is to work in a team environment where structuring projects efficiently is key. You can download the book for free from Andrew Ng’s website.
Getting the Most Out of These eBooks
While having access to these free eBooks is fantastic, the key to success in data science is a combination of consistent practice, curiosity, and hands-on experimentation. Here are a few tips to make the most of these resources:

Set a learning plan: Begin with the basics of statistics and Python, such as Think Stats and Python Data Science Handbook, and gradually move on to more complex topics like those in The Elements of Statistical Learning.
Practice regularly: Theory alone won’t make you a data scientist. Practice coding and solving problems regularly, especially the exercises provided in these books.
Work on projects: Choose datasets that interest you, and use the knowledge gained from these books to create small projects. Platforms like Kaggle are also great places to find inspiration.
Join a community: Engaging with other learners can provide motivation and support. Consider joining online forums, study groups, or social media communities dedicated to data science.

Final Thoughts
The world of data science can appear complex, with numerous subfields and skills required to become proficient. These four eBooks offer a well-rounded introduction to some of the most important concepts, tools, and techniques in data science, giving you a clear path forward in your learning journey.
Whether you’re interested in honing your Python skills, learning statistical foundations, or structuring machine learning projects effectively, these free resources have you covered. Grab these eBooks, start experimenting, and you’ll soon find yourself equipped with the skills and knowledge to tackle more advanced topics in the fascinating world of data science.

With Chinese tourism numbers down, Australia is using cricket to target visitors from India

Travel

Tourism Australia’s mascot Ruby the Roo has a new batting partner in Australian cricket captain Pat Cummins, who stars alongside her in a multi-million-dollar campaign aimed at enticing more Indian travellers Down Under.Timed ahead of the Australia-India Test series opener, which starts in Perth on Friday, the new advertising blitz showcases iconic tourist spots like Cape Tribulation in Far North Queensland, Kangaroo Island in South Australia, Western Australia’s Rottnest Island and Sydney’s Palm Beach.Tourism Australia said the Howzat for a Holiday? campaign — which features a series of cheeky social media videos and a television commercial — is expected to reach 50 million Indian viewers. The campaign will also include billboards, signage and print advertisements across India.Tourism Australia is hoping to tap into India’s growing middle class while also recovering from setbacks in the Chinese market, which has been hit hard by geopolitical tensions and the pandemic.An image from Tourism Australia’s new Howzat for a Holiday campaign.