Machine learning (ML) is a branch of artificial intelligence (AI) that focuses on enabling computers to “think” for themselves. Of course, they owe this thinking to humans (data scientists and ML engineers) who continuously supervise ML algorithms and models.
So, there’s no AI takeover (for now at least), just incredible ways to propel several industries forward by automating repetitive tasks, extracting valuable insights from data, and improving decision-making processes.
But how do humans precisely communicate with computers in machine learning?
The answer is through programming languages.
One programming language stands out among the rest for its simplicity and versatility. By the title of this guide, you can already guess we’re talking about Python.
This beloved programming language is all over the machine learning field, so mastering it gives you a great head start in the industry.
With this in mind, let’s examine how you can learn Python for machine learning courses. If you already have some basic knowledge of this programming language, don’t worry. We’ll also mention a great machine learning Python course to take your knowledge to the next level.
Factors to Consider When Choosing a Python for ML Course
Do a Google search for “machine learning Python course,” and you’ll be met with dozens of web pages that promise a sound understanding of this programming language. However, you’ll find the best course for your needs if you can identify those needs first.
Course Content and Curriculum
Your chosen course’s curriculum is arguably the most important factor for selecting the perfect machine learning Python course. One look at the listed topics, and you’ll know whether the course is right for you.
Let’s take your previous experience with Python as an example. If you have none, a course that jumps straight into machine learning algorithms without covering the Python basics will obviously not work for you.
Instructor’s Expertise and Experience
What bridges the gap between struggling to comprehend a complex subject and feeling that nothing can stop you in your learning journey? The answer is simple – a good instructor.
Before committing to a course, check who teaches it. Find out the instructor’s background with Python and whether they have enough expertise to guide you through this programming language’s intricacies.
If their bio checks all the boxes, watch at least one of their lectures. It doesn’t hurt to check whether their teaching style and voice suit you, as these can also make or break your learning experience.
Course Duration and Flexibility
Most online courses are self-paced, allowing you to create your own schedule. Fixed-timing courses also have their benefits, though. They’re usually instructor-led, so you can use the opportunity to ask questions and receive clarification as you learn the material.
As for duration, the course’s description typically indicates how long the course lasts and the recommended pace. Before starting, make sure you can commit to the course from beginning to end. Otherwise, you’re just wasting time and gaining incomplete knowledge.
Hands-On Projects and Real-World Applications
Programming languages are inherently practical, so ensure that your chosen course features hands-on projects and practical examples. Sticking solely to theory will do little to prepare you for what’s waiting in the real world.
Course Reviews and Ratings
You probably check reviews before going to a new restaurant, renting an Airbnb, or purchasing clothes online. So why should shopping for online courses be any different? When a course piques your interest, check how other learners have rated it. But don’t stop at glancing at the average rating. Read through some reviews to ensure they aren’t fake and to get a better picture of the course’s quality.
Pricing and Value for Money
There are plenty of free machine learning resources online. But the more advanced courses and certificates usually come with a fee. And that’s perfectly understandable. What’s not understandable or acceptable are courses that charge ridiculously high fees yet offer little value. To avoid wasting money (and probably time), check whether the course’s price is justifiable by its duration, level, type, and provided support.
Top Python for ML Courses Reviewed
Here are our favorite Python courses primarily focused on machine learning. We’re positive you’ll find the perfect machine learning Python course, whether this is the first time you use this programming language or want to master this skill.
Python for Machine Learning
The Python for Machine Learning course on Great Learning is a great place to start your Python-learning journey. This course is beginner-friendly and relatively short, so you won’t get overwhelmed from the get-go.
This course focuses on three Python libraries: NumPy, Pandas, and Matplotlib. It guides you through the basic concepts (arrays, intersection, loading, etc.) and then moves on to more complex functions. At the end of the course, you take a quiz. Pass the quiz, and you’ll get a certificate of completion.
Applying for this course is free. Not only that, but you’ll also receive free lifetime access, so you can revisit the course whenever you’d like. Although, some learners believe that there’s little to revisit. In total, this course lasts for 90 minutes. Those who are serious about Python learning will probably need more than this.
Still, you can view this course as a beginner’s guide and move to more advanced lessons afterward. To apply, you only need to create an account on the platform and send an enrollment request.
Machine Learning A-Z: AI, Python & R
If you want to start with the basics but cover the more advanced stuff within the same course, this Udemy’s gem is for you. It covers another programming language besides Python, R. However, this won’t be an issue, as you can focus solely on Python.
The course is broken into 10 parts, with over 40 hours of on-demand videos. Each section (and even the lessons within them) is separate, so you can choose to complete the ones that will benefit you now. Start with data preprocessing, and work toward machine learning model selection.
Those seeking practical exercises in Python will love this course. However, you might need to research some notions independently, as not all lecture sections are explained in great detail.
You can purchase lifetime access to this course for $89.99 (a little over €83). The price includes a certificate of completion and several additional learning materials (articles and downloadable resources). Complete the purchase to apply for this course.
Machine Learning With Python by IBM
IBM is one of the leading companies in the machine learning field, so you should take advantage of every chance to learn from its experts. If you’re just gaining your footing in machine learning, you’ll cover all your bases with this offering.
It will take approximately 12 hours over four weeks to complete the coursework. After each lesson, you’ll get a chance to put your newly-learned knowledge to the test.
One thing to keep in mind is that this course focuses more on machine learning using Python than the programming language itself. So, if you’ve never worked with Python, an additional resource or two might come in handy.
You can use Coursera’s 7-day trial to enroll in this course. Afterward, you’ll be charged $39 (approximately €36) a month. The same fee is a must if you want to receive a certificate.
The Complete Machine Learning Course With Python
Are you a data scientist in the making looking to build a solid portfolio with Python? If yes, you’ll love this course. You can find it on Udemy, just like millions of learners before you. This number might surprise you at first. But once you see that one of the founders of this course is Andrew Ng, a thought leader in machine learning, it will make much more sense.
In 18 hours, this course covers all the basics of machine learning with Python. But there’s a catch. You’ll need at least basic Python programming knowledge to keep up.
If this isn’t an issue, create an Udemy account and pay the $59.99 (around €55.50) fee to apply. Lifetime access and a certificate of completion are included.
Programming for Everybody (Getting Started With Python)
While not focused on machine learning per se, this course is necessary for anyone who has yet to work with Python. Pair it with one of the other courses on our list, and your success is guaranteed.
As the name implies, this course covers all the basics. It is designed to allow virtually anyone to follow, regardless of their skills. The simplest math is all you need.
You’ll also need 19 hours to complete this course offered by the University of Michigan. However, the instructor snuck a couple of non-Python-related stories into those 19 hours, which some learners didn’t like.
If you don’t mind a break here and there, join this course on Coursera for free or $49 (a little over €45) if you want a certificate.
Additional Resources for Learning Python for Machine Learning
Perhaps you can’t get enough of learning about Python. Or you find Python for machine learning courses lacking information. Whatever the case, you can find additional resources (both online and offline) to help you master this programming language. Check out some of our favorites:
- Books and e-books: “Python for Data Science, for Dummies,” “Introduction to Machine Learning with Python: A Guide for Data Scientists,” “Python Data Science Handbook: Essential Tools for Working with Data”
- Blogs: Planet Python, Real Python
- YouTube channels: IBM Technology, Google Career Certificates, techTFQ
- Community forums and discussion groups: Kaggle Discussions, Reddit (r/learnpython)
The Path to Python
As you can see, there’s no shortage of Python for machine learning courses, even hosted by some of the biggest names in the industry. Take one of the listed courses or combine them; the choice is all yours. All that matters is that you ultimately master this programming language and crush any data science career you choose.
If these courses aren’t enough to quench your thirst for knowledge, a Bachelor’s in Modern Computer Science will definitely do the trick. With it, you can learn all the ins and outs of Python and machine learning in general.
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From the same author
Read the articleSource:
- Agenda Digitale, published on November 25th, 2025
In recent years, the word ” sustainability ” has become a firm fixture in the corporate lexicon. However, simply “doing no harm” is no longer enough: the climate crisis , social inequalities , and the erosion of natural resources require a change of pace. This is where the net-positive paradigm comes in , a model that isn’t content to simply reduce negative impacts, but aims to generate more social and environmental value than is consumed.
This isn’t about philanthropy, nor is it about reputational makeovers: net-positive is a strategic approach that intertwines economics, technology, and corporate culture. Within this framework, digitalization becomes an essential lever, capable of enabling regenerative models through circular platforms and exponential technologies.
Blockchain, AI, and IoT: The Technological Triad of Regeneration
Blockchain, Artificial Intelligence, and the Internet of Things represent the technological triad that makes this paradigm shift possible. Each addresses a critical point in regeneration.
Blockchain guarantees the traceability of material flows and product life cycles, allowing a regenerated dress or a bottle collected at sea to tell their story in a transparent and verifiable way.
Artificial Intelligence optimizes recovery and redistribution chains, predicting supply and demand, reducing waste and improving the efficiency of circular processes .
Finally, IoT enables real-time monitoring, from sensors installed at recycling plants to sharing mobility platforms, returning granular data for quick, informed decisions.
These integrated technologies allow us to move beyond linear vision and enable systems in which value is continuously regenerated.
New business models: from product-as-a-service to incentive tokens
Digital regeneration is n’t limited to the technological dimension; it’s redefining business models. More and more companies are adopting product-as-a-service approaches , transforming goods into services: from technical clothing rentals to pay-per-use for industrial machinery. This approach reduces resource consumption and encourages modular design, designed for reuse.
At the same time, circular marketplaces create ecosystems where materials, components, and products find new life. No longer waste, but input for other production processes. The logic of scarcity is overturned in an economy of regenerated abundance.
To complete the picture, incentive tokens — digital tools that reward virtuous behavior, from collecting plastic from the sea to reusing used clothing — activate global communities and catalyze private capital for regeneration.
Measuring Impact: Integrated Metrics for Net-Positiveness
One of the main obstacles to the widespread adoption of net-positive models is the difficulty of measuring their impact. Traditional profit-focused accounting systems are not enough. They need to be combined with integrated metrics that combine ESG and ROI, such as impact-weighted accounting or innovative indicators like lifetime carbon savings.
In this way, companies can validate the scalability of their models and attract investors who are increasingly attentive to financial returns that go hand in hand with social and environmental returns.
Case studies: RePlanet Energy, RIFO, and Ogyre
Concrete examples demonstrate how the combination of circular platforms and exponential technologies can generate real value. RePlanet Energy has defined its Massive Transformative Purpose as “Enabling Regeneration” and is now providing sustainable energy to Nigerian schools and hospitals, thanks in part to transparent blockchain-based supply chains and the active contribution of employees. RIFO, a Tuscan circular fashion brand, regenerates textile waste into new clothing, supporting local artisans and promoting workplace inclusion, with transparency in the production process as a distinctive feature and driver of loyalty. Ogyre incentivizes fishermen to collect plastic during their fishing trips; the recovered material is digitally tracked and transformed into new products, while the global community participates through tokens and environmental compensation programs.
These cases demonstrate how regeneration and profitability are not contradictory, but can actually feed off each other, strengthening the competitiveness of businesses.
From Net Zero to Net Positive: The Role of Massive Transformative Purpose
The crucial point lies in the distinction between sustainability and regeneration. The former aims for net zero, that is, reducing the impact until it is completely neutralized. The latter goes further, aiming for a net positive, capable of giving back more than it consumes.
This shift in perspective requires a strong Massive Transformative Purpose: an inspiring and shared goal that guides strategic choices, preventing technology from becoming a sterile end. Without this level of intentionality, even the most advanced tools risk turning into gadgets with no impact.
Regenerating business also means regenerating skills to train a new generation of professionals capable not only of using technologies but also of directing them towards regenerative business models. From this perspective, training becomes the first step in a transformation that is simultaneously cultural, economic, and social.
The Regenerative Future: Technology, Skills, and Shared Value
Digital regeneration is not an abstract concept, but a concrete practice already being tested by companies in Europe and around the world. It’s an opportunity for businesses to redefine their role, moving from mere economic operators to drivers of net-positive value for society and the environment.
The combination of blockchain, AI, and IoT with circular product-as-a-service models, marketplaces, and incentive tokens can enable scalable and sustainable regenerative ecosystems. The future of business isn’t just measured in terms of margins, but in the ability to leave the world better than we found it.
Read the articleSource:
- Raconteur, published on November 06th, 2025
Many firms have conducted successful Artificial Intelligence (AI) pilot projects, but scaling them across departments and workflows remains a challenge. Inference costs, data silos, talent gaps and poor alignment with business strategy are just some of the issues that leave organisations trapped in pilot purgatory. This inability to scale successful experiments means AI’s potential for improving enterprise efficiency, decision-making and innovation isn’t fully realised. So what’s the solution?
Although it’s not a magic bullet, an AI operating model is really the foundation for scaling pilot projects up to enterprise-wide deployments. Essentially it’s a structured framework that defines how the organisation develops, deploys and governs AI. By bringing together infrastructure, data, people, and governance in a flexible and secure way, it ensures that AI delivers value at scale while remaining ethical and compliant.
“A successful AI proof-of-concept is like building a single race car that can go fast,” says Professor Yu Xiong, chair of business analytics at the UK-based Surrey Business School. “An efficient AI technology operations model, however, is the entire system – the processes, tools, and team structures – for continuously manufacturing, maintaining, and safely operating an entire fleet of cars.”
But while the importance of this framework is clear, how should enterprises establish and embed it?
“It begins with a clear strategy that defines objectives, desired outcomes, and measurable success criteria, such as model performance, bias detection, and regulatory compliance metrics,” says Professor Azadeh Haratiannezhadi, co-founder of generative AI company Taktify and professor of generative AI in cybersecurity at OPIT – the Open Institute of Technology.
Platforms, tools and MLOps pipelines that enable models to be deployed, monitored and scaled in a safe and efficient way are also essential in practical terms.
“Tools and infrastructure must also be selected with transparency, cost, and governance in mind,” says Efrain Ruh, continental chief technology officer for Europe at Digitate. “Crucially, organisations need to continuously monitor the evolving AI landscape and adapt their models to new capabilities and market offerings.”
An open approach
The most effective AI operating models are also founded on openness, interoperability and modularity. Open source platforms and tools provide greater control over data, deployment environments and costs, for example. These characteristics can help enterprises to avoid vendor lock-in, successfully align AI to business culture and values, and embed it safely into cross-department workflows.
“Modularity and platformisation…avoids building isolated ‘silos’ for each project,” explains professor Xiong. “Instead, it provides a shared, reusable ‘AI platform’ that integrates toolchains for data preparation, model training, deployment, monitoring, and retraining. This drastically improves efficiency and reduces the cost of redundant work.”
A strong data strategy is equally vital for ensuring high-quality performance and reducing bias. Ideally, the AI operating model should be cloud and LLM agnostic too.
“This allows organisations to coordinate and orchestrate AI agents from various sources, whether that’s internal or 3rd party,” says Babak Hodjat, global chief technology officer of AI at Cognizant. “The interoperability also means businesses can adopt an agile iterative process for AI projects that is guided by measuring efficiency, productivity, and quality gains, while guaranteeing trust and safety are built into all elements of design and implementation.”
A robust AI operating model should feature clear objectives for compliance, security and data privacy, as well as accountability structures. Richard Corbridge, chief information officer of Segro, advises organisations to: “Start small with well-scoped pilots that solve real pain points, then bake in repeatable patterns, data contracts, test harnesses, explainability checks and rollback plans, so learning can be scaled without multiplying risk. If you don’t codify how models are approved, deployed, monitored and retired, you won’t get past pilot purgatory.”
Of course, technology alone can’t drive successful AI adoption at scale: the right skills and culture are also essential for embedding AI across the enterprise.
“Multidisciplinary teams that combine technical expertise in AI, security, and governance with deep business knowledge create a foundation for sustainable adoption,” says Professor Haratiannezhadi. “Ongoing training ensures staff acquire advanced AI skills while understanding associated risks and responsibilities.”
Ultimately, an AI operating model is the playbook that enables an enterprise to use AI responsibly and effectively at scale. By drawing together governance, technological infrastructure, cultural change and open collaboration, it supports the shift from isolated experiments to the kind of sustainable AI capability that can drive competitive advantage.
In other words, it’s the foundation for turning ambition into reality, and finally escaping pilot purgatory for good.
-
Go to all authors
Related posts
Source:
- Agenda Digitale, published on November 25th, 2025
In recent years, the word ” sustainability ” has become a firm fixture in the corporate lexicon. However, simply “doing no harm” is no longer enough: the climate crisis , social inequalities , and the erosion of natural resources require a change of pace. This is where the net-positive paradigm comes in , a model that isn’t content to simply reduce negative impacts, but aims to generate more social and environmental value than is consumed.
This isn’t about philanthropy, nor is it about reputational makeovers: net-positive is a strategic approach that intertwines economics, technology, and corporate culture. Within this framework, digitalization becomes an essential lever, capable of enabling regenerative models through circular platforms and exponential technologies.
Blockchain, AI, and IoT: The Technological Triad of Regeneration
Blockchain, Artificial Intelligence, and the Internet of Things represent the technological triad that makes this paradigm shift possible. Each addresses a critical point in regeneration.
Blockchain guarantees the traceability of material flows and product life cycles, allowing a regenerated dress or a bottle collected at sea to tell their story in a transparent and verifiable way.
Artificial Intelligence optimizes recovery and redistribution chains, predicting supply and demand, reducing waste and improving the efficiency of circular processes .
Finally, IoT enables real-time monitoring, from sensors installed at recycling plants to sharing mobility platforms, returning granular data for quick, informed decisions.
These integrated technologies allow us to move beyond linear vision and enable systems in which value is continuously regenerated.
New business models: from product-as-a-service to incentive tokens
Digital regeneration is n’t limited to the technological dimension; it’s redefining business models. More and more companies are adopting product-as-a-service approaches , transforming goods into services: from technical clothing rentals to pay-per-use for industrial machinery. This approach reduces resource consumption and encourages modular design, designed for reuse.
At the same time, circular marketplaces create ecosystems where materials, components, and products find new life. No longer waste, but input for other production processes. The logic of scarcity is overturned in an economy of regenerated abundance.
To complete the picture, incentive tokens — digital tools that reward virtuous behavior, from collecting plastic from the sea to reusing used clothing — activate global communities and catalyze private capital for regeneration.
Measuring Impact: Integrated Metrics for Net-Positiveness
One of the main obstacles to the widespread adoption of net-positive models is the difficulty of measuring their impact. Traditional profit-focused accounting systems are not enough. They need to be combined with integrated metrics that combine ESG and ROI, such as impact-weighted accounting or innovative indicators like lifetime carbon savings.
In this way, companies can validate the scalability of their models and attract investors who are increasingly attentive to financial returns that go hand in hand with social and environmental returns.
Case studies: RePlanet Energy, RIFO, and Ogyre
Concrete examples demonstrate how the combination of circular platforms and exponential technologies can generate real value. RePlanet Energy has defined its Massive Transformative Purpose as “Enabling Regeneration” and is now providing sustainable energy to Nigerian schools and hospitals, thanks in part to transparent blockchain-based supply chains and the active contribution of employees. RIFO, a Tuscan circular fashion brand, regenerates textile waste into new clothing, supporting local artisans and promoting workplace inclusion, with transparency in the production process as a distinctive feature and driver of loyalty. Ogyre incentivizes fishermen to collect plastic during their fishing trips; the recovered material is digitally tracked and transformed into new products, while the global community participates through tokens and environmental compensation programs.
These cases demonstrate how regeneration and profitability are not contradictory, but can actually feed off each other, strengthening the competitiveness of businesses.
From Net Zero to Net Positive: The Role of Massive Transformative Purpose
The crucial point lies in the distinction between sustainability and regeneration. The former aims for net zero, that is, reducing the impact until it is completely neutralized. The latter goes further, aiming for a net positive, capable of giving back more than it consumes.
This shift in perspective requires a strong Massive Transformative Purpose: an inspiring and shared goal that guides strategic choices, preventing technology from becoming a sterile end. Without this level of intentionality, even the most advanced tools risk turning into gadgets with no impact.
Regenerating business also means regenerating skills to train a new generation of professionals capable not only of using technologies but also of directing them towards regenerative business models. From this perspective, training becomes the first step in a transformation that is simultaneously cultural, economic, and social.
The Regenerative Future: Technology, Skills, and Shared Value
Digital regeneration is not an abstract concept, but a concrete practice already being tested by companies in Europe and around the world. It’s an opportunity for businesses to redefine their role, moving from mere economic operators to drivers of net-positive value for society and the environment.
The combination of blockchain, AI, and IoT with circular product-as-a-service models, marketplaces, and incentive tokens can enable scalable and sustainable regenerative ecosystems. The future of business isn’t just measured in terms of margins, but in the ability to leave the world better than we found it.
Source:
- Raconteur, published on November 06th, 2025
Many firms have conducted successful Artificial Intelligence (AI) pilot projects, but scaling them across departments and workflows remains a challenge. Inference costs, data silos, talent gaps and poor alignment with business strategy are just some of the issues that leave organisations trapped in pilot purgatory. This inability to scale successful experiments means AI’s potential for improving enterprise efficiency, decision-making and innovation isn’t fully realised. So what’s the solution?
Although it’s not a magic bullet, an AI operating model is really the foundation for scaling pilot projects up to enterprise-wide deployments. Essentially it’s a structured framework that defines how the organisation develops, deploys and governs AI. By bringing together infrastructure, data, people, and governance in a flexible and secure way, it ensures that AI delivers value at scale while remaining ethical and compliant.
“A successful AI proof-of-concept is like building a single race car that can go fast,” says Professor Yu Xiong, chair of business analytics at the UK-based Surrey Business School. “An efficient AI technology operations model, however, is the entire system – the processes, tools, and team structures – for continuously manufacturing, maintaining, and safely operating an entire fleet of cars.”
But while the importance of this framework is clear, how should enterprises establish and embed it?
“It begins with a clear strategy that defines objectives, desired outcomes, and measurable success criteria, such as model performance, bias detection, and regulatory compliance metrics,” says Professor Azadeh Haratiannezhadi, co-founder of generative AI company Taktify and professor of generative AI in cybersecurity at OPIT – the Open Institute of Technology.
Platforms, tools and MLOps pipelines that enable models to be deployed, monitored and scaled in a safe and efficient way are also essential in practical terms.
“Tools and infrastructure must also be selected with transparency, cost, and governance in mind,” says Efrain Ruh, continental chief technology officer for Europe at Digitate. “Crucially, organisations need to continuously monitor the evolving AI landscape and adapt their models to new capabilities and market offerings.”
An open approach
The most effective AI operating models are also founded on openness, interoperability and modularity. Open source platforms and tools provide greater control over data, deployment environments and costs, for example. These characteristics can help enterprises to avoid vendor lock-in, successfully align AI to business culture and values, and embed it safely into cross-department workflows.
“Modularity and platformisation…avoids building isolated ‘silos’ for each project,” explains professor Xiong. “Instead, it provides a shared, reusable ‘AI platform’ that integrates toolchains for data preparation, model training, deployment, monitoring, and retraining. This drastically improves efficiency and reduces the cost of redundant work.”
A strong data strategy is equally vital for ensuring high-quality performance and reducing bias. Ideally, the AI operating model should be cloud and LLM agnostic too.
“This allows organisations to coordinate and orchestrate AI agents from various sources, whether that’s internal or 3rd party,” says Babak Hodjat, global chief technology officer of AI at Cognizant. “The interoperability also means businesses can adopt an agile iterative process for AI projects that is guided by measuring efficiency, productivity, and quality gains, while guaranteeing trust and safety are built into all elements of design and implementation.”
A robust AI operating model should feature clear objectives for compliance, security and data privacy, as well as accountability structures. Richard Corbridge, chief information officer of Segro, advises organisations to: “Start small with well-scoped pilots that solve real pain points, then bake in repeatable patterns, data contracts, test harnesses, explainability checks and rollback plans, so learning can be scaled without multiplying risk. If you don’t codify how models are approved, deployed, monitored and retired, you won’t get past pilot purgatory.”
Of course, technology alone can’t drive successful AI adoption at scale: the right skills and culture are also essential for embedding AI across the enterprise.
“Multidisciplinary teams that combine technical expertise in AI, security, and governance with deep business knowledge create a foundation for sustainable adoption,” says Professor Haratiannezhadi. “Ongoing training ensures staff acquire advanced AI skills while understanding associated risks and responsibilities.”
Ultimately, an AI operating model is the playbook that enables an enterprise to use AI responsibly and effectively at scale. By drawing together governance, technological infrastructure, cultural change and open collaboration, it supports the shift from isolated experiments to the kind of sustainable AI capability that can drive competitive advantage.
In other words, it’s the foundation for turning ambition into reality, and finally escaping pilot purgatory for good.
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