SKU: 22076292418

Milwaukee M18 BTP-802 Akku Transferpumpe 18 V 1817 l/h + 2x Akku 8,0 Ah + Ladegerät

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Description

Milwaukee M18 BTP-802 Akku Transferpumpe 18 V 1817 l/h + 2x Akku 8,0 Ah + LadegerätLieferumfang: 1x Milwaukee M18 BTP Akku Transferpumpe 18 V 2x Milwaukee M18 HB8 18 V 8,0 Ah Li Ion High Output Akku 1x Milwaukee M12 18 FC Universal Akku Schnell Ladegert Produktbeschreibung: Die Milwaukee M18 BTP Akku Transferpumpe ist ein leistungsstarkes Gert, das mit einem 18 V Akku betrieben wird. Mit einem Gewicht von nur 3,4 kg ist diese Transferpumpe leichter als vergleichbare Kabelgerte und bietet somit eine hohe Tragbarkeit. Die Pumpe verfgt

Lieferumfang:

- 1x Milwaukee M18 BTP Akku Transferpumpe 18 V
- 2x Milwaukee M18 HB8 18 V 8,0 Ah Li-Ion High Output Akku
- 1x Milwaukee M12-18 FC Universal Akku Schnell Ladegerät

Produktbeschreibung:

Die Milwaukee M18 BTP Akku Transferpumpe ist ein leistungsstarkes Gerät, das mit einem 18-V-Akku betrieben wird. Mit einem Gewicht von nur 3,4 kg ist diese Transferpumpe leichter als vergleichbare Kabelgeräte und bietet somit eine hohe Tragbarkeit. Die Pumpe verfügt über eine maximale Fördermenge von 1817 l/h, wodurch bis zu 908 Liter pro M18™ Akkuladung gefördert werden können. Die REDLINK™-Elektronik sorgt dafür, dass das Gerät automatisch abgeschaltet wird, wenn kein Wasser mehr angesaugt wird. Dadurch wird der Akku geschont und die Lebensdauer der Pumpe verlängert. Die flexible Antriebswelle und der leistungsstarke Motor ermöglichen eine maximale Ansaughöhe von 5,5 m und eine maximale Förderhöhe von 23 m. Dadurch eignet sich die Pumpe ideal für verschiedene Anwendungen in Haushalt und Garten. Die Milwaukee M18 BTP Akku Transferpumpe ist mit einem ¾″-Außengewinde ausgestattet, das für marktübliche Wasseranschlüsse geeignet ist. Für optimale Leistung wird ein Schlauch mit 19-mm-Innendurchmesser empfohlen. Bitte beachten Sie, dass diese Pumpe ausschließlich für Klarwasser geeignet ist und nicht zum Pumpen von Schmutzwasser verwendet werden sollte. Die Milwaukee M18 BTP Akku Transferpumpe ist zu 100 % systemkompatibel mit dem MILWAUKEE® M18™-Produktprogramm. Das bedeutet, dass der Akku dieser Pumpe mit allen anderen M18™-Geräten von Milwaukee verwendet werden kann. Dadurch wird Flexibilität und Effizienz bei Ihrer Arbeit gewährleistet.

Technische Daten:

Hersteller: Milwaukee
Herstellerbezeichnung: M18 BTP
Akku: Li-ion
Spannung (V): 18
Durchflussmenge (l/h): 1817
Durchflussmenge [Liter pro Minute]: 30,3
Gewindegröße (mm): ¾″ (19 mm) Außengewinde
IP Schutzklasse: IP54
Max. Ansaughöhe (m): 5,5
Max. Förderhöhe (m): 23
Max. Wassertemperatur (°C): 60
Gewicht [ohne Akku] [Kg]: 3,4
Gewicht mit Akku (EPTA) (kg): 4,1 (M18 B5)


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SKU: 22076292418

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4.3 ★★★★★
Based on 23 reviews
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A
Verified Purchase
Amazon Customer
Chelsea, US
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 10, 2025
K
Verified Purchase
Kindle Customer
Belleville, US
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 3, 2026
T
Verified Purchase
Tommy Jonsson
Carnegie, US
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 4, 2026
M
Verified Purchase
Moses Kayanda
Dallas, US
★★★★★ 5
One of the best machine learning books...
Format: Paperback, Format: Paperback
Machine Learning can often be intimidating whether you are starting out or already a practitioner. It is easy to get stuck on one concept, walk away frustrated, or just copy that code you find on StackOverflow without really understanding what it does. What the authors of this book, Machine Learning with PyTorch and Scikit-Learn, have managed to do is to keep the reader engaged giving a deeper illustration as to how the concepts work. In this book, you get practical code examples, a detailed explanation of how the various library tools work, and exposure to the mathematical concepts behind machine learning algorithms. In addition, what I like about the book unlike many machine learning books is that the authors have managed to intuitively explain how each algorithm works, how to use them, and the mistake you need to avoid. I have not read a Machine Learning book that better explains Transformers as this one does. The authors have managed to give a detailed dive into this model architecture through well-explained codes and illustrations. As a reader, you walk away having intuitively grasped the concepts of attention and self-attention in ways that will make this crucial NLP architecture clear. You get exposed to pre-trained models from HuggingFace library which really helps to have that hands-on experience working with large datasets. As they have done throughout the book, the authors have broken down those complex mathematical operations into simple explanations that are easy to follow. What I generally like about the book is how it seamlessly connects all the chapters, not throwing off the reader. There are numerous external resources quoted throughout the book. This helps spark that curiosity to dig deeper. In addition, you get introduced to PyTorch, getting exposed to all those sophisticated libraries that help the reader learn how to maximize their compute power. I would say it is not intimidating at all even if you have not used PyTorch before. I would recommend this book to anybody seeking a textbook that is both easy to read and modern in its content. If were to rate the book I will give it a 10/10 as it really applies to both beginners and experienced practitioners, covers all the concepts one needs to apply in their operations, and acts as a quick reference.
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Reviewed in the United States on March 1, 2022
G
Verified Purchase
Gabe Rigall
Massapequa, US
★★★★★ 5
Thorough Primer for Machine Learning and PyTorch
Format: Paperback
BLUF: A thorough primer for machine learning enthusiasts with plenty of theory to underscore its many practical examples. A definite must-have for anyone looking to add PyTorch to their machine learning tool belt. PROS: - Extremely thorough (if not comprehensive). I really appreciate that this book doesn't just thrust one into building models with PyTorch. It starts at the "beginning" and provides examples, theory, additional resources, and citations along the way. - Theory. Those whose calculus and linear algebra courses ended many years ago will appreciate (if not remember exactly) the mathematical theory and notation that accompanies almost every paragraph. This book gives one the opportunity to "dig deeper" or stay in the shallows until the notation stops. - Python. Rather than simply utilizing Scikit-Learn to illustrate concepts and introduce models, this book contains many sections where models (such as a Perceptron) are coded from the ground up so the reader can fully understand the underlying mechanics. Python enthusiasts will nerd out. Parents of small children might want to skip a few pages. - Graphs, charts, and graphics. There are plenty of places where a drier text might have foregone the use of graphs. This text does not. It does however refrain from overusing them. - PyTorch. This should be obvious from the title, but this text prioritizes PyTorch instead of TensorFlow. This is especially helpful for those looking for an alternative to Keras and TensorFlow as the PyTorch API is very user-friendly. CONS: - Almost too much code. This isn't a true "con" but anyone wanting to emulate or follow along with the examples would do well to get the digital edition so they can copy and paste. - Length and complexity. Anyone hoping for a "quick read" or a "quick start guide" will be disappointed. This book hovers somewhere between an undergraduate primer and a graduate-level text for length and readability. This is not to say that it's difficult to read, merely that there are other "quick start" / "practical" texts out there that cater more to a lay audience.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on February 26, 2022

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