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dracaena draco

dracaena draco Shop 'Dragon Tree

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Description

dracaena draco Shop 'Dragon TreeIntroducing the Dragon Tree, also known as Dracaena draco. The Greek word "drkaina," which means "female dragon," gave rise to the name "dracaena." This refers to the 'Draco' kind of brilliant red resin. The name "dragon tree" is believed to have originated from the red sap that oozes from the trunk when cut, resembling a dragon's blood. This unique characteristic has contributed to its mythical and intriguing reputation. Hailing from Madagascar, it

Introducing the Dragon Tree, also known as Dracaena draco. The Greek word "drákaina," which means "female dragon," gave rise to the name "dracaena." This refers to the 'Draco' kind of brilliant red resin. 

The name "dragon tree" is believed to have originated from the red sap that oozes from the trunk when cut, resembling a dragon's blood. This unique characteristic has contributed to its mythical and intriguing reputation.


Hailing from Madagascar, it is also known as the Madagascar dragon tree and goes by many other common names such as dragon blood tree, corn plant, ribbon plant, Asparagus Draco, and dragon's blood plant.

The dragon tree, a tall, cylindrical tree with sword-shaped leaves, grows tall in the landscape, whereas the houseplant is shorter but has identical leaves.

This dragon tree stands tall and can grow up to 15 feet tall in its natural habitat.

If grown indoors, it can grow up to 4 feet boasting a thick cylindrical trunk adorned with stunning rosettes. These rosettes are no ordinary ones either; they are crowned with magnificent sword-shaped leaves that will make you feel like you've stepped into a mythical kingdom.

The dragon plant flowers bloom from late winter to early spring, with white flowers, which are produced in clusters on the tips of its branches. The flowers emit a strong, sweet fragrance that attracts pollinators such as bees and butterflies. The blooming period is relatively short, lasting only a few weeks.

Interesting Facts

  1. The dragon tree has a thick, gnarled trunk that adds a touch of character and charm to its overall appearance.
  2. It has a long lifespan and can live for several decades, becoming a cherished part of your home.
  3. Its sap is a vibrant red color, giving it a dramatic and eye-catching appearance.
  4. The Dragon Tree is believed to bring good luck and positive energy to its surroundings, according to ancient folklore.

Propagation of the dragon tree can easily be done by stem cuttings. Simply take a cutting from the stem of the healthy plant and allow it to dry out before planting it in well-draining soil. Keep the cutting in a warm, humid environment to encourage root growth.

With its show-stopping presence, this dragon blood tree will undoubtedly add to the landscape of your lovely home. Plus, this slow-growing plant is perfect for those who want to add something truly special to their collection.   

Watering Needs 

The dragon blood tree is a succulent shrub that definitely stands out from the rest. Since it is a drought-tolerant plant, it does not need much water, but should not be overwatered or underwatered. But here's a fun fact - unlike most trees, dragon trees do not tolerate prolonged drought well. 

In fact, during the scorching summer months, it might require watering once a week to keep those arms perky and looking their best. Keep the soil moist but not saturated.

If you ever notice your dragon blood plant gradually curling towards the center, don't freak out! That just means they're thirsty and in need of some water. So, make sure to water them thoroughly when you see this happen (but be careful not to let them stand in watery soil), or else risk facing root rot - yikes! 

Stay on top of your watering game with this quirky dragon plant and watch as they continue to thrive and turn heads with their unique appearance. 

Light Requirements 

If growing indoors, place your Dracaena tree a few feet away from the south-facing window. If they don't receive enough light for a long period, their stems may grow leggy and become paler and their spines will shorten; so, make your house plant happy and give it plenty of light. 

For outdoor cultivation, the dragon tree requires a lot of light and should be grown in either full sun or part shade. New plants can get burned in the full sun as they were probably grown in a greenhouse; therefore, dragon blood tree exposure to sunlight gradually increases. If the sun is too bright and is burning your plant's leaves, consider purchasing a sunshade or moving the plant to a more shaded location.  

The dragon tree can also thrive in filtered or low-light conditions. It can also grow in areas that are partially shaded, but not in those that receive direct sunlight. The plant is vulnerable to exposure to the full sun. 

Optimal Soil & Fertilizer Needs 

Dracaena plant favors very airy, sandy soil that drains well. Planting them in ordinary soil will result in compacted roots, stunted growth, and most likely root rot. Instead, make or buy a well-draining regular potting soil, or ideally use Soil Cactus Mix Blend 1 Gal 4 Qtour specialized potting mix that contains over 5 natural substrates and mycorrhizae to promote the development of a strong root system that helps your blood tree to thrive. 

When it comes to fertilizing your dragon blood trees, it only needs a tiny bit applied once a year in the spring growing season with a balanced liquid fertilizer that has lower doses of NPK (5-10-5). No harsh chemicals here, folks - just pure, natural goodness that lasts longer and encourages nutrient absorption, so your succulent stays happy as a clam.  

So go ahead and give your Dragon's blood plant some love with a good natural fertilizer because when it comes to soil and fertilizers, through experience, we know what works best!  

Hardiness Zones & More 

When growing indoors, the Dragon Tree prefers average room temperatures ranging from 6°F to 75°F. It can tolerate slightly cooler temperatures, but it's best to avoid extreme temperature fluctuations. It is adaptable and can tolerate average indoor humidity levels. However, it appreciates a slightly higher humidity level, so misting the leaves occasionally or placing a humidifier nearby can be beneficial, especially during dry winter months.

For outdoor cultivation, the Dragon Tree is best suited for USDA hardiness zones 9 to 12, which are regions with mild winters and warm climates. The Dragon Tree can tolerate temperatures as low as 30°F for short periods but prefers temperatures above 50°F. It's important to protect it from frost and extreme temperature fluctuations, as they can damage the plant.

Final Thoughts

Overall, the Dragon Tree (Dracaena draco), is a stunning indoor plant with spiky foliage that adds a touch of exotic beauty to any space. It's known for its easy care, making it perfect for beginners. This plant thrives in bright, indirect light and can tolerate lower light conditions too. It likes average room temperatures and can handle normal indoor humidity levels. Just water it when the top inch of soil is dry, and mist the leaves occasionally for a little extra humidity. 

We think you will love this dragon plant – order your very own Dracaena draco - Dragon blood tree for sale and start enjoying its beauty in no time!  

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Par
Whiting, US
★★★★★ 5
Excellent book on ML
Format: Paperback
This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents. The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!! The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!! I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge. Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents. Personally I was disappointed by lack of any example on time series. Must have for ML practitioner as a reference and guide.
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Reviewed in the United States on December 20, 2024
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Richard Hackathorn
Louisville, US
★★★★★ 5
Excellent Textbook for Hands-On Learning of ML
Format: Kindle
This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book. For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch. For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min). From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures. I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications. Several textbook editions later, what is different about this new edition? First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills. Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like. Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
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Reviewed in the United States on February 26, 2022
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Amazon Customer
Cuba, US
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
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Reviewed in the United States on December 10, 2025
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Kindle Customer
Carnegie, US
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
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Reviewed in the United States on May 3, 2026
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Tommy Jonsson
Charlottesville, US
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
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Reviewed in the United States on May 4, 2026

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