SKU: 60383616264

Massage Oil Candle

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Ships within 48 hours · Estimated delivery Aug 23 - Aug 28

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Description

Massage Oil CandleA massage oil candle offers a unique and sensual way to enhance relaxation and intimacy. Crafted from a blend of nourishing oils such as soy wax, sweet almond oil, and shea butter, it melts into a pool of warm, luxurious liquid when lit. The flickering flame releases a delicate fragrance that fills the room, creating an ambiance of warmth and tranquility. Whether enjoyed alone as a self care ritual or shared with a partner for a romantic massage, a

A massage oil candle offers a unique and sensual way to enhance relaxation and intimacy. Crafted from a blend of nourishing oils such as soy wax, sweet almond oil, and shea butter, it melts into a pool of warm, luxurious liquid when lit. The flickering flame releases a delicate fragrance that fills the room, creating an ambiance of warmth and tranquility.

Whether enjoyed alone as a self-care ritual or shared with a partner for a romantic massage, a massage oil candle provides a decadent treat for the senses. Its soothing warmth and nourishing properties help melt away tension and promote relaxation, while the intimate act of massage fosters connection and intimacy. It's a luxurious indulgence that invites you to unwind, pamper yourself, and indulge in the pleasures of touch.

How to use:

Light the candle for 15-20 minutes or until oil reaches a full melt pool, blow out the flame, pour the oil onto your hand then proceed to massage on the body.

Why you'll love it:

Massage oil embodies a wonderful experience to give a luxury massage at home with oil that lasts and absorbs into the skin smoothly.

Why You Can Shop With Confidence

✔ Made in small batches for freshness
✔ Rich, long-lasting fragrance that stays with you
✔ Glides beautifully into skin
✔ Secure checkout & protected payment processing
✔ Clear scent descriptions so you know exactly what to expect
✔ Real customer results & testimonials

Our massage oil candles are designed to deeply hydrate dry skin and support skin that needs extra care. Many of our customers love how nourished and comforted their skin feels after consistent use.

Shipping:

Most orders ship within 1-2 business days.

6.25 oz candle

35-40 hours burn time

Scents

Sophisticated:

Pink sugar consists of a sweet, playful, and youthful scent profile. The fragrance is often characterized by top notes of sugary cotton candy, strawberry, and raspberry, giving it a delightful and candy-like sweetness. These notes are usually complemented by heart notes of caramel, licorice, and red berries, adding depth and a touch of indulgence. Base notes such as vanilla, musk, and sandalwood provide a warm and creamy finish.

Midnight Woods:

Midnight Woods fragrances feature a rich, warm, and slightly spicy scent profile. The fragrance combines deep, woody notes with a touch of sweetness and musk. Key notes include mahogany, blood orange, tonka, complemented by hints of vanilla, amber, and sandalwood. 

Sexy Thang:

A sexy and smooth scent consisting of notes such as strawberry, jasmine, lilac, rose, musk & sandalwood.

The Gentleman:

A bold clean scent with top notes of lemon, lavender, and marine accord with mid notes of rose and jasmine on a dry base of sandalwood, cedarwood, and musk.

Unscented:

Great for customers who may be prefer an unscented massage experience.


 

 

Shipping Notes
  • Free Standard Shipping on $100+ Orders to the USA.
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  • Delivery to the USA:
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Exchange/Return Notes
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  • Final sale items are not eligible for returns or exchanges.
  • To process your return/exchange, please contact us at [email protected]
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SKU: 60383616264

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4.1 ★★★★★
Based on 24 reviews
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Product Reviews
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Verified Purchase
Par
Pawtucket, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 20, 2024
R
Verified Purchase
Richard Hackathorn
West Palm Beach, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on February 26, 2022
A
Verified Purchase
Amazon Customer
Lake Worth, 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
Lowell, 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
Cuba, 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

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