Choose the Right Laptop for Data Science and Machine Learning
If you’re learning Data Science and Machine Learning, you definitely need a laptop. This is because you need to write and run your own code to get hands-on experience. When you also consider portability, the laptop is the best option instead of a desktop.
A traditional laptop may not be perfect for your data science and machine learning tasks. You need to consider laptop specifications carefully to choose the right laptop. If you’re looking to buy a laptop for data science and machine learning tasks, this post is for you! Here, I’ll discuss 20 necessary requirements of a perfect laptop data science and machine learning tasks.
Let’s get started!
Requirement 1: Generation of the Processor
Always consider buying newer-generation processors. Intel 11th Gen processors and AMD 5th Gen (5000 Series) processors are now available. Intel 8th Gen, Intel 10th Gen and AMD 3rd Gen (3000 Series), AMD 4th Gen (4000 Series) processors are other options that you can consider. However, the processing power, new hardware compatibility, power efficiency and thermal management drastically increase with newer-generation processors.
Requirement 2: Number of Cores and Threads
This is one of the most critical requirements that you should consider. Most of the machine learning tasks require parallel computations. For example, when you train a Random Forest algorithm or performing hyperparameter tuning, you can specify a higher number of cores to be used by the algorithm when your processor has a higher number of cores. This will speed up the process significantly. Cores are the number of independent CPUs in a single chip. They are hardware. Threads are instructions that can be processed by a single CPU core. Always consider buying a laptop with a higher number of CPU cores and threads especially if your laptop doesn’t come with a separate (discrete) graphic card (GPU). 4-cores — 8-threads processor is the minimum requirement that I can recommend for you. If you can afford more money, you can go for 6-cores — 12-threads or 8-cores — 16-threads or higher.
Requirement 3: Base Clock Speed (Frequency)
The base frequency is the minimum speed of the processor. The higher the base frequency, the faster your processor. This is typically measured in Gigahertz (GHz).
Requirement 4: Cache Memory
Cache memory acts as a buffer between RAM and the CPU. It holds frequently-used data and instructions so that they are immediately available to the CPU when used again. The higher the cache memory, the faster your computer. Cache memory is typically measured in Megabytes (MB). An 8 MB of cache memory is recommended.
Requirement 5: Supported Memory Types, Size and Speed
It is worth considering this if you’re planning to upgrade the memory in the future. Recommend memory type is DDR-4, the size is 8 GB and the speed is 3200 MHz.
By considering the above requirements, I can recommend the following processors for you.
- AMD Ryzen 7 4700U (Cores: 8, Threads: 8, Base clock: 2.0 GHz, Cache: 8 MB)
- AMD Ryzen 5 4500U (Cores: 6, Threads: 6, Base clock: 2.3 GHz, Cache: 8 MB)
- AMD Ryzen 7 4800H (Cores: 8, Threads: 16, Base clock: 2.9 GHz, Cache: 8 MB)
- AMD Ryzen 5 4600H (Cores: 6, Threads: 12, Base clock: 3.0 GHz, Cache: 8 MB)
- Intel Core i5–1135G7 (Cores: 4, Threads: 8, Base clock: 2.4 GHz, Cache: 8 MB)
- Intel Core i7–10700 (Cores: 8, Threads: 16, Base clock: 2.9 GHz, Cache: 16 MB)
This is a list of my choice. You have the freedom to choose the right processor by considering both the above requirements and your budget.
Requirement 6: RAM Size
This is considered by most people. But keep in mind that increasing the RAM size does not speed up your computer. Higher RAM sizes will allow you to multi-tasking. At least 8 GB RAM size is recommended. I do not recommend 4 GB RAM because the operating system already takes about 3 GB of the RAM and only 1 GB is available for other tasks. If you can afford and your laptop supports, upgrading to 12 GB or 16 GB is a perfect option. You will often want to install virtual operating systems on your laptop for big data analytics. Such virtual operating systems needs at least 4 GB of RAM. The current operating system tasks about 3 GB RAM. In this case, 8 GB of RAM will not be enough and 12 GB and 16 GB are perfect options.
Requirement 7: RAM Bus Speed
The recommended speed is 2666 MHz. Do not go below this. Modern DDR-4 RAMs support 3200 MHz of bus speed. The higher the bus speed, the faster your computer.
Requirement 8: Storage Type
This is the most critical requirement. Traditional laptops come with HDDs (Hard Disk Drives). HDDs are really really slow. If you buy an i7 laptop with a traditional HDD, your laptop will be really slow. It takes much time to boot up and open programs. HDDs have moving mechanical parts which delay the processing of information and reduce reliability and durability. Therefore, it is necessary to buy or upgrade your laptop with an SSD (Solid State Drive). SSDs are significantly powerful than HDDs. They have no moving parts and provide superior performance. NVMe SSD is an upgrade version of a normal SSD. They are 6x faster than a normal SSD. If your laptop motherboard supports for NVMe SSDs, you can consider upgrading. You can even replace your HDD with a normal SSD, but not with an NVMe SSD. For this, you should buy an SSD with a 2.5-inch form factor, not an M.2 form factor. NVMe SSDs do not support the 2.5-inch form factor.
Requirement 9: Storage Size
If you consider buying a laptop with SSD, you may not be able to afford money to buy a 1 TB SSD because it is much expensive. The ideal size is 512 GB. Do not go below this.
Requirement 10: Brand
NVIDIA and AMD are the two major brands of graphics cards. Tensorflow deep learning library uses the CUDA processor which compiles only on NVIDIA graphics cards. Therefore, I highly recommend you buy a laptop with an NVIDIA GPU if you’re planning to do deep learning tasks. A GTX 1650 or higher GPU is recommended. Another advantage of having a separate graphics card is that an average GPU has more than 100 cores, but a standard CPU has 4 or 8 cores.
Requirement 11: GPU Size
At least a 4 GB GPU is recommended.
Requirement 12: Blue Light Filtering Feature (Eye care)
This is something you should definitely consider for. Your eyes are really really worth. Data science and machine learning students spend hours of time in front of a laptop. Most electronic devices with displays emit harmful blue light and laptops do also. However, modern laptops have blue light filtering technology and flicker-free screens. Another great option is buying a monitor and connecting it with your existing laptop. Most modern monitors come with certified low-blue light, flicker-free screens.
Requirement 13: Display Size
A 15.6-inch or 17.3-inch display is highly recommended. Do not go below this. Having a 22-inch or 24-inch display is the perfect option. Therefore, consider buying a monitor and connecting it with your laptop.
Requirement 14: Display Resolution
A full-HD (1080p) or an HD (720p) display is recommended.
I can't tell you to buy a specific brand. It is totally up to you for deciding it. Here are some points to consider for.
Requirement 15: Reliability
Reliability refers to how often your laptop fails when operating. Unexpected shutdowns, blue screen errors and other hardware failures are the most common issues.
Requirement 16: Durability
Durability refers to how long you can use your laptop. You can guess the durability of your laptop by looking at its warranty period. If you can use your laptop for at least 4 years, it will not waste your money.
Requirement 17: After-sales and Technical support
Your chosen brand should provide product manuals, upgrade options and technical support.
Requirement 18: Upgradability
Your chosen brand should provide easy upgrade options for adding RAM and secondary storage (e.g. SSDs)
Requirement 19: Keyboard Type
A full-size keyboard with a number pad is highly recommended.
Requirement 20: Operating System (OS)
Windows 10 is recommended. It is user-friendly. If you want, other operating systems such as Linux can be easily run virtually within the Windows OS.
I hope you’ll buy the right laptop next time!
My readers can sign up for a membership through the following link to get full access to every story I write and I will receive a portion of your membership fee.
Sign-up link: https://rukshanpramoditha.medium.com/membership
Thank you so much for your continuous support! See you in the next story. Happy learning to everyone!
20 Necessary Requirements of a Perfect Laptop for Data Science and Machine Learning Tasks? ›
For data science applications and workflows, 16GB of RAM is recommended. If you're looking to train large complex models locally, HP offers configurations of up to 128GB of blazing-fast DDR5 RAM.Which laptop is good to data science machine learning? ›
- HP Envy 17T. HP is a well-known brand in the world of electronics and laptops. ...
- Apple MacBook Pro. ...
- ASUS VivoBook 15. ...
- ASUS ROG Strix G17. ...
- Lenovo Thinkpad P53s. ...
- Dell G15. ...
- Prometheus XVI. ...
- Acer Swift 3.
- CPU- Intel Core i5-8265U up to 3.9 GHz.
- RAM- 16GB DDR4 RAM.
- GPU- Don't have dedicated GPU.
- Storage- 256GB-2TB SSD (Varies from Model to Model)
- Desktop- 14” TN FHD.
- Weight: 3.41 lbs.
|Memory 16GB minimum||Hard Drive SSD is preferred 500GB minimum (or 256 GB and an external hard drive)|
|CPU Intel i5 minimum Intel i7 or i9 preferred (M1 and M2 NOT recommended)||Operating Systems Windows 10 or 11 (Home or Pro) Mac: High Sierra or later Linux: recent distribution|
For data science applications and workflows, 16GB of RAM is recommended. If you're looking to train large complex models locally, HP offers configurations of up to 128GB of blazing-fast DDR5 RAM.How much RAM do I need for data science laptop? ›
The amount of RAM required for data science is at least 8 GB, and any less, and you'll struggle to develop many of the current state-of-the-art models. You can always increase up to 64 GB and beyond, but this is often overkill and too much. However, some other things to consider when you're making your purchase.Is 16GB RAM enough for machine learning? ›
8 to 16 GB of Random Access Memory (RAM) is ideal for data science on a computer. Data science requires relatively good computing power. 8 GB is sufficient for most data analysis work but 16 GB is more than sufficient for heavy use of machine learning models.How many CPU cores for data science? ›
An easy recommendation is for 32 cores with either of the Intel or AMD platforms mentioned above. The 64-core TR Pro may be ideal if you have highly data parallel tasks with a significant amount of time spent in computation, but scaling may not be as efficient as with the 32-core if memory access is a limiting factor.What laptop specs do I need for Python? ›
Any laptop for Python Programming should have at least 8 GB of RAM. But I recommend getting at least 16 GB of RAM if you can afford it. Because, the bigger the RAM, the faster the operations. But if you think a 16 GB RAM laptop is a bit costly for you, you can go with 8 GB RAM, but don't go below 8 GB.Do you need a powerful computer for machine learning? ›
A powerful CPU is crucial for running complex ML algorithms and data analysis. The GPU is important for running deep learning algorithms, and the more RAM your laptop has, the better it can handle large data sets and complex ML models.
How many CPU cores do I need for machine learning? ›
Do more CPU cores make machine learning & AI faster? The number of cores chosen will depend on the expected load for non-GPU tasks. As a rule of thumb, at least 4 cores for each GPU accelerator is recommended. However, if your workload has a significant CPU compute component then 32 or even 64 cores could be ideal.What is the minimum specs for machine learning? ›
A minimum of 8 GB of GPU memory is recommended for optimal performance, particularly when training deep learning models. NVIDIA GPU driver version: Windows 461.33 or higher, Linux 460.32. 03 or higher. A CPU with the Advanced Vector Extensions (AVX) instruction set.How much RAM do I need for computer science? ›
Generally, we recommend 8GB of RAM for casual computer usage and internet browsing, 16GB for spreadsheets and other office programs, and at least 32GB for gamers and multimedia creators. How you use your computer influences how much RAM you need, so use this as a guideline.Is 32GB RAM overkill computer science? ›
32GB of RAM is considered high and is generally overkill for most users. For most everyday use and basic tasks such as web browsing, email, and basic office work, 8GB of RAM is more than enough. Even for gaming or video editing, 16GB is typically sufficient.How overkill is 128gb RAM? ›
Unless you're editing 8K resolution videos or planning to work with multiple RAM-demanding programs simultaneously, 128 GB is overkill for most users as well. Those who run workloads that demand upwards of 128 GB will probably already know how much RAM they need.Is 64gb of RAM overkill for programming? ›
It's very generous, but not overkill.Is 1tb SSD enough for data science? ›
Requirement 9: Storage Size
If you consider buying a laptop with SSD, you may not be able to afford money to buy a 1 TB SSD because it is much expensive. The ideal size is 512 GB. Do not go below this.
At a minimum, you want to have at least as much memory in the system as there is with the largest GPU, otherwise, a potential bottleneck arises. This means that if your GPU has 32 GB then the minimum RAM you should have is 32 GB.Do we need graphics card for data science? ›
Thanks to their thousands of cores, GPUs handle machine learning tasks better than CPUs. It takes a lot of computing power to train neural networks, so a decent graphics card is needed. As you progress, you'll need a graphics card, but you can still learn everything about machine learning to use a low-end laptop.How much virtual memory set for 16GB RAM? ›
The initial size would be 1.5 x 4,096 = 6,144 MB and the maximum size would be 3 x 6,144 = 18,432 MB.
Do I need 16GB or 32GB RAM for machine learning? ›
As a rough guideline, for small to medium-sized datasets and simple models, 8-16GB of RAM should be sufficient. For larger datasets and more complex models, 32GB or more of RAM may be required.How much virtual memory is recommended for 16GB? ›
For PCs with 16 GB RAM, a paging file of 24 GB makes little sense. The more physical memory is installed, the smaller the paging file can be. For example we recommend a paging file of 2.5 GB for PCs with 16 GB RAM and 5 GB for PCs with 32 GB RAM.What processor is best for data science? ›
- 1.) AMD Ryzen 5 4500.
- 2.) Apple M1.
- 4.) AMD Ryzen 5 2600.
- 5.) AMD Ryzen 7 5700G.
- 6.) Intel Core i3-12100F.
- 7.) AMD Ryzen 5 2600X.
- 8.) Intel Core i5-10600K.
- Do I Need To Spend Thousands On A Processor For Machine Learning?
CPU and RAM
I recommend Intel i5 and i7 processors, especially 8th, 9th or 10th generation. I9 is rarely found in laptops; it's just too expensive. The weaker i3 is not worth considering, especially since it is not much cheaper. Alternatives are the CPUs from AMD (Ryzen 5 and 7).
Python Will Not Use All CPUs By Default
Running a regular Python program will not use all CPU cores by default. Instead, it will run on one CPU core until completion. This is a problem as modern systems have a CPU with 2, 4, 8, or more cores.
4 GB RAM (8 GB preferred) 15 GB available hard disk space. Internet connection.Does Python use a lot of RAM? ›
Those numbers can easily fit in a 64-bit integer, so one would hope Python would store those million integers in no more than ~8MB: a million 8-byte objects. In fact, Python uses more like 35MB of RAM to store these numbers. Why? Because Python integers are objects, and objects have a lot of memory overhead.Which laptop is best for programming and coding? ›
- Microsoft Surface Pro 7.
- ASUS F512DA-EB51 Vivobook 15.
- Lenovo ThinkPad E595.
- ASUS ZenBook 13 Ultra-Slim Laptop.
- Lenovo Ideapad L340 Gaming Laptop.
- HP 17.3” HD+ Flagship.
- Dell XPS 17 9700 Laptops.
- Razer Blade Stealth 13 Ultrabook Laptop.
When dealing with machine learning, and especially when dealing with deep learning and neural networks, it is preferable to use a graphics card to handle the processing, rather than the CPU. Even a very basic GPU is going to outperform a CPU when it comes to neural networks.Do I need i7 for machine learning? ›
Although a minimum of 8GB RAM can do the job, 16GB RAM and above is recommended for most deep learning tasks. When it comes to CPU, a minimum of 7th generation (Intel Core i7 processor) is recommended. However, getting Intel Core i5 with Turbo Boosts can do the trick.
How many cores should a programming laptop have? ›
Conclusion. When buying a new computer, whether a desktop PC or laptop, it's important to know the number of cores in the processor. Most users are well served with 2 or 4 cores, but video editors, engineers, data analysts, and others in similar fields will want at least 6 cores.Is RAM speed important for machine learning? ›
RAM Size. RAM size does not affect deep learning performance. However, it might hinder you from executing your GPU code comfortably (without swapping to disk). You should have enough RAM to comfortable work with your GPU.Which laptop is best for artificial intelligence? ›
- Apple MacBook Pro.
- Asus ZenBook 14.
- Acer Nitro 5.
- Acer Predator Helios 300.
- Dell G5 15 Gaming Laptop.
Although there is some debate over exactly how important coding skills are for a Machine Learning Engineer, it is generally agreed that you would need to develop at least basic programming skills in order to most effectively leverage leverage, create, and implement machine learning models and machine learning ...Is 8GB or 16GB better for computer science? ›
If your computer science emphasis is big data analytics, then having 16 GB RAM would help the speed of processing data since there would be 8 GB more memory for the CPU to use. But that is not essential – you can still use 8 GB RAM to process data.Is 8GB RAM and 256gb SSD enough? ›
An SSD is non-volatile and permanently saves data, whereas RAM is a compressed sort of memory. This implies that the SSD saves data even while it is off, whereas the RAM needs to be refreshed continuously. A256GB SSD and 8GB of RAM is enough for a computer.Do I need a powerful laptop for programming? ›
While you can technically program on any laptop, you'll want to choose one that is powerful enough to handle the demands of coding. A laptop with a fast processor, plenty of RAM, and a solid-state drive will help you work more quickly and efficiently.Do you need a good laptop for machine learning? ›
Key Considerations for Choosing a Laptop for ML and Data Science. Processor: A powerful CPU is crucial for running complex ML algorithms and data analysis. Look for a laptop with an Intel Core i7 or i9 processor or an AMD Ryzen 7 or 9 processor.Which is best data science and machine learning? ›
Data analytics is a better career choice for people who want to start their career in analytics. Data science is a better career choice for those who want to create advanced machine learning models and algorithms.What kind of computer do I need for machine learning? ›
What CPU is best for machine learning & AI? The two recommended CPU platforms are Intel Xeon W and AMD Threadripper Pro. This is because both of these offer excellent reliability, can supply the needed PCI-Express lanes for multiple video cards (GPUs), and offer excellent memory performance in CPU space.
Should I buy a laptop or desktop for data science? ›
It doesn't really matter what computer you choose for doing data science. Any fairly decent laptop will do the job.How much RAM is required for machine learning? ›
As a rough guideline, for small to medium-sized datasets and simple models, 8-16GB of RAM should be sufficient. For larger datasets and more complex models, 32GB or more of RAM may be required.What is the minimum RAM for machine learning? ›
Understanding machine learning memory requirements is a critical part of the building process. Sometimes, though, it is easy to overlook. The average memory requirement is 16GB of RAM, but some applications require more memory.Do I need a high end computer for machine learning? ›
The short answer is yes, deep learning does require high CPU. Deep learning algorithms are computationally intensive and require a lot of processing power. High-end CPUs are often used to process the data, as they are capable of handling large amounts of data quickly and efficiently.Which is harder data science or machine learning? ›
The consensus is that data science is in fact easier than machine learning. Data science involves more statistics, while machine learning involves more computer science in addition to statistics.Who gets paid more data scientist or machine learning engineer? ›
The average salary of a Machine Learning Engineer is more than that of a Data Scientist. In the United States, it is around US$125,000; in India, it is ₹875,000.Should I learn data science or machine learning first? ›
1 Answer. Data science is not just a single entity. For a data scientist, one needs to have knowledge of Machine Learning along with other skills like programming, stats, and the ability to handle huge datasets. Data Science uses machine learning in modeling for predicting and forecasting the future from the data.How many CPU cores for machine learning? ›
Deep learning requires more number of core not powerful cores. And once you manually configured the Tensorflow for GPU, then CPU cores and not used for training. So you can go for 4 CPU cores if you have a tight budget but I will prefer to go for i7 with 6 cores for a long use, as long as the GPU are from Nvidia.Is 256gb SSD enough for data science? ›
But I personally recommend SSD. If you are going with HDD, I would recommend 1 TB of storage space and if you are going with SSD, I would recommend at least 256 GB of storage space. Recommended Requirement- 512 GB SSD or more.Is i5 processor good for data science? ›
The best CPUs for data science are AMD and Intel core processors. A data science laptop should not be less than an Intel Core i5 7th generation. You're going to be running lots of applications simultaneously so you need a laptop with excellent performance.