Best Python Packages for Machine Learning
The most popular python packages for machine learning and how data scientists can work with them more efficiently
Artificial Intelligence (AI) and Machine Learning (ML) are everywhere these days. In fact, it’s surprising how much of it we already take for granted, such as recommendations about what we watch and buy (Netflix and Amazon), personal assistants (Siri and Alexa), fraud alerts (Visa and MasterCard), route optimization (Google maps), and spam filtering.
First things first, why use Python for Machine Learning?
Python is widely considered the tool of choice for data science projects in general and ML initiatives in particular.
If you have already have an understanding of Python and it’s machine learning capabilities, then you may skip this part. But if you are a data scientist or analyst who is just starting out, then here’s why Python is the best language to start with.
- When compared to C, C++ and Java, it is much easier to learn.
- It is intuitive and minimalistic.
- In comparison to R, there is less chance of making mistakes with Python, even if you are a code newbie.
- The simple syntax makes it easy to write and debug your code.
Many data scientists get introduced to machine learning with the help of Python.
Which Python Packages are most useful for Machine Learning?
Machine learning projects start with the data. Typically, 80% of the work is cleaning up the data, feeding it to your algorithms and training the machine learning component. If you’ve done a good job normalizing the data, you’ll get convergence and a model you can use.
Python features the bulk of all open source ML and data engineering tools.
When you start working with Python, your first interaction will be with Pandas that helps prep data. When you move on to algorithm and model building, NumPy, SciPy and Scikit-learn help with the fundamentals while TensorFlow, Keras and Natural Language ToolKit open up the Machine Learning aspect. The matplotlib library and scimitar-learn library help with data visualization for machine learning.
- TensorFlow (deep learning with neural networks)*
- scikit-learn (machine learning algorithms)
- theano (deep learning with neural networks)
- keras (high-level neural networks API)
- pandas (data analysis)
- NumPy (multidimensional arrays)
- SciPy (algorithms to use with numpy)
- HDF5 (store & manipulate data)
- matplotlib (data visualization)
- cryptography (recipes and primitives)
- pyOpenSSL (python interface to OpenSSL)
- passlib and bcrypt (password hashing)
- requests-oauthlib (Oauth support)
Such robust support from a programming language makes Python the preferred choice for many companies when it comes to data science and machine learning. Which in turn leads to more career opportunities for Python Devs who have experience with these packages. You will be able to not only process data but also put it to use in a web application.
Open source community packages are powerful too but…..you can end up wasting days installing and configuring python packages before they are able to start writing any useful algorithms.
That’s where ActivePython steps in.
ActivePython provides all the packages for data science and machine learning, and is also pre-optimized for computational performance to ensure productivity right out of the box.
ActivePython includes open source community packages like Pandas to help with the data pre-processing. Packages like TensorFlow, and Keras, as well as scikit-learn, provide the algorithms, additional libraries, computational power and user-friendly control to develop the learning stages.
A key bottleneck in any machine learning project is the processing of algorithms. ActivePython incorporates Intel’s Math Kernel Library (MKL), which takes advantage of multiple cores and vector registers to accelerate basic linear algebra operations and solvers, Fast Fourier Transforms (FFTs), arithmetic and transcendental operations, and more.
This means YOU get the Machine Learning Packages You Need – No Configuration Required.
Your mathematical routines and model training run FASTER, so you can get your MACHINE LEARNING PROJECT to market faster.
Why is ActiveState Python more useful for Data Scientists?
ActivePython makes it possible to build a single, consistent Python environment across the data science and programming teams. As a result, productizing your machine learning model is less like throwing it over the wall to coders and more like passing the baton.
Python shines as a rapid prototyping environment, providing web frameworks like Django and Flask that allow your developers to incorporate your learning models in web apps, APIs, etc., and then scale them out in production using integrated cloud tools for AWS and Google. While open source Python provides many of the tools and libraries for Machine Learning, high-value staff can end up wasting days on the low-value work of installing and configuring packages.
ActivePython not only comes pre-compiled with the most popular open source packages for machine learning, but is also pre-optimized for compatibility and speed, ensuring data scientists and application development teams can be productive right out of the box.
Having served Fortune 1000 companies for 20 years, we understand optimizing open source software like Python for quicker turnarounds.
‘So what do machine learning, data and python do together? Asking for a friend.’
The introduction of ML is causing a sea change in many industries. Software applications that don’t incorporate some form of ML are likely to be a rarity in the coming decade. The best example we can give to a beginner is its application in healthcare. Alzheimer’s onset can actually occur at a much younger age, but it goes undetected partly because testing for Alzheimer’s involves a spinal tap procedure that’s neither safe nor cheap. Today, an ML-assessed blood test can identify a set of blood proteins which can predict concentrations of amyloid-beta in spinal fluid.
Here are three Machine Learning Python projects you can try right away!
Another example of Machine Learning projects is a chatbot! Food companies like Subway, Dominos and Starbucks are all experimenting with letting people place orders using chatbots. There is a Python-based implementation of a chatbot on Github that you can try “out of the box” with just a few commands to setup your environment!
There you go! You have an easy-to-deploy virtual environment that has all your dependencies resolved for you, as well as everything you need to build your app. ActiveState takes the (sometimes frustrating) environment setup portion out of your hands, allowing you to focus on actual development.
Whether you are a complete beginner venturing into Machine Learning, or an experienced developer working on some fresh machine learning projects, ActivePython can save you time when it comes to finding and readying all the python packages you need to get the job done. It leaves you time to focus on actual data science – detecting anomalies, uncovering root cause, correlating logs, predicting failures, turn ideas into results and collaborate better with your team!