Python Machine Learning Libraries That You Should Master

When it comes to machine learning Python is the best programming language. It offers everything that a developer needs to build either a small to large machine learning algorithms. Python is a high-level programming language with a simple syntax and shorter development time.

As per a report by builtwith.com, 45% of technology companies prefer to use Python for AI and machine learning implementation. The reason behind this huge number is not only the power and simplicity of Python but also the libraries that this language offers.

Python has a huge collection of libraries. Most machine learning algorithms are complex and include lots of maths, so writing them from yourself is a headache giving task. And that where Python libraries come and reduce the effort to half for developers.


They provide all necessary modules which are simple and short, you don’t need to write long lines of codes with Python. Python libraries also pretty powerful and it also teaches many different things that you might don’t know while using them.

There are many options available and you can choose one of the hundreds of libraries based on your use-case, skill, and need for customization.

In this post, we will discuss the five powerful libraries in Python to learn in 2019, which can be used by developers to implement machine learning in their existing applications, note that don’t judge below libraries on there order in series, the order is random here.

Scikit-Learn

Scikit-Learn machine learning techgrabyte

Scikit-Learn is one of the most widely used and powerful machine learning library. It provides ease in use and it is simple yet efficient tools for developers. Scikit-Learn  is specially designed and developed to work with large and complex data set.

It offers various different types of algorithms for the implementing standard machine learning and data mining tasks like reducing dimensionality, classification, regression, clustering, and model selection. Task likes transforming data, feature selection, and ensemble methods is a game of few lines of code.

Scikit-Learn is built on top of the popular NumPy, SciPy, and matplotlib libraries. Therefore Scikit-Learn has a similar pattern that you always see in this three libraries.

Scikit-learn is focused on modeling data but not on summarizing, manipulating, and loading data. Some of the most popular model groups provided by Scikit-Learn include supervised models, manifold learning, parameter tuning, feature selection, dimensionality reduction, clustering, and cross-validation.

If we compare to other Python Libraries then Scikit-learn is a bit lower level and tends to act as the foundation for many other ML implementations and for beginners it is a more-than-sufficient tool to work with, until you start implementing more complex algorithms.

TensorFlow

TensorFlow machine learning techgrabyte

TensorFlow is one of the most talk Python libraries Python and the reason behind this is that it’s a Google product. TensorFlow was developed by Google in collaboration with Brain Team.

TensorFlow is an open source high-level neural network library that helps you to program your network architectures while avoiding the low-level details. It is a computational framework used for the purpose of expressing algorithms that involve numerous Tensor operations.

As neural networks are expressed in the form of computational graphs, their implementation is done using TensorFlow in the form of a series of operations on Tensors.

Tensors are nothing but the N-dimensional matrices that represent our data. It focuses is more on allowing you to express your computation as a data flow graph, which is much more suited to solving complex problems.




TensorFlow offers utilities for effective data pipelining, and consist of built-in modules for serialization, visualization, and inspection of modules. TensorFlow uses a system of multi-layered nodes that allows you to quickly set up, train, and deploy artificial neural networks with large datasets.

The one thing that I personally like most about TensorFlow is its flexible architecture. TensorFlow allows the developers to deploy it to one or more CPUs or GPUs in a desktop, server, or mobile device all with the same API.

Another thing I like about TensorFlow that we hardly find in other Python libraries is that when you write a program in Python, you can compile and run it on either your CPU or GPU. So you don’t have to write at the C++ or CUDA level to run on GPUs.

Recently, the Tensorflow team started incorporating support for Keras. To understand how to accomplish a specific task in TensorFlow.

Keras

Keras machine learning techgrabyte

Though Keras is mostly used for deep learning, it is also a very useful library for machine learning. Keras is a high-level neural networks API, written in Python. Keras is easy and best library for beginners. It provides an easier mechanism for building neural networks. It also provides some of the best utilities for compiling models, processing datasets, visualization of graphs, and much more.

Keras is capable of capable of running on top of TensorFlowCNTK, or Theano. It has support for convolutional and recurrent neural networks. It supports other common utility layers like a dropout, batch normalization, and pooling.

Keras contains numerous implementations of commonly used neural network building blocks such as layers, objectives, activation functions, optimizers, and a host of tools to make working with image and text data easier.

It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

LightGBM

Lightgbm machine learning techgrabyte

 

LightGBM consists libraries are LightGBM, XGBoost, and CatBoost. All these libraries are competitors that help in solving a common problem and can be utilized in almost the similar manner.

These libraries help developers in building new algorithms by using redefined elementary models and namely decision trees. Therefore, there are special libraries which are designed for fast and efficient implementation of this method.

These library provides provide highly scalable, optimized, and fast implementations of gradient boosting, which makes it popular among machine learning developers. Because most of the machine learning full stack developers won machine learning competitions by using these algorithms.

Theano

Theano machine learning techgrabyte

Theano is a computational framework machine learning library in Python. It allows developers to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.

Theano works similar to Tensorflow, but it not as efficient as Tensorflow. Because of its inability to fit into production environments. Moreover, Theano can also be used on a distributed or parallel environments just similar to Tensorflow.

What sets Theano apart is that it takes advantage of the computer’s GPU. This allows it to make data-intensive calculations up to 100 times faster than when running on the CPU alone. Theano’s speed makes it especially valuable for deep learning and other computationally complex tasks.

Theano has a tight integration with NumPy. Theano is fast and has stability optimizations. It also offers extensive unit-testing and Self-verification.

Bonus:

NumPy

Numpy machine learning techgrabyte

NumPy is one the most old, powerful and evolved library. NumPy is a general-purpose array-processing package consist of multidimensional array objects and a collection of routines for processing those arrays. The NumPy stands for Numerical Python.

Using NumPy, mathematical and logical operations on arrays can be performed. Tensorflow and other libraries use Numpy internally for performing multiple operations on Tensors.

Array interface is the best and the most important feature of Numpy. This interface can be utilized for expressing images, sound waves, and other binary raw streams as an array of real numbers in N-dimensional.

NumPy targets the C Python reference implementation of Python, which is a non-optimizing bytecode interpreter. Mathematical algorithms are written for this version of Python often run much slower than compiled equivalents.

NumPy addresses the slowness problem partly by providing multidimensional arrays and functions and operators that operate efficiently on arrays, requiring rewriting some code, mostly inner loops using NumPy.

NumPy is often used along with packages like SciPy (Scientific Python) and Mat−plotlib (plotting library). This combination is widely used as a replacement for MatLab, a popular platform for technical computing. However, Python alternative to MatLab is now seen as a more modern and complete programming language.

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