Needless to say, Python has become the most popular languages in the world of programming and one primary reason for this is its wide spectrum that covers scientific computing to a great extent.
According to researchers and analysts, 2019 will have a great scope of artificial intelligence and machine learning. A new opinion that graces the headlines is that “Machine learning will become more oriented towards Python since Java is longer an option due to its complexity Java.”
As the world of application development is becoming more Pythonic, the demand for Python experts tends to rise. The python programming certification enables beginners to secure a good job pain-free.
From economists to mathematicians and data scientists, professionals from varying fields prefer to learn Python due to its syntax that is in sync with mathematical syntax and, therefore, the language becomes easy to learn.
This post introduces you to the most useful Python tools that can help you complete machine learning and data science operations. No wonder if you are new to AI and ML, read this post and deepen your knowledge in Machine Learning With Python.
Machine Learning Tools
Shogun accounts for support vector machine with the help of its open-source machine learning platform. It is one of the oldest machine learning tools, created in 1999 and is based on C++. It offers you an array of machine learning methods, and the aim behind its creation is to streamline machine learning operations by providing you with algorithms that are as easy as pie to implement.
The tool offers you the Python interface that triggers unified large-scale learning while optimizing high-performance speed. According to its users, however, the tool’s API is complex.
Keras is an interconnected neural networks API that deepens your Python learning by providing a Python library. Since you get an easier way to express neural networks as compared to other libraries, Keras can be an ideal option for beginners in machine learning. Keras is written in Python and is capable of running on top neural network frameworks like TensorFlow, CNTK or Theano.
Though Keras’ official site boasts four principles like friendliness, modularity, extensibility, and sync with Python, speed is where it lacks.
Scikit-Learn makes your data mining and data analysis hyper-efficient. Having its major contribution to machine learning, it has become a good fit for data science as well. Moreover, the tool offers an easy to use API along with grid and random search. Its high speed has brought innovation in toy datasets. Some of its advanced functionalities include dimensionality reduction, model selection, preprocessing, classification, regression, and clustering.
Pattern streamlines machine learning, network analysis, canvas visualization, data mining, and natural language processing. It is a web mining module and encompasses attestations as the tool has undergone 350-unit tests. It comes for free.
Theano comes first when it comes to advanced Python deep learning libraries and is named after Pythagoras. The list of its features is quite long that begins from efficient symbolic differentiation, tight integration with NumPy to transparent use of GPU, and agility in speed.
The tool has the capability of defining, optimizing, and evaluating mathematical expressions. Today, a ton of other libraries have been built over Theano. Apart from so many features, the tool faces mixed views as some experts call it the master of your learning curve; however, on the other hand, some professionals call it due to its inability to work in sync with production environments.
Data science tools
SciPy is wholly Pythonic and is open-source software, specially developed for engineering, mathematics, and science. NumPy, IPython, etc., are some of the libraries that SciPy takes into account to solve your math- and science-oriented programming tasks. You can improve your numeracy skills and publish the results using SciPy that costs you nothing.
Taking advantage of NumPy, Pandas, and Scikit-Learn, Dask offers you seamless analytics. Parallelizing existing code is far easier since its DataFrame is similar to the Pandas library.
It is an advanced compiler based on the LLVM compiler infrastructure to sync machine code with Python syntax. One of the important benefits of working with Numba in data science applications is its high speed, especially when using a code with NumPy arrays. It also works great for machine learning applications as it fastens hardware being developed for machine learning applications.
HPAT stands for High-Performance Analytics Toolkit. The tool has a compiler-based framework for big data analytics. It is capable of optimizing specific functions with the @jit.decorator and scales analytics/machine learning codes in Python to the bare-metal cluster.
This is all about the post.
We are in an era where machine learning tends to trigger decision making with the help of algorithms that feed data and lead to predictions. Machine learning algorithms has helped organizations to make sense out of their databases. With the advent of Python, machine learning has become no more a field of technical experts. I hope this post helps you understand what makes Python and machine learning co-exist. The tools that I have mentioned above can certainly help you scale machine learning in Python.