10 Machine learning frameworks those are popular amongst Data scientists

By arvind |Email | Sep 17, 2018 | 8256 Views

As more and more companies are stretching their hands in the field of data science and AI, the demand for data scientists has seen a massive rise. In the last couple of years Data scientists has taken the spot of the number one job in America, and not only for America Data Scientists, data science jobs are amongst the most coveted careers all around the globe. 

Being declared as the sexiest job of the century, the job even offers a high average salary to people that provides the right skill set; companies need a professional who can parse through data and the company's collection to drive business insights and apply it to new technologies like machine learning an artificial. According to a report conducted recently by Figure Eight 89 percent of data scientists said they love their job which was around 67 percent in the year 2015.

When asked from a group of data scientists an odd 90 perfect of the data science workers said that some part of their work informs Artificial Intelligence or Machine learning projects. Almost 40 percent said that a majority of their work does so. Almost 50 percent of respondents said that the quality and quantity of training data is the biggest challenge in their work.

Data science and machine learning remain relatively young fields, so there is not yet overwhelming consensus on what languages, tools, and frameworks are best. While the machine learning community has largely begun using Python (61%), according to the report, there remains wide variability in the machine learning frameworks in use.
Here are 10 of them

1. Pandas: Pandas is Python Data Analysis Library; a panda is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools.
Primary object types:
Data Frame: rows and columns (like a spreadsheet)
Series: a single column

2. NumPy: NumPy is the fundamental package for scientific computing with Python. It contains among other things
  •  a powerful N-dimensional array object
  •  sophisticated (broadcasting) functions
  •  tools for integrating C/C++ and Fortran code
  •  Useful linear algebra, Fourier transform, and random number capabilities
Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

3. Scikit-learn: Scikit-learn is a free software machine learning library for the Python programming language. It feature various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, l-means and DBSCAN and is designed to interoperate with the python numerical and scientific libraries NumPy and SciPy.

4. Matplotlib: Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits.

5. Tensorflow: TensorFlow├?┬ó?├?┬ó is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google's AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.

6. Keras: Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. 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.
Use Keras if you need a deep learning library that:
  •  Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).
  • Supports convolutional networks and recurrent networks, as well as combinations of the two.
  •  Runs seamlessly on CPU and GPU.

7. Seaborn: Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Seaborn aims to make visualization a central part of exploring and understanding data. Its dataset-oriented plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots.

8. Pytorch and torch: Torch is a Tensor library like Numpy, unlike Numpy it has strong GPU support. Lua is a wrapper for Torch, and for that you will need LuaRocks package manager. PyTorch has no need of the LuaRocks package manager, no need to write code in Lua. And because we are using Python, we can develop Deep Learning models with utmost flexibility. We can also exploit major Python packages likes scipy, numpy, matplotlib and Cython with pytorch's own autograd.

9. AWS Deep Learning AMI: The AWS Deep Learning AMIs provide machine learning practitioners and researchers with the infrastructure and tools to accelerate deep learning in the cloud, at any scale. You can quickly launch Amazon EC2 instances pre-installed with popular deep learning frameworks such as Apache MXNet and Gluon, TensorFlow, Microsoft Cognitive Toolkit, Caffe, Caffe2, Theano, Torch, PyTorch, Chainer, and Keras to train sophisticated, custom AI models, experiment with new algorithms, or to learn new skills and techniques. Whether you need Amazon EC2 GPU or CPU instances, there is no additional charge for the Deep Learning AMIs - you only pay for the AWS resources needed to store and run your applications.

10. Google Cloud ML Engine: Cloud Machine Learning Engine brings the power and flexibility of TensorFlow to the cloud. You can use Cloud ML Engine to train your machine learning models and get predictions using the managed resources of Google Cloud Platform.

Source: HOB