Machine Learning Tools You Need To Know in 2018

Python 

A high-level, interpreted and general-purpose dynamic programming language that focuses on code readability. Python, which was released in 1991, has a syntax that emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages modularity and code reuse.

RStudio 

An integrated development environment (IDE) for the R programming language. Available in open source and and runs on desktop as well. It includes a code editor, debugging, and visualization tools. RStudio was designed as a way to offer enterprise-ready professional products for teams to scale and share work.

 

R

Very important in data science because of its versatility in the field of statistics. Usually used in the field of data science when the task requires special analysis of data for standalone or distributed computing. Also perfect for exploration.

 

Tensorflow

Enables developers to get started with Deep Learning in the cloud. The framework has broad support in the industry and has become a popular choice for Deep Learning research and application development, particularly in areas such as computer vision, natural language understanding, and speech translation.

 

Keras

A minimalist Python library for Deep Learning that can run on top of TensorFlow. It was developed to make implementing Deep Learning models as fast and easy as possible for research and development. Designed to enable fast experimentation with deep neural networks, it's key advantages come from being user-friendly, modular, and extensible.

 

Scikit-learn

A popular open-source Machine Learning toolkit, it contains algorithms for supervised and unsupervised learning, including Gaussian processes and neural networks. It allows you to load and transform datasets, along with model selection and evaluation. Before the release of TensorFlow in 2015, Scikit-learn was the most popular open source Machine Learning framework.

 

Anaconda

A free and open source distribution of the Python and R programming languages for data science and Machine Learning applications (large-scale data processing, predictive analytics, scientific computing). Aims to simplify the process of installing, upgrading, configuring, and removing computer programs (i.e. package management) in an efficient and consistent manner. 

 

Caffe

A Deep Learning framework made with speed and modularity in mind. Caffe is a good choice if you are looking for more of an "off-the-shelf" neural network architecture. It is fairly easy to set up and train without needing to add unfamiliar building blocks to the network. Caffe is especially popular in the computer vision community for those who want to use vision algorithms but are less versed in Deep Learning,

 

Pandas 

An open source data analysis library for providing easy-to-use data structures and data analysis tools for the Python programming language. Provides fast and flexible data structures designed to make working with “relational” or “labeled” data easier. Has the broader goal of becoming a powerful and flexible open-source data analysis/manipulation tool available in any language.  Pandas is widely used in data wrangling.

 

NumPy

A math library for python. It enables effective computation. Enriches the programming language Python with powerful data structures, implementing multi-dimensional arrays and matrices. Enables a large library of high-level mathematical functions to operate.

 

Kafka

Evolved from being just a messaging system to a complete streaming platform. Gained a lot of traction in the industry and has been widely adopted amongst Fortune500 companies (35%). Reasons for its rapid popularity include high throughput ingestion and seamless integration with a lot of external systems. Enables Real-Time Analytics through Kafka Streams.

  

Talend

Runs natively in Hadoop using the Apache ecosystem. First data integration platform built on Spark. Makes it easy to build Big Data integration jobs leveraging Spark, Spark Streaming and Machine Learning. Allows seamless movement between batches, streaming and real-time in the Cloud or with Big Data.

 

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