Experts have made it quite clear that 2018 will be a bright year for artificial intelligence and machine learning. Some of them have also expressed their opinion that "Machine learning tends to have a Python flavor because it's more user-friendly than Java". When it comes to data science, Python's syntax is the closest to the mathematical syntax and, therefore, is the language that is most easily understood and learned by professions like mathematicians or economists. Here I will present my top 10 list of the most useful Python tools for both machine learning and data science applications. If you feel like deepening your knowledge in either field and you don't know where to start, this is the best place for you!
Welcome to the world of machine learning with scikit-learn. Machine learning can be overwhelming at times, and this is partly due to a large number of tools that are available on the market. This post will simplify this process of tool selection down to one -- scikit-learn. In this series, you will learn how to construct an end-to-end machine learning pipeline using some of the most popular algorithms that are widely used in industry and professional competitions, such as Kaggle. Now, let's begin this fun journey into the world of machine learning with scikit-learn!
One of the big surprises of the past few years has been the spectacular rise in the use of Python* in high-performance computing applications. With the latest releases of Intel Distribution for Python, included in Intel Parallel Studio XE 2019, the numerical and scientific computing capabilities of high-performance Python now extends to machine learning and data analytics. Because it's easy to learn and comes with vast open source packages and libraries tailored for just about every computation domain, especially data analytics and machine learning. Industrial strength data analytics involves some very serious math. A single application might employ many complex solutions requiring a significant effort to develop.
Python programming language has huge libraries and frameworks to facilitate coding and save development time. It is famed for its simplicity, easily readable code, and brief syntax and logic. Since machine learning deals with extremely complex algorithms and multi-stage workflows, here python's brief and easy logics play important role in saving developer's time. On the other hand, when it comes to Data Science, Python has packages that are rooted specifically for data science job. SciPy, NumPy, and pandas facilitate data analysis and can be easily integrated with web apps.
In this article, let's check about some of the best frameworks and libraries for Machine Learning. This list is created by me based on a variety of parameters, some would surely not accept it but again it is according to me and would vary from person to person. If you are a beginner, check out our articles on "Machine learning crash course" and "Machine learning specialization course". Each of these Frameworks is different from each other and takes much time to learn, during the time of making this list we took care of features other than the basic ones, User base and community & support was one of the most important parameters. Some frameworks are more mathematically oriented, and hence geared more towards statistical than neural networks.