In this article, you'll see top 30 Python libraries for Machine Learning. In this article, you'll see top 30 Python libraries for Machine Learning. Today, Python is one of the most popular programming languages and it has replaced many languages in the industry. There are various reasons for its popularity and one of them is that python has a large collection of libraries. Python is one of the most widely used languages by Data Scientists and Machine Learning experts across the world. Though there is no shortage of alternatives in the form of languages like R, Julia and others, python has steadily and rightfully gained popularity. Similar to the Google Trends shown above(the plot is prepared using matplotlib and pytrends), confidence is visible year over year with python featuring way above its peers in the StackOverflow surveys for 2017 and 2018. These trends/surveys are the consequences of ease of use, shorter learning curve, widespread usage, strong community, large number of libraries covering depth and breadth of a number of research and application areas. The amazing popularity might make one think that python is the gold standard for Machine Learning.
Statsmodels is an open-source statistics-driven module that offers various classes and functions to the many statistical models available for statistical analysis and exploration of data. The module covers a vast number of models ranging from Linear Regression, Discrete Models, Time Series Analysis, Survival Analysis, and many other miscellaneous models.
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.
It's never been easier to get started with machine learning. In addition to structured MOOCs, there is also a huge number of incredible, free resources available around the web. Familiarity and moderate expertise in at least one high-level programming language is useful for beginners in machine learning. Unless you are a Ph.D. researcher working on a purely theoretical proof of some complex algorithm, you are expected to mostly use the existing machine learning algorithms and apply them in solving novel problems. This requires you to put on a programming hat.
Most aspiring data scientists begin to learn Python by taking programming courses meant for developers. They also start solving Python programming riddles on websites like LeetCode with an assumption that they have to get good at programming concepts before starting to analyzing data using Python. This is a huge mistake because data scientists use Python for retrieving, cleaning, visualizing and building models; and not for developing software applications. Therefore, you have to focus most of your time in learning the modules and libraries in Python to perform these tasks. Follow this incremental steps to learn Python for data science.