The best machine learning libraries for beginners
The Machine Learning environment has grown a lot over the last decade. The AI community is so powerful, accessible, and helpful that there is a code, library, or blog for almost anything in AI. If you're going to launch your path in this magical world, now is the time to get started. In this article on Machine Learning Libraries, we will address a detailed list of libraries to perform much of the Machine Learning activities.
Machine Learning Libraries For Mathematical Computation
Numpy or numerical Python is potentially one of the most popular Python packages for Machine Learning. Scientific computing uses a ton of matrix operations. And these operations can be very computationally intensive. Naive implementation will quickly lead to inefficient use of memory.
Numpy arrays are a special class of arrays that execute these operations in milliseconds. These arrays are in C's programming language. A single matrix would have millions of numbers in activities like Natural Language Processing, where you have multiple words and hundreds of thousands of sentences. You must learn how to proceed with this library.
Machine Learning Libraries For Table Data
In basic words, Pandas is a Python counterpart of Microsoft Excel. Whenever you have tabular info, consider using Pandas to manage it. The nice thing about Pandas is that doing business is just a matter of a few lines of code. If you want to do something complicated, and you find yourself worrying about a lot of coding, there's a high possibility that there's a Pandas order to satisfy your wish in a line or two.
Data Preprocessing & Modeling Machine Learning Libraries
Scikit Learn is currently the most popular library for Machine Learning. It provides almost every common model – Lasso-Ridge, Linear Regression, Logistics Regression, SVMs, Decision Trees, and much more. Not only that, but it also offers a wide variety of resources for pre-processing data, vectoring text using TF-IDF, BOW or hashing vectorization, and many more.
It's received immense support from the group. The only downside is that it does not support well-distributed computing for large-scale development environment applications. If you want to develop your career as a Data Scientist or Machine Learning Engineer, this library is a must!
Machine Learning Libraries Modeling Time Series
Statsmodels is another library for the application of mathematical learning algorithms. However, it is more popular with its module that helps incorporate time series models. You can conveniently decompose a time series into its pattern component, seasonal component, and a residual component.
Machine Learning Libraries For Reading Text
Regular Expressions or Regex
Regular expressions or regex are perhaps the simplest and most useful text processing library. Helps to locate text according to specified string patterns in the text. For example, if you want to overwrite all 'can't's and 'don't's in your text that you can't or don't have, regex can do it in a jiffy.
If you want to find phone numbers in your text, you just need to identify the pattern and the standard expressions and return all the phone numbers in your text. Not only can you find trends, but you can also swap them with a string of your choosing. Making the right matching patterns can be a little challenging at first, but once you get a hang of it, it's fun!
NLTK or Natural Language Toolkit is a robust library for natural language activities. It's a go-to kit for all your text processing needs – from word tokenization to lemmatization, dependency parsing, stemming, and elimination of stopwords, chunking, and more.
Tensorflow is by far the most common library with comprehensive documentation and support from the developer community. It's been developed by Google. For product-based industries, Tensorflow is not a brainer because of the environment it offers for model prototyping for development. Tensorboard, a web-based simulation platform, lets developers simulate model output, model parameters, and gradients.
In a single line, Pytorch is all that Tensorflow isn't. It was developed by Facebook as a Pythonic version of the original Torch Library, a profound learning system written for the programming language of Lua.
It was designed to be as Pythonic as possible, unlike Tensorflow. One of the main ways in which Tensorflow blows out of water is by the execution of Complex Graphs. On the go, you can describe the model components. This is a blessing if you want to do analysis where you need this kind of low-level API versatility.
If you're a newcomer and you want to get your hands dirty, Pytorch is your business. Since it's relatively fresh, it's not as popular as Tensorflow. Yet the group is increasingly shifting its priorities.
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