'10 Data Science and Machine Learning Libraries for Python' by javinpaul python datascience
Today, I am going to introduce you to some of those awesome libraries like TensorFlow, NumPy, Pandas, SciPy, Scikit-learn, Seaborn, Keras, and Matplotlib. I know there are many more libraries but with my limited experience and exposure, I have heard of these main libraries so far.
I'll definitely add new libraries to this list as and when I come across but till then knowing these libraries will help you a lot, particularly if you are also learning Data Science, Artificial Intelligence, and Machine learning using Python. Whether you are a beginner or already know Data Science, learning these libraries can make you more productive and enhance your profile. By the way, if you are a complete beginner, I suggest you start with a hands-on course like10 Best Python libraries for Data Science, Analysis, Visualization, and Machine learning Without any further ado, here is a basic introduction to some of the most popular Python libraries for Data Science and Machine learning. I have tried to keep the explanation short and sweet and pointed it out to the resource to learn more just for the sake of brevity and clarity. As I am also learning Python and Machine learning, will write in detail about each of these libraries in the future because you would need at least one post to explain them in a little bit of detail.This is one of the most popular machine learning library and there is a good chance that you might have already heard about it. You might know thatis from Google and was invented by their Brains team and used in the RankBrain algorithm which powers millions of search questions on Google's search engine. In general, it is a symbolic math library and is also used for machine learning applications such as neural networks. There are many applications of TensorFlow and a lot of stories you can find on the web like how a Japanese farmer used TensorFlow to filter Cucumber., it will not only teach you TensorFlow but also the basics of machine learning and How Neural Networks., but, if you need more choices, you can also take a look at my list of best TensorFlow courses for Machine learning programmers and Data Scientists.One of the main problems with creating machine learning and deep learning-based solutions is that Implementing them can be tedious to create and require you to write many lines of complex code.In a few lines of code, you can create a model that could require hundreds of lines of conventional code.Complete Tensorflow 2 and Keras Deep Learning BootcampIt's a great course by Jerry Kurata, a Solutions Architect at InStep Technologies, who is also an instructor of some of the popular machine learning courses on Pluralsight like3. Scikit-learn This is another popular Python library for machine learning. In fact, Scikit-learn is the primary library for machine learning. It has algorithms and modules for pre-processing, cross-validation, and other such purposes. Some of the algorithms deal with regression, decision trees, ensemble modeling, and non-supervised learning algorithms like clustering.Python for Data Science and Machine Learning BootcampIt is one of the most comprehensive course on Data Science and Machine Learning with Python and along with Scikit-learn also teach you other popular machine learning algorithms like
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