In this guide, we’ll show the . must know Python libraries for machine learning and data science One of the reasons is Python’s extensive package availability, which makes ML easier. Python and Machine Learning (ML) are the two most in-demand skills for data scientists . And Python is the most popular programming language for machine learning . What are the best Python libraries for Machine Learning? As experienced data scientists, we’ve compiled this list with . references to tutorials and examples You’ll know exactly where to dive deeper into your Python studying for ML. Let’s get started! Before we start If you are brand new to Python, please take our FREE Python crash course for data science . We can’t learn these libraries without strong foundation knowledge of Python. If you are new to Machine Learning, please start from Machine Learning for Beginners: Overview of Algorithm Types . Machine learning has different algorithms (types), which focus on solving different problems. With the basics of ML, you’ll learn better about each Python ML library since it often targets various tasks. Great! Now we are ready to look at the top 6 Python packages/libraries for machine learning and data science. NumPy is the fundamental package for in Python. Most of the other Python libraries for machine learning are built upon NumPy. You can’t do data science using Python without NumPy. NumPy scientific computing Some of NumPy’s functionalities include: and matrices creation. multi-dimensional arrays comprehensive . mathematical functions generators. random number routines. linear algebra discrete Fourier transforms. fast vectorized operations. This is a beginner-friendly tutorial of Python NumPy (arrays) basics for data science. Learn this essential library with examples. Further Reading: Python NumPy Tutorial: Practical Basics for Data Science Pandas is the foundation library for . pandas data analysis and manipulation If you are new to data science, you might wonder what it has to do with ML? Before training ML algorithms/models, the data needs to be processed and cleaned. This process often takes the of time for machine learning practitioners. And pandas makes this process a lot easier for structured datasets. majority pandas offers powerful data structures like . We can use it to: DataFrames between Python and various sources such as CSV files, and SQL databases. import or write data based on descriptive statistics. analyze data with flexibility. group by the datasets. manipulate and transform : This complete tutorial helps you get hands-on experience with data analysis and manipulation. Learn more about the essential functions/methods of Python pandas library for machine learning. Further Reading Learn Python Pandas for Data Science: Quick Tutorial Seaborn is a popular Python library for making . It is based on and also integrated with data structures. Seaborn statistical data visualizations matplotlib pandas Seaborn is especially useful for . Some of the functionalities that seaborn offers: exploring and understanding data options for visualizing univariate and bivariate distributions. options for visualizing numerical and categorical variables. automatic estimation and plotting of linear regression models. ability to build complex visualizations such as multi-plot grids. : Further Reading How to use Python Seaborn for Exploratory Data Analysis Unlock the power of seaborn by exploring an example dataset with Histograms, Heatmaps, Scatter plots, Barplots, etc. Scikit Learn (Sklearn) is one of the most popular Python libraries for machine learning, which supports and . It provides tools for fitting models, preprocessing data, selecting and evaluating models, etc. It’s built on , , and libraries. scikit-learn supervised unsupervised learning NumPy SciPy matplotlib Some of the main features of scikit-learn are: fitting machine learning algorithms and models such as classification, regression, clustering. transforming and preprocessing the data. supporting machine learning pipeline integration. model evaluation, such as cross-validation. : Further Readings 1. scikit-learn User Guide Read the official documentation for instructions on features. 2. Linear Regression in Machine Learning: Practical Python Tutorial Check out the detailed tutorial about Linear Regression, a foundation supervised predictive algorithm. 3. How to Visualize a Decision Tree in 3 Steps with Python A simple example of applying the decision tree algorithm with Scikit-Learn. TensorFlow and Keras is an end-to-end open-source platform for machine learning, first developed and used by Google. It makes the ML model creation easier for both beginners and experts. It’s especially prevalent when building models. Deep learning has been particularly successful with text and image data, which are popular applications of machine learning. TensorFlow deep learning ( is a high-level API on top of TensorFlow for building and training deep learning models. It makes TensorFlow easier to use. Keras used to be a stand-alone framework but is supported in TensorFlow now. It can be used for prototyping, research, and production. Keras tf.keras ) Some of the commonly used TensorFlow’s features are: deep learning (deep neural networks). image processing. text analysis. reinforcement learning. : Further Readings 1. TensorFlow/Keras Tutorial Check the official documentation for the basics. 2. How to do Sentiment Analysis with Deep Learning (LSTM Keras) Learn how to build a deep learning model to classify the Yelp review data in Python step-by-step. 3. 3 Steps to Time Series Forecasting: LSTM with TensorFlow Keras A machine learning time series analysis example with Python. See how to transform the dataset and fit LSTM with the TensorFlow Keras model. 4. Hyperparameter Tuning with Python: Keras Step-by-Step Guide Neural Networks have many hyperparameters, which makes it harder to tune. This is a practical guide to Hyperparameter Tuning with Keras TensorFlow in Python. Implement this machine learning technique to improve your model’s performance. PyTorch is a framework that competes with TensorFlow for developing models. This library has grown and is now more popular than TensorFlow in academia. PyTorch deep learning TensorFlow used to need developers to create and compile a static graph before being able to see the mathematical operations. While PyTorch used dynamic graphs that allowed users to detect errors more quickly. The research communities had entrenched with PyTorch by the time TensorFlow 2.0 was released with similar functionality. Yet, PyTorch is still not widely considered to be production-ready compared to TensorFlow, with the latter being more scalable. Today, both frameworks offer similar things, with PyTorch holding the ground in academia and TensorFlow trending in the industry. Some of the main applications of PyTorch include: computer vision. Natural Language Processing (NLP). reinforcement learning. : Further Reading PyTorch Tutorials Other Python libraries for Machine Learning and Data Science Besides the top 6 must-know libraries, there are also a couple of other popular Python libraries for machine learning. NLTK (Natural Language Toolkit) is a handy package for tasks. Features include: NLTK NLP tokenization. keyword searching. tagging. text classification. named entity detection. over 50 corpora such as Wordnet. : Further Reading How to use NLP in Python: a Practical Step-by-Step Example This is an application for the NLTK package on Indeed Job postings. SciPy is a set of modules for advanced on NumPy data. It is the foundation package for higher-level libraries such as . Some features include: SciPy mathematical operations scikit-learn Fourier transforms. optimization. signal processing. linear algebra. probability and statistics. image processing. : Further Reading SciPy Tutorial Matplotlib is a comprehensive data library in Python. Some features include: Matplotlib visualization - creating interactive plots. - offering flexible customization of the plot. It is a foundation library supporting , which is easier to use. But when we want to customize the plots more, matplotlib becomes necessary. seaborn : Further Reading Matplotlib Tutorial That’s it! We’ve covered all the essential Python libraries for Machine Learning and Data Science. Hope you get a better idea of where to continue your Python learning. Which Python package for ML will you learn first? Leave a comment for any questions you may have or anything else. Before you leave, don’t forget to sign up for the Just into Data newsletter ! Or connect with us on Twitter , Facebook . So you miss any new data science or machine learning articles from us! won’t

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