Top 10 Python Libraries For Machine Learning And Data Science
Python is increasingly becoming the most sought after programming language thanks to a number of factors such as its simplicity, the ease of project development, deployment, and maintenance, portability, and a large collection of libraries among other factors. Python has also become the programming language of choice for Machine Learning algorithms and Data Science. All you need to do is find the best Python libraries for ML and data science for the function you wish to execute.
Why Python is the best programming language for Machine Learning and Data Science.
Python comes with some of the best features and flexibilities that not only increase developers’ productivity. Additionally, its extensive libraries help ease the workload. Here are some of the features that make Python the best programming language for Machine Learning algorithms and Data Science:
- A free and open-source nature- This makes Python community friendly while guaranteeing long-term improvements.
- Extensive libraries – This ensures there is solution for just about every problem you will encounter
- Seamless implementation and integration – this guarantees access for developers with the varying skill level to embrace it
- Reduced coding and debug time – this translates into increased productivity
- Python is great for soft computing and Natural Language processing
- Python works seamlessly with C++ and C code modules
Related Post: Learn Python With These Amazing Online Courses
Top 10 Python libraries for Machine Learning and Data Science in 2021
MatPlotLib
MatPlotLib is without doubt one of the best Python library for data science. With it, you can visualize data and create amazing stories. Another SciPy Stack, you can seamlessly use Matplotlib to visualize your data using 2D figures.
So how do you use Matplotlib for Machine Learning and Data Science?
This, as the name alludes, is the plotting library for Python that provides an object-oriented API for embedding plots into applications. It closely resembles MATLAB embedded in Python.
With Matplotlib, you can create bar plots, histograms, scatter plots, contour plots, quiver plots, spectrograms, stem plots, and convert area plots to pie plots. Basically, you can use Matplotlib to depict a wide range of visualizations. With little effort, you can use Matplotlib to facilitate labels, legends, grids, and other formatting capabilities. Basically, Matplotlib is a powerful data visualization tool!
Best online MatPlotLib courses
Pandas
Pandas (Python Data Analysis Library) is an open-source Python library that provides high-performance yet easy-to-use data analysis and data structures tools for the labeled data in Python programming language. It is an excellent tool for data wrangling or data munging. Pandas is designed for seamless data reading, data manipulation, aggregation, and visualization.
So how do you use Pandas for Machine Learning and Science?
Pandas use data in TSV or CSV file or a SQL database to create a data frame, a Python project with rows and columns. This data frame is quite similar to tables in statistical software like Excel or SPSS. Here are some of the data science functions you can perform with Pandas:
- Data indexing, renaming, manipulating, and sorting as well as merging data frames
- Updating, adding, and deleting columns from data frames
- Handling missing data or NANs and input of missing files
- Plotting data with histograms or box plots
Best online Pandas courses
TensorFlow
This is an Artificial Intelligence library that developers use for creating large scale neural networks with multiple layers of data flow graphs. It can also be used for building Deep Learning models, pushing state-of-the-art in AI/ML and allows for seamless deployment of Machine Learning-powered applications.
So when do you use TensorFlow in Machine Learning and Data Science?
TensorFlow is one of the best Python libraries for machine learning and data science when it comes to data classification, perception, interpretation, discovering, predicting, and creation. Here is some of the Machine Learning application of TensorFlow:
- Voice and sound recognition – Security, Automotive, UX/UI, and IoT
- Text-based apps – Google Translate, Threat Detection, Gmail smart reply
- Sentiment Analysis –mostly for CX or CRM
- Face Recognition – Photo tagging, Facebook Deep Face, Smart Unlock
- Time Series – Google, Amazon, and Netflix recommendations
- Video detection – Airports, motion detection real-time threat detection in gaming, and security
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NumPy
NumPy is one of the most prominent Python libraries for Machine Learning algorithms and Data Science. This general-purpose array-processing library provides high-performance multidimensional array objects and tools. Its core object is the homogenous multidimensional array.
So when do you use NumPy for Machine Learning and Data Science?
NumPy is used for processing arrays that store values of the same datatype. It is used to execute mathematical operations on arrays and their vectorization. This, in turn, enhances performance and speeds up the execution time. Here are some of the Machine Learning and Data Science functions that you can perform with NumPy:
- Basic array operations like addition, multiplication, slicing, flattening, reshaping, and array indexing
- Advanced array operations like stacking arrays, splitting sections, and broadcasting arrays
- Working with date, time, or linear algebra
- Basic slicing and advanced indexing in NumPy Python
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SciPy
The SciPy library is one of the most important packages that come with the SciPy stack. (It is important to mention that SciPy stack is different from SciPy the library). ScyPy builds on the NumPy array object and is part of the stack that comes with Pandas, Matplotlib, and SymPy as well as several other tools.
So when do you use SciPy for Machine Learning and Data Science?
SciPy uses arrays as its core data structure. SciPy library comes with modules for effective mathematical functions like interpolation, linear algebra, integration, optimization, and statistics. SciPy library is primarily built upon NumPy.
Best online SciPy courses
Spark MLlib
Developed by Apache, Spark MLlib is a Machine Learning library that enables seamless scaling of computations. It is user-friendly, quick and easy to set up and enables a hassle-free integration with other tools. Spark MLlib is one of the best Python libraries for developing Machine Learning applications and algorithms.
Best online Spark MLlib courses
Scikit-learn
Scikit-learn is a robust Machine Learning library for Python that was first introduced to the world as a Google Summer of Code project. It comes with ML algorithms like random forests, SVMs, k-means clustering, mean shift, spectral clustering and cross-validation among other algorithms. Scikit Learn is part of the SciPy Stack and supports other related functions like NumPy and SciPy.
So when do you use Scikit-learn for Machine Learning and Data Science?
Scikit-Learn offer a range of supervised and unsupervised learning algorithms in Python. Scikit-Learn is the go-to Python library for learning supervised models like Naïve Bayes to grouping unlabeled data like K-means clustering. Basically, Sikit is used for data modeling. Here are some of the data science functions that you can execute with Scikit-learn:
- Classification – like image recognition and Spam detection.
- Clustering – stock price and drug response monitoring
- Regression – grouping experimental outcomes and customer segmentation
- Dimensionality reduction – increased efficiency and data visualization
- Model selection – using parameter tuning to improve accuracy
- Pre-processing – input data preparation for processing with ML algorithms
Best online Scikit-lean courses
Keras
This is a TensorFlow high-level API for training and building Deep Neural Network code. This open-source neural network library for Python is used for stastistical modeling, working with text and images, as well as creating simplified coding for deep learning. Keras is built for Python and this makes it user-friendlier and composable.
So when do you use Keras for Machine Learning and Data Science?
- When determining accuracy percentage
- When computing loss function
- When creating custom function layers
- For built-in data and image processing
- When writing functions with repeating code blocks: 20,50, 100 layers deep
Statsmodels
If you have used R before, you might be tempted to believe conducting statistical tests and statistical data exploration is way easy. Well, that’s true until you come across Statsmodels.
So when do you use Statsmodels for Machine Learning and Data Science?
Statsmodels is a powerful Python library that provides seamless computations for descriptive statistics and estimation and inference for statistical models. Here are some of the Machine Learning and Data Science functions that you can execute with Statsmodels:
- Correlation
- Linear regression
- Ordinary Least Squares (OLS)
- Generalized linear models and Bayesian model
- Survival analysis
- Uni-variate and bi-variate analysis and Hypothesis testing
Plotly
Plotly is an excellent graph plotting library for Python. You can use it to import, copy, paste, and stream the data that is meant for analysis or visualization.
So when do you use Plotly for ML and Data Science?
The Plotly graph library is used for data visualization in the following way:
- Basic charts – lines, pies, dots, bubbles, sunburst, Treemap, Filled area charts, Sankey, and Gantt.
- Statistical and seaborn styles – Histograms, Error, Facet and Trellis Plots, Box, Trend lines, and Violin plots.
- Scientific charts – log, contours, Quiver, Radar, ternary, polar plots, and heat maps Windrose.
- Financial charts
- Transformers
- Financial charts
- Jupyter widgets interaction
Conclusion
Python is a powerful tool that not only serves as a general-purpose programming language but also takes care of specific projects or workflow. With tons of libraries and packages that expand its capabilities, Python is a great programming language of choice for anyone looking to develop programs and algorithms for Machine Learning algorithms and Data Science. All you need to do is figure out the machine learning or data science function you want to execute and find the best Python package for data science for it.