Python is the most used computing language because of two major reasons,
- It has an amazing community.
- It is highly versatile in both machine learning and AI applications.
Most of the projects are developed on a small set of Python libraries for machine learning that comprehensively manage data loading to deep learning at a massive scale. Python’s handy ecosystem of libraries offers systematic tools for data visualization, data handling, model building, feature engineering, and evaluation. These aspects make Python libraries best-suited for handling ML workflow, speedy and reliable.
Some of the most popular machine learning libraries in Python are NumPy, TensorFlow, Pandas, and others. We will be discussing them in detail soon. Before that, I would quickly go through the most versatile aspects of these libraries.
- Libraries offer optimized implementations of complicated algorithms.
- Streamlines feature engineering and data processing.
- Offers an optimum environment for experimentation and prototyping.
- Can be easily used both in industry and academia.
Let’s jump right into the details of these Python libraries; I have divided them into two distinct categories, one for data science and the other for popular machine learning use cases. Besides the definition, I have also included what these libraries are best used for and how you can install them.
Core ML Libraries for Data Science
1. NumPy
NumPy is the starting point, the math brain behind Python. It is the fundamental computing library that supports multi-dimensional and huge arrays and matrices. It also has an impressive collection of mathematical functions. NumPy is widely used in machine learning for executing vectorized operations, managing numerical data, and efficiently implementing computations related to low-level mathematics.
Used For
- Extensively for transformation and representing numerical features.
- Uses vectorization for quick mathematical operations.
- Serves as multiple machine learning libraries’s computational backbone.
- Manages memory of large datasets efficiently.
You Can Install Using: pip install numpy
2. Pandas
Developed on top of NumPy, the Pandas library is used for distinguished data analysis and manipulation. Think of it as a refined version of Microsoft Excel; it can effectively handle huge dataset processing. With its convenient data structures, Series and DataFrame, engineers use it for data cleaning, feature engineering, aggregations, and joins. It can be fed with structured, time-series, or tabular data.
Used For
- Mainly efficient for data cleaning, transformation, and preparation.
- Refine exploratory data analysis.
- Useful in managing inconsistent, lost, and categorical data.
- Easy integration with visualization libraries and machine learning.
You Can Install Using: pip install pandas
3. SciPy
SciPy is best used in combination with NumPy, when the latter is not enough. It offers substantial scientific tools that show up in actual problems, like signal processing, statistical modeling, and optimization. It is a perfect library for people who want mathematical and scientific functions under one roof.
Used For
- SciPy is popularly used in machine learning and data science for data preprocessing with interpolation, algorithm optimization, and statistical analysis.
- Risk management using statistical analysis in finance.
- Resolving boundary value problems, electrical system signal analysis, and performing simulations in engineering.
You Can Install Using: pip install scipy
Related: Migrating to the Cloud: Top 10 Data Migration Challenges and Solutions
Machine Learning Libraries in Python
4. Matplotlib
One of the most popular ML libraries in Python, Matplotlib, is a complete data visualization library utilized to generate interactive and static plots. Its role is vital in ML to know data distributions, interpret model performances, and identify patterns using graphical representations.
Used For
- Visualization of datasets and model outputs.
- Assists in publication-quality and custom plots.
- Vital for result interpretation.
- Support to detect skewness, trends, and any imbalances.
You Can Install Using: pip install matplotlib
5. Scikit-learn
A machine learning library in Python that offers efficient and easy-to-use tools for traditional machine learning tasks. Scikit-learn provides support for both supervised and unsupervised learning algorithms, as well as utilities for model evaluation, preprocessing, and validation.
Used For
- Popular use cases are regression, classification, and clustering.
- Solves classical machine learning problems.
- Offers evaluation and preprocessing tools.
- Simple and convenient API.
You Can Install Using: pip install -U scikit-learn (adding -U ensures that you download the latest version or update your existing version)
6. TensorFlow
Google developed TensorFlow as an open-source deep learning framework. You can use it to develop, train, and deploy extensive neural networks. It aids in both research and production-level ML systems.
Used For
- Neural networks and deep learning.
- Aids in GPU and distributed training.
- Highly versatile model architecture design.
- Production-ready and expandable.
You Can Install Using: pip install tensorflow
7. Keras
A distinguished neural network API, Keras streamlines the development of deep learning models. It simplifies the entire process of neural network development. This aspect makes Keras very useful for fast prototyping and for beginners.
Used For
- Efficient development of neural networks.
- Very little code required; beginner-friendly.
- Classification and regression support.
- High-speed development.
You Can Install Using: pip install keras
8. PyTorch
An open-source deep learning Python library in data science, PyTorch, is used widely for having dynamic computation graphs, which assist in the modification of models during execution. This property makes this language very flexible and heavily used for experimentation and research.
Used For
- Developing intuitive and dynamic models.
- Assists with custom training logic.
- You can efficiently customize and debug.
- Popular for research-related deep learning.
You Can Install Using: pip3 install torch torchvision torchaudio
9. Seaborn
Developed on Matplotlib, Seaborn is a library for statistical data visualization. It can generate good-looking and informative plots that support relationship understanding between variables at the time of exploratory data analysis.
Used For
- Exploratory data analysis.
- Improves data interpretation.
- Generates refined statistical plots.
- Directly operates with pandas DataFrames.
You Can Install Using: pip install seaborn
10. CatBoost
If you are having trouble with categorical data, CatBoost is the best choice among Python libraries for machine learning. It intelligently manages categories, so you can give most of your time to modeling and less time to encoding.
Used For
- Classification, regression, and ranking.
- Real-world uses in self-driving cars, high-energy physics research, and bot detection.
- Provides tools for model analysis and visualization.
- Cuts down the effort and time spent on parameter tuning.
You Can Install Using: pip install cat boost
Summarizing
No matter what machine learning, data science or AI projects you work on, you will come across these libraries in your profession. The most dedicated ML engineers usually uses all the above listed 10 projects in their career. The learning path of the machine learning libraries in Python looks like:
Pandas → NumPy → Scikit-learn → PyTorch → TensorFlow
This way, it is possible to master the basics and then move on to more advanced frameworks. But this is definitely not something “written on stone.” You can choose what library suits your purpose the best or what library you want to master in the long-term.
