Machine Learning Engineer
The average salary for Machine Learning Engineer is $157,315 / year according to Indeed.com
Python libraries for machine learning provide a wide range of functionality for machine learning, from data preprocessing and feature engineering to model building and evaluation. They are also well-documented and have active communities, making them easy to learn and use.
We have compiled a stack with a brief overview of each Python machine learning library, including labels and helpful data points.
Machine Learning Specialization
Master fundamental AI concepts and practical machine learning skills through this high-ROI specialization taught by AI visionary Andrew Ng.
Machine Learning Scientist with Python
Master key machine learning skills with 23 concise courses on Python, supervised & unsupervised learning, NLP, TensorFlow, PyTorch, Keras, and more for a successful career.
Machine Learning with PySpark
This program enhances your skills in data-driven predictions using Apache Spark, covering techniques like decision trees, logistic and linear regression, ensembles, and pipelines.
Become a Machine Learning Engineer
You'll master the skills necessary to become a successful Machine Learning Engineer by learning data science and machine learning techniques, and building and deploying machine learning models in production using Amazon SageMaker.
Practicing Machine Learning Interview Questions in Python
Machine Learning DevOps Engineer - Nanodegree
Master DevOps skills for automating ML model building & monitoring with this Nanodegree program, offering technical mentorship for aspiring MLOps/ML DevOps engineers.
NumPy is a Python library for scientific computing. It provides a high-performance multidimensional array object and a collection of routines for operating on arrays. NumPy is used in a wide variety of scientific and engineering applications, including machine learning, image processing, and data analysis.
Pandas is a Python library for data analysis and manipulation. It provides a high-performance data structure called a DataFrame, which is a two-dimensional data structure with labeled rows and columns. Pandas also provides a variety of functions for data analysis and manipulation, such as filtering, sorting, and aggregating data.
Scikit-learn is a Python library for machine learning. It provides a wide range of algorithms for classification, regression, clustering, and other machine learning tasks. Scikit-learn is built on top of NumPy and SciPy, and it provides a simple and easy-to-use interface for machine learning.
Keras is a high-level API for building and training machine learning models. It is built on top of TensorFlow and provides a simple and easy-to-use interface for building and training neural networks. Keras is a good choice for beginners and for developers who want to quickly prototype and deploy machine learning models.
PySpark is a Python API for Apache Spark, a distributed computing framework for large-scale data processing. PySpark provides a high-level API for working with Spark DataFrames and RDDs. PySpark is a good choice for machine learning applications that need to be scalable to large datasets.
H2O.ai is an open-source, distributed machine learning platform that makes it easy for businesses of all sizes to build and deploy AI models. It offers a variety of features for data preparation, machine learning, and model deployment, as well as a powerful AutoML platform that can automatically select and tune machine learning algorithms for your data.
Python is a widely used general-purpose programming language in machine learning. It is a popular choice for machine learning because it is easy to learn and use, has a large and active community, and has a wide range of libraries and tools available.
Python machine learning libraries offer various functions for tasks, such as data preprocessing, feature engineering, model building, and model evaluation. They also provide a diverse set of machine learning algorithms, including classification, regression, clustering, and dimensionality reduction.
Some of the most popular Python machine learning libraries include:
- NumPy: NumPy is a scientific computing library for Python. It offers functions for linear algebra, statistics, and signal processing, making it suitable for data preprocessing and feature engineering.
- SciPy: SciPy is another scientific computing library for Python that includes modules for statistics, signal processing, and optimization. It is useful for data preprocessing, feature engineering, and developing custom machine learning algorithms.
- Pandas: Pandas is a library for data analysis and manipulation in Python. It provides functions for data cleaning, wrangling, and visualization, making it a valuable tool for data preprocessing and feature engineering.
- Scikit-learn: Scikit-learn is a comprehensive machine learning library for Python. It offers a wide range of machine learning algorithms that are easy to use and efficient, covering classification, regression, clustering, and dimensionality reduction.
- TensorFlow: TensorFlow is a popular library for machine learning, particularly for deep learning tasks. It is well-suited for building and training deep learning models.
- PyTorch: PyTorch is another library for machine learning, with a focus on deep learning. It is known for its flexibility and ease of use in building and training deep learning models.
- Keras: Keras is a high-level API for building and training machine learning models in Python. It is particularly useful for beginners and developers who want to quickly prototype and deploy machine learning models.
Python Machine Learning Libraries: Applications
Python machine learning libraries find applications in industries such as finance (stock prediction, fraud detection), healthcare (disease diagnosis, patient outcome prediction), retail (product recommendation, demand prediction), and manufacturing (machine failure prediction, quality control).
- Classification: Predicting the class of a data point, such as spam detection or image recognition.
- Regression: Predicting continuous values, such as house prices or customer demand.
- Clustering: Grouping data points based on similarities, useful for customer segmentation or product categorization.
- Dimensionality reduction: Reducing the number of features in a dataset while preserving important information, aiding in model performance and data visualization.
These are just a few examples of the many ways that Python machine learning libraries can be used. As the field of Data Science, Machine Learning and AI continues to grow, Python is expected to remain a popular choice for its development.
Disclaimer: This website's content is for informational and promotional purposes only, and we may earn a commission from purchases made through our site. We are not affiliated with, endorsed, or sponsored by any companies whose logos and materials appear here, unless stated otherwise. All trademarks and intellectual property belong to their respective owners.
If you believe any content infringes on your copyrights, please submit a DMCA takedown request with the necessary information, and we will act accordingly. By using our website, you agree to this disclaimer and assume full responsibility for using the provided information.