Machine learning drives innovation across industries and has recently gained much interest in the business world. Take these statistics as proof:
- Over 80% of enterprises want employees who understand machine learning1
- More than 75% of businesses prioritize machine learning over other technological campaigns2
- For nearly 65% of companies, the need to prioritize machine learning has increased2
- The global employment rate of machine learning engineers will grow by about 22% between now and 20301
These figures bring one thing into perspective for data scientists and machine learning engineers: Being well-versed in machine learning technologies can position both them and their companies for success. Knowing more about popular machine learning tools can help position you for career success and leadership opportunities. Read on to discover popular machine learning platforms, libraries and data preprocessing tools.
Cloud-Based Machine Learning Platforms
Machine learning platforms provide an infrastructure for creating, training and deploying machine learning algorithms. They automate the machine learning model building process. This makes it easy to deploy new AI solutions at scale.3
For years, organizations needed to invest in on-premise infrastructure to use machine learning models. This was expensive, especially for small and midsize businesses. Cloud-based platforms make machine learning more accessible and affordable, and they eliminate the need for in-house infrastructure.4
Let’s take a look at examples of popular cloud-based solutions.
Azure Machine Learning
Azure Machine Learning is a Microsoft product. It provides developers with tools to develop, train and execute machine learning algorithms. Azure integrates with technologies designed for cross-workspace collaboration. This streamlines machine learning operations (MLOps).5 The platform supports popular programming languages like Python and R.6
Amazon SageMaker has three features that streamline machine-learning tasks for different professionals:7
- SageMaker Canvas: Provides a no-code, visual interface for business analysts to make machine learning predictions
- SageMaker Studio: Enables data scientists to prepare algorithm training data and develop machine learning models
- SageMaker MLOps: Enables machine learning engineers to execute and manage machine learning programs
This cloud platform supports programming languages such as Ruby and Python.
Google Cloud offers a myriad of machine learning and AI solutions to automate workflows. This makes building custom models easy, fast and efficient. Additionally, the platform comes with a Natural Language API. This feature empowers developers to use natural language understanding in their apps. It also enables engineers to train machine learning models to categorize, extract and interpret emotions (sentiment analysis).8 Google Cloud supports Go, Java, Ruby, Python and Rust, among other programming languages.9
Machine Learning Libraries
Machine learning libraries are powerful frameworks. They equip machine learning engineers and data scientists with pre-built code and ready-to-use functions. This eliminates the need to write code from scratch, which saves time and accelerates the machine learning model development process.10
There are several popular machine learning libraries to explore.
TensorFlow is a powerful machine learning framework designed for deep learning—a machine learning technique that trains computers to think like the human brain. TensorFlow helps machine learning models identify patterns and make decisions based on big, unlabeled and unstructured data. This enables developers and data scientists to equip their models with human intelligence. They can use TensorFlow to build systems that analyze large, complex data and perform complicated tasks.11
PyTorch is a popular deep-learning framework. Like TensorFlow, it comes with GPU acceleration—graphic processing unit support for enhanced computational performance. This enables developers and researchers to train their models quickly.12, 13 PyTorch emphasizes an object oriented dataset and dynamic assembly of graph components. It incurs little runtime overhead.
Scikit-learn is a popular ML library with an intuitive interface that makes it suitable for beginners in machine learning. It provides an extensive collection of algorithms for machine learning tasks, such as:14
- Classification: Involves identifying which group an object belongs to
- Clustering: Focuses on categorizing the same objects into sets
- Regression: Involves determining the relationship between dependent and independent variables
Scikit-learn is powerful and easy to use, but it’s not the best choice for deep learning compared to TensorFlow or PyTorch, as it is not optimized for deep learning and lacks enhanced computation capabilities.15 But, it is often used in data preprocessing and results analysis tasks because of its wide range of capabilities and ease of use.
Data Preprocessing Tools
Data preprocessing converts raw data into a format that algorithms can understand. For instance, random forest is a commonly-used machine learning algorithm that does not accept missing (null) values in a dataset.16 For machine learning experts to deploy a random forest algorithm, they preprocess null values.
Data preprocessing techniques include:17
- Data cleaning: Involves removing incorrect data, eliminating duplicates and replacing missing values with estimates (imputation), such as mean
- Data transformation: Focuses on changing the data into a suitable format, such as scaling it to a common range (normalization)
- Data reduction: Involves reducing the size of a dataset without losing required information
- Data augmentation: Involves expanding the existing data set by making changes that do not materially affect the data interpretation
- For example, if a photograph is randomly rotated, it is still the same photograph. And, if various noises are added to recordings of speech, after controlling for noise level, they will have the same effect on intelligibility. Changes such as these are often done in preprocessing for efficiency.
Let’s take a closer look at data preprocessing tools.
Automunge automates the prediction of missing data in a dataset. It also facilitates other data preprocessing tasks, such as normalization and imputation.18
Pandas is user-friendly, flexible and powerful. It comes with a two-dimensional table called DataFrames. This feature enables completing data preparation tasks, such as:19
- Sorting data
- Creating derived columns
- Filling in missing values in a dataset
Become a Leader in Machine Learning With MSOE’s Online Programs
Businesses are looking for people with machine learning knowledge to strengthen their workforce. Gain advanced machine learning skills with an online Master of Science in Machine Learning from Milwaukee School of Engineering. This program equips you with real-world experience and focuses on how to use machine learning technologies to solve complex industrial problems.
The curriculum for the online M.S. in Machine Learning helps you master various machine learning tools, many of which were discussed in this post. In each course, you will go in depth on theory and application of a different essential aspect of machine learning. While there are many post-baccalaureate programs that can introduce you to machine learning concepts and tools, MSOE’s program takes these lessons a step further by focusing on the application of machine learning to industrial problems and the development and deployment of machine learning-based products.
If you’re not quite ready for a master’s program, MSOE also offers an online Graduate Certificate in Applied Machine Learning. The program consists of two application-oriented courses that fuse concepts from statistics and computer science to design algorithms and software that process data, make predictions and aid decision making. After completing your certificate, you have the option to apply your earned credits to the full master’s program.
- Retrieved on July 29, 2023, from zippia.com/advice/machine-learning-statistics/
- Retrieved on July 29, 2023, from forbes.com/sites/louiscolumbus/2021/01/17/76-of-enterprises-prioritize-ai--machine-learning-in-2021-it-budgets/?sh=ef6d62618a37
- Retrieved on July 29, 2023, from snowflake.com/guides/machine-learning-platforms.
- Retrieved on July 29, 2023, from roboticsbiz.com/machine-learning-cloud-or-on-premise/
- Retrieved on July 29, 2023, from azure.microsoft.com/en-us/products/machine-learning
- Retrieved on July 29, 2023, from learn.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/data-science-and-machine-learning
- Retrieved on July 29, 2023, from aws.amazon.com/sagemaker/
- Retrieved on July 29, 2023, from cloud.google.com/products/ai
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- Retrieved on July 29, 2023, from coursera.org/articles/python-machine-learning-library
- Retrieved on July 29, 2023, from azure.microsoft.com/en-us/resources/cloud-computing-dictionary/what-is-deep-learning/
- Retrieved on July 29, 2023, from viso.ai/deep-learning/pytorch-vs-tensorflow/
- Retrieved on July 29, 2023, from pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/
- Retrieved on July 29, 2023, from scikit-learn.org/stable/
- Retrieved on July 29, 2023, from scikit-learn.org/stable/modules/neural_networks_supervised.html
- Retrieved on July 29, 2023, from geeksforgeeks.org/data-preprocessing-machine-learning-python/
- Retrieved on July 29, 2023, from geeksforgeeks.org/data-preprocessing-in-data-mining/
- Retrieved on July 29, 2023, from researchgate.net/publication/358763310_Missing_Data_Infill_with_Automunge
- Retrieved on July 29, 2023, from nvidia.com/en-us/glossary/data-science/pandas-python/