Types of Machine Learning Algorithms

Types of Machine Learning Algorithms


Machine learning (ML) is becoming increasingly important as more people use online search engines, recommendation algorithms, and other software that relies on artificial intelligence (AI). Different machine learning algorithms have different benefits and uses, and data science professionals can help organizations decide which technique to use.

To learn more about the different types of machine learning algorithms, check out the infographic below, created by Maryville University’s online Master of Science in Data Science program.

  • Supervised learning helps with multiclass classification (two or more types of answers) and ensemble learning (synthesizing machine learning models to develop predictions).
  • Unsupervised learning helps with clustering (grouping data based on similarity) and anomaly detection (identifying unusual data points).
  • Semisupervised learning helps with machine translation (translating language without a full dictionary) and fraud detection (identifying fraud based on available examples).
  • Reinforcement learning helps with robotics (teaching physical tasks to robots) and resource management (helping organizations plan their resource allocation based on finite resources and defined goals).

Commonly Used Algorithms

  • Linear regression establishes a relationship between variables by fitting them to a line.
  • Logistic regression predicts the probability of an event by fitting data to a logit function.
  • A decision tree algorithm classifies problems by splitting data into two or more sets based on the most significant attribute.
  • The KNN algorithm stores all cases and classifies new cases based on the most similar class.
  • The gradient boosting algorithm and AdaBoost algorithm are used to boost other algorithms when large amounts of data need to make predictions with high accuracy.

How to Choose the Best Algorithms and Methods

To determine which algorithms and methods to use, you should first determine a project goal. Machine learning algorithms solve specifically identified problems.

Then, work to understand the data you’re using. Is the data raw? Random? Biased? Is the data organized? Large enough? Prepared?

Once you know your data, you should evaluate the training time. Lower training quality means the ML learns faster, but it also means sacrificing the accuracy and efficiency of longer training.

Finally, you need to choose the features and parameters. Depending on the training time and quality, more features and parameters can produce higher-quality results.

The Future of Machine Learning

AI and machine learning will continue to be increasingly important. These are some of the people leading the field into the future.

Forerunners in Machine Learning

Forerunners in machine learning technology include the following individuals:

Andrew Ng

Andrew Ng is the founder and CEO of Landing AI, and the founder of deeplearning.ai. Ng developed the Autonomous Helicopter Project and the Stanford Artificial Intelligence Robot project, which are used in speech recognition systems, open-source robotics software platforms, and deep learning.

Fei-Fei Li

Fei-Fei Li is the Sequoia Professor of Computer Science at Stanford University. Li invented ImageNet, a massive data set and benchmarking drive that has helped expand AI and deep learning, and currently works on ambient intelligent systems for healthcare delivery.

Demis Hassabis

Demis Hassabis is the co-founder and CEO of DeepMind. Hassabis currently leads all AI efforts at Google and has written several award-winning papers.

Ian Goodfellow

Ian Goodfellow is the former director of machine learning at Apple. Goodfellow wrote the textbook Deep Learning and developed the system that transcribes the addresses of locations photographed by Google Street View. He currently works at DeepMind, which builds neural networks.

How to Get Involved

If you’re interested in working with machine learning, the first step is to learn more about it. Find ways to learn about AI, machine learning algorithms, and other necessary programming skills.

It’s also important to gain math skills. Study linear algebra, statistics, and probability through online courses or tutoring.

Earning a degree is another way to learn more about ML. Some machine learning jobs may require a degree in data science, computer engineering, or a related field.

Practicing with free data sets can be helpful. Use existing data sets to focus on programming and machine learning algorithms.

It’s a good idea to build a portfolio. Work on projects that highlight your skills and interests to attract employers.

Learning to Learn

Understanding the types of machine learning algorithms can help you effectively use AI and ML to mitigate risk, improve sales, and better understand the people that computer systems interact with. A solid understanding of programming, math, and communication can boost your experience with machine learning algorithms.

Sources

Built In, “The Top 10 Machine Learning Algorithms Every Beginner Should Know”

DataFlair, “Advantages and Disadvantages of Machine Learning Language”

Expert.ai, “What Is Machine Learning? A Definition”

Indeed, 10 Key Benefits of Machine Learning (with Uses and FAQs)

Indeed, “How to Break Into Machine Learning in 11 Steps”

Label Your Data, “”How to Choose the Right Machine Learning Algorithm: A Pragmatic Approach”

ReadWrite, “AI Leaders: List of the Top 10 Visionaries in the Industry”

SAS Institute, Banking Risk Management

SAS Institute, Health Care Data Analytics

SAS Institute, Retail Analytics and Consumer Goods Software

TechTarget, “Machine Learning”



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