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Machine Learning

What Is Machine Learning?

Machine learning is the iterative process a computer uses to identify patterns in a dataset given specific constraints. It involves “teaching” a computer to explore environments and acquire new skills without explicitly programming it to do so.

Machine learning is one of the foundations of artificial intelligence, which is the science of making a system or machine exhibit human intelligence. Machine learning enables artificial intelligence.

Another term often discussed with machine learning is deep learning. Deep learning is an evolution of machine learning. Deep learning uses an artificial neural network to drive machine learning algorithms without human guidance.

Why Is Machine Learning Important?

Machine learning is important in business because it can analyze bigger and more complex data while delivering faster, more accurate results at larger scales. This helps organizations quickly identify profitable opportunities and potential risks.

The Machine Learning Life Cycle

The steps needed to build a machine learning model are:

  • Select and prepare data
  • Select a machine learning algorithm to use
  • Train the algorithm on the data to create a custom model
  • Validate the resulting model’s performance on testing (a.k.a. “holdout”) data
  • Use model on new data (a.k.a. “scoring”)

Machine learning models should also be monitored and optimized over time to continue to drive the most powerful and accurate business outcomes.

Machine Learning Methods

There are three main categories of machine learning: supervised, unsupervised, and reinforcement.

ML- Supervised Learning
Supervised learning describes a class of problems that involves using a model to learn a mapping between input examples and the target variable. Supervised learning algorithms are trained using a labeled dataset and are taught to come to a specific conclusion based on historical data.


ML- Unsupervised Learning
Unsupervised learning describes a class of problems that involves using a model to describe or extract relationships in data. Compared to supervised learning, unsupervised learning operates upon only the input data without expected outputs or target variables. Unsupervised learning algorithms ingest unlabeled datasets, look for similarities or patterns in the data and use that information to sort, group, and classify the data without being taught what to look for.


ML- Reinforement Learning
Reinforcement learning describes a class of problems where an agent (the learner) operates in an environment (everything the agent interacts with) and must learn to operate using feedback. The use of an environment means that there is no fixed training dataset, rather a goal or set of goals that an agent is required to achieve, actions they may perform, and feedback about performance toward the goal. The algorithm uses trial and error to determine which actions yield the best outcomes.

Machine Learning Use Cases

Machine learning can be leveraged by many organizations and has so many industry-specific applications. Some examples include:

Human Resources

  • Workforce trends and forecasting
  • Recruiting optimization
  • Capacity prediction

Consumer Packaged Goods

  • Product lifecycle management
  • Stock optimization
  • Demand forecasting

Supply Chain

  • Supplier optimization
  • Inventory planning and replenishment
  • Risk analysis and monitoring

Healthcare

  • Clinical and population health management
  • Medical imaging insights
  • Patient risk identification

Office of Finance

  • Planning budgeting, forecasting, and cash flow analysis
  • Revenue forecasting
  • Fighting fraud, waste, and abuse

IT/COE

  • Root cause analysis
  • Ticket triage
  • Anomaly detection

Retail

  • Personalization
  • Recommendations
  • Merchandise supply planning

Machine Learning and Analytic Process Automation

For a machine learning model to be successful, the data being used to train the model needs to be thoroughly and thoughtfully prepared and analyzed. If this foundational process can be automated in any way, it can get a business from data input to insights more quickly, saving time and money in the process.

Automation of entire analytics processes is key to success and keeps companies agile. Machine learning can help organizations deliver transformative outcomes more quickly, and analytic process automation makes it even faster.

How to Get Started with Machine Learning

Alteryx Analytic Process Automation Platform™ fully integrates the complete analytics workflow. In addition to data preparation and other features, it allows for automated, fully-guided machine learning and modeling, as well as “expert-mode” options, to drive faster results.

Data access, preparation, modeling, monitoring and model tuning, and sharing of analytic results all happen in the same place, on one easy-to-use platform. Get started by signing up for a free trial of the platform today.

We also offer the Advancing Data & Analytics Potential Together (ADAPT) program to new graduates and unemployed data workers. You’ll get access to free data science and analytics learning courses, an Alteryx Designer license, virtual 1:1 support from our associates, and more. Learn more and apply for the program today.

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