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Machine learning is the iterative process a computer uses to identify patterns in a dataset given specific constraints.
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.
- 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.
variable. Supervised learning algorithms are trained using a labeled dataset and are taught to come to a
specific conclusion based on historical data.
Machine Learning Use Cases
Machine learning can be leveraged by many organizations and has so many industry-specific applications. Some examples
- Workforce trends and forecasting
- Recruiting optimization
- Capacity prediction
Consumer Packaged Goods
- Product lifecycle management
- Stock optimization
- Demand forecasting
- Supplier optimization
- Inventory planning and replenishment
- Risk analysis and monitoring
- 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
- Root cause analysis
- Ticket triage
- Anomaly detection
- Merchandise supply planning
Machine Learning and Analytics 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 analytics automation makes it even faster.
How to Get Started with Machine Learning
Alteryx Analytics 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