What is Machine Learning
Machine Learning is a part of Artificial Intelligence. It builds algorithms that allow computers to learn how to perform tasks from data instead of requiring a programmer to write code for those tasks. Machine Learning does this by generalizing from examples and is a more cost-effective and faster approach when Big Data is involved.
What you can do with machine learning
- Data Mining
- Text Analysis
- Image Processing
- Machine Learning
Anomaly detection: This technique is used for credit card fraud detection. Companies can detect which transactions are outside the usual purchasing patterns of the user.
Association rules: Supermarkets and eCommerce sites use this method to discover customer purchasing habits by identifying which products are brought together.
Predictions: Banks use this to determine credit worthiness and probability of default for potential loans. Other uses include trading (building predictive models of prices and market volatilities), portfolio management and risk management.
Machine Learning is used to classify information from text such as emails, chats, documents and even tweets. This gives companies the ability to do:
Spam filtering: With Machine Learning, email programs classify an email as spam based on the content in the email.
Sentiment analysis: This method can be used to classify if the opinion expressed by the writer is positive, neutral or negative.
Information extraction: The system is taught to extract a particular piece of information such as keywords, addresses, names etc.
Image tagging: The Machine Learning algorithms automatically detect faces or specified objects in a photo based on the photos that you manually tag.
Optical Character Recognition: The algorithms learn to identify a certain image as a written character and transform a scanned text document into a digital file.
Self-driving cars: Machine Learning is at the heart of the driverless car. It helps the car learn what is a stop sign or if a car is approaching by looking at each frame taken by a video camera.
Machine Learning is being applied to help robots become smarter at avoiding obstacles and completing tasks. A popular technique for achieving this is Reinforcement Learning, where the robot learns to perform a task by learning from the reinforcement it receives. It can be negative if it hits the wall and positive if it gets to the goal.
Why organizations must implement machine learning
- Machine learning in general
- Why it is relevant more than ever
- Supervised Learning – features, Algorithms, Applications
- Unsupervised Learning – features, Algorithms, Applications
- Examples and strategies
- Feedback / questions
Why you need machine learning
Sales and account managers can get alerts from the algorithms about specific customers or deals that are at risk. Machine Learning gives management actionable, real-time insights about their customers and vendors.
Marketing campaigns can be personalized with Machine Learning to meet the needs of prospective customers. Customers can be given special offers based on their previous buying patterns.
Predictive analysis is playing an important role in HR departments today. Machine Learning models are being deployed to identify and recruit employees and also to make existing employees work more efficiently.
The existing financial systems show historical financial transactions. But applications using Machine Learning show future opportunities and how to get more profits out of existing systems.
Machine learning services
We also help companies setup their systems to use Google’s machine learning algorithms which analyzes data and predicts future outcomes. The service works with Google BigQuery or can pull in datasets from Google cloud storage.
Ensure your machine learning projects are a success with our data scientists.
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This is what makes our data scientists not just good, but great – their ability to communicate with business decision makers and enable them to draw important business conclusions.Consult With Our Machine Learning Experts
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