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Machine Learning

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

What exactly is machine learning?

Machine learning is a subfield of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic how humans learn, gradually improving accuracy. 

IBM has a long history of working with machine learning. With his studies on the game of checkers, one of its own, Arthur Samuel, is credited with coining the term “machine learning.” In 1962, the self-proclaimed checkers master, Robert Nealey, played the game on an IBM 7094 computer and lost to the computer. 

This performance appears minor compared to what can be done now, but it is regarded as a critical milestone in artificial intelligence. Over the next few decades, technical advances in storage and processing power will enable some of the innovative products we know and enjoy today, such as Netflix’s recommendation engine or self-driving automobiles. 

Machine learning is a critical component of the rapidly expanding discipline of data science. Algorithms are trained to generate classifications or predictions using statistical approaches, revealing crucial insights in data mining initiatives. These insights are then used to drive decision making within applications and enterprises, hopefully influencing key growth indicators. 

As big data expands and grows, so will the market demand for data scientists, who will be required to assist in the discovery of the most relevant business issues and, ultimately, the data to answer them.

How does machine learning function?

UC Berkeley (external to IBM) divides a machine learning algorithm’s learning system into three major components. 

A Decision Process: Machine learning algorithms are typically used to create a prediction or classification. Your algorithm will generate an estimate about a pattern in the data based on some input data, which can be labelled or unlabeled. 

 

An Error Function: An error function is used to evaluate the model’s prediction. 


Model Optimization: If the model fits better to the data points in the training set, weights are modified to narrow the gap between the known example and the model prediction. The algorithm will repeat this assessment and optimize the procedure, updating weights autonomously until an accuracy criterion is reached.

Methods of machine learning

Machine learning classifiers are classified into three types. 

  1. Machine learning with supervision 
  2. Semi-supervised Learning
  3. unsupervised Machine Learning 

Machine learning with reinforcement

Reinforcement machine learning is a behavioural machine learning paradigm comparable to supervised learning, except that the algorithm is not trained on sample data. Using trial and error, this model learns as it goes. To establish the optimal advice or policy for a given situation, we will reinforce a series of successful outcomes. Use examples for machine learning in the real world. 

 

Here are a few instances of machine learning that you might meet daily:

 

  1. Speech recognition
  2. Customer service
  3. Computer vision
  4. Recommendation engines
  5. Automated stock trading

IBM Cloud and machine learning

IBM Watson Studio on IBM Cloud Pak for Data offers the end-to-end machine learning lifecycle on a data and AI platform. Machine learning models may be built, trained, and managed wherever your data resides and deployed anywhere in your hybrid multi-cloud environment.

Machine Learning Difficulties

As machine learning technology progresses, it has undoubtedly made our lives easier. However, incorporating machine learning into enterprises has created a variety of ethical questions about AI technologies. Among them are the following:

  1. Technological singularity
  2. AI impact on jobs
  3. Privacy
  4. Bias and discrimination
  5. Accountability

Deep Learning vs Machine Learning

Because deep learning and machine learning are frequently used interchangeably, it is essential to understand the differences between the two. Artificial intelligence encompasses the subfields of machine learning, deep learning, and neural networks. Deep learning, on the other hand, is a subfield of machine learning, while neural networks are a subfield of deep learning. 

 

The difference between deep learning and machine learning is in how each algorithm learns. Deep learning automates most of the feature extraction process, removing part of the need for manual human involvement and allowing for the usage of more extensive data sets.

Frequently Asked Questions

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