Machine Learning (Definition)

This science is not new, it is just one that has evolved over time. Machine learning applies artificial intelligence, exposing a model to data and its adaptation. Let’s get to understand what machine learning is, its types, its application and uses, and machine learning limitations.

Machine Learning

What is Machine Learning?

Machine Learning is a branch and application of artificial intelligence that pivots around software systems or applications that use data to automatically learn, imitate, and improve without exact programming. It is aimed to allow computers to learn and predict an output without human assistance, becoming more accurate based on the data provided.

Application And Uses of Machine Learning

As an important aspect of data science and analysis, machine learning has numerous applications and uses. Following a trend or pattern of data usage, algorithms can generate useful insights used for decision-making within applications and businesses to influence growth. Machine learning is used in the following areas:

  • Recommendation engine: This is a type of data filter that predicts a user’s preferences using machine learning algorithms to find the usage patterns of consumers.A good example is the “Recommended videos for you” section on Netflix or “based on photos you have like” feed on Instagram or “people you may know” on Facebook. This may be the most popular use of machine learning. Applications generally gather users trends using provided data explicitly or implicitly to predict preferences to achieve consumer satisfaction. If a user’s pattern changes, the software adjusts.
  • Virtual assistants: Virtual assistants are incorporated into many systems examples include Google Now, Alexa, and Siri. They are called AI or Digital or Smart assistants which when activated collect information based on a user’s previous involvement with them.
  • Commuting predictions: GPS services make use of machine learning to estimate areas of congestion and distance based on daily usage. Also in mobile applications like Bolt and Uber, ML is used to define price surge hours by predicting the rider demands and use.
  • Customer relationship management: Customer relationship management is the use of combined strategies and technologies by companies to analyze interactions of customers and potential customers. CRM software uses machine learning models like bots to extract information from a website and show customers, chatbots, email, or prompt sales team members to respond to the most important messages first.
  • Business intelligence
  • Human resource information systems: These systems allow HR processes to occur electronically. Data entry and management resources used by HRIS systems work with machine learning models, to schedule interviews, performance appraisals and to filter through applications identifying the best candidates for a position.
  • Self-driving cars
  • Fraud detection: Most payment systems with the help of machine learning use data collected over time to distinguish between legitimate transactions and illegitimate ones.
  • Spam email filtering
  • Malware threat detection
  • Business Process Automation (BPA)
  • Predictive Maintenance.

Types Of Machine Learning

Data scientists choose the method of ML used depending on the kind of data to be predicted. Machine learning methods are classified based on how accurate an algorithm’s prediction gets over time.

They include

  • Supervised Learning
  • Unsupervised Learning
  • Semi-Supervised Learning
  • Reinforcement Learning
  1. Supervised Learning

A known analysis from the past dataset is used by the Data scientist to train algorithms to predict future events and give desired results.  These datasets used for training are labelled and have defined variables fed into the model which are cross-validated to avoid underfitting or overfitting for correlation. Both the input and the output of the algorithm are specified. The learning algorithm compares its output with the correct, intended output and finds errors to modify the model accordingly.

Supervised learnings are used for

  1. Classifying spam mail in a separate folder from the inbox
    1. Regression modeling: Predicting continuous values.
    1. Ensembling: Combination of multiple predictions of machine learning models to produce an accurate prediction.
    1. Binary classification: Sharing data into two categories.
    1. Multi-class classification: Choosing between more than two types of answers.

Methods used in supervised learning include: support vector machine (SVM),  neural networks, linear regression,  naïve Bayes, logistic regression, random forest, and others

2. Unsupervised learning

In contrast to supervised machine learning, unsupervised learning trains on, analyze algorithms and unlabeled data sets without human intervention. This studies and finds hidden paths, meaningful connections, and discovers similarities and differences between data. The data that algorithms train on as well as the predictions or recommendations they output are predetermined.

They used for

  1. Clustering: Splitting the dataset into groups based on similarity includes k clustering, probabilistic clustering methods, etc
  2. Anomaly detection: Identifying unusual data points in a data set; image and pattern detection
  3. Association mining: Identifying sets of items in a data set that frequently occur together; Customer segmentation
  4. Dimensionality reduction: Reducing the number of variables in a data set either by principal component analysis (PCA) and singular value decomposition (SVD)

3. Semi-supervised learning

Somewhere in between supervised and unsupervised learning, we have semi-supervised learning. Data scientists train the system with smaller labelled data sets from a larger unlabeled dataset as a guide but the model is free to find hidden data paths and develop understanding on its own without human interference. The performance of algorithms considerably increases efficiency and accuracy. Semi-supervised learning solves not having enough labeled data (or not being able to afford to label enough data) to train a supervised learning algorithm. It serves as a middle ground for the types above.

They can be used for:

  1. Machine translation: Teaching algorithms to translate language based on less than a full dictionary of words.
  2. Labeling data: Algorithms trained on small data sets can learn to apply data labels to larger sets automatically.
  3. Fraud detection: Identifying cases of fraud when you only have a few positive examples.

4. Reinforcement learning

With a goal and clearly defined rules for a multi-step process, data scientists use reinforcement learning to teach a machine. It is a similar learning model to supervised learning although the model isn’t training with a dataset and the model decides the paths it takes. This works on a trial and discovering errors or rewards systems. A recognized pattern of successful outcomes in line with the ultimate goal can now be used for a recommendation or probable solution to a problem and maximizes its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.

Reinforcement learning is often used in areas such as:

  1. Resource management: Given finite resources and a defined goal, reinforcement learning can help enterprises plan out how to allocate resources.
  2. Robotics: Robots can learn to perform tasks in the physical world using this learning method
  3. Video gameplay: Bots are taught to play several video games using reinforcement learning

Limitations and Challenges of Machine Learning

  • Expensive: Projects require expensive software infrastructure and data scientists and analysts command high salaries
  • Bias and discrimination: The problem of machine learning being biased. Some algorithms trained on data sets exclude certain populations or contain errors that can lead to inaccurate models of the world that, at best, fail and, at worst, are discriminatory. A company is at the risk of failure, regulatory and reputational harm if it bases core business processes on biased models
  • Technological singularity: Although there is a palpable fear of AI surpassing human intelligence, many researchers are not concerned with this idea in the near or immediate future.
  • Accountability: There is currently no significant legislation to regulate AI practices, there is no real enforcement mechanism to ensure that ethical AI is practiced.
  • AI impact on jobs: Although artificial intelligence has in the past created human job loss, we have seen that when a new technology rolls around, the market demand for specific job roles shifts.
  • Privacy: This is to be discussed in the context of data privacy, data protection, and data security, and these concerns have allowed policymakers to make more strides here in recent years.
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