Machine Learning is the science (and art) of programming computers so they can learn from data.

Slightly more general definition: Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed.

Example: Emails spam filter is a Machine Learning program that can learn to flag spam given examples of spam emails (e.g., flagged by users) and examples of regular (nonspam, also called “ham”) emails. The examples that the system uses to learn are called the training set. Each training example is called a training instance (or sample).

Machine learning is a subfield of artificial intelligence that uses algorithms trained on data sets to create models that enable machines to perform tasks that would otherwise only be possible for humans, such as categorizing images, analyzing data, or predicting price fluctuations.

Features of Machine Learning

  • Machine learning uses data to detect various patterns in a given dataset.
  • It can learn from past data and improve automatically.
  • It is a data-driven technology.
  • Machine learning is much similar to data mining as it also deals with the huge amount of the data.

Why we need Machine Learning?

Suppose you want to write a spam filter; the traditional programming approach would be like this:

  1. First you would try to find how the spam looks like. You might notice that some words or phrases (such as “credit card,” “free,” “4U,” and “amazing”) have more chance to come up and you would also notice a few other patterns in the sender’s name, the email’s body, and so on.
  2. You would write a algorithm for each of the patterns that you noticed, and your program would flag emails as spam if a number of these patterns are detected.
  3. You would test your program and repeat steps 1 and 2 until it is good enough.
traditional Approach in Computer science

Since the problem is not trivial, your program will likely become a long list of complex rules—pretty hard to maintain.

In contrast, a spam filter based on Machine Learning techniques automatically learns which words and phrases are good predictors of spam by detecting unusually frequent patterns of words in the spam examples compared to the ham examples. The program is much shorter, easier to maintain, and most likely more accurate.

Machine Learning approach

Moreover, if spammers notice that all their emails containing “4U” are blocked, they might start writing “For U” instead. A spam filter using traditional programming techniques would need to be updated to flag “For U” emails. If spammers keep working around your spam filter, you will need to keep writing new rules forever. In contrast, a spam filter based on Machine Learning techniques automatically notices that “For U” has become unusually frequent in spam flagged by users, and it starts flagging them without your intervention

Machine Learning approach

Another area where Machine Learning shines is for problems that either are too com plex for traditional approaches or have no known algorithm. For example, consider speech recognition: say you want to start simple and write a program capable of distinguishing the words “one” and “two.” You might notice that the word “two” starts with a high-pitch sound (“T”), so you could hardcode an algorithm that measures high-pitch sound intensity and use that to distinguish ones and twos.

Obviously this technique will not scale to thousands of words spoken by millions of very different people in noisy environments and in dozens of languages. The best solution (at least today) is to write an algorithm that learns by itself, given many example recordings for each word.

To summarize, Machine Learning is great for:

  • Problems for which existing solutions require a lot of hand-tuning or long       lists of rules: one Machine Learning algorithm can often simplify code and perform better.
  • Complex problems for which there is no good solution at all using a traditional approach: the best Machine Learning techniques can find a solution.
  • Fluctuating environments: a Machine Learning system can adapt to new       data.

•   Getting insights about complex problems and large amounts of data.

Types of Machine Learning

Machine learning can be broadly classified into four types based on the nature of the learning system and the data available: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Let’s delve into each of these:

Supervised learning:  In this approach, the model is trained on a labeled dataset. In other words, the training data you feed to the algorithm includes the desired solutions, called labels.

The model learns a mapping between the input (features) and the output (label) during the training process. Once trained, the model can predict the output for new, unseen data.

Supervised learning can be grouped further in two categories of algorithms:

image
  • Classification
  • Regression

Some common supervised learning algorithms are:

  • k-Nearest Neighbours
  • Linear Regression
  • Logistic Regression
  • Support Vector Machines (SVMs)
  • Decision Trees and Random Forests
  • Neural networks

Unsupervised learning: It involves training the model on an unlabeled dataset. The model is left to find patterns and relationships in the data on its own.

Unsupervised machine learning is often used by researchers and data scientists to identify patterns within large, unlabeled data sets quickly and efficiently. 

It can be further classifieds into three main categories of algorithms:

  • Clustering
  • Dimension Reduction
  • Association
image 1

Some of the most important unsupervised learning algorithms are:

Clustering

  • K-Means
  • K-Medoid
  • DBSCAN
  • Hierarchical Cluster Analysis (HCA)

Visualization and dimensionality reduction

  • Principal Component Analysis (PCA)

Association rule learning

  • Apriori

Semi-supervised learning: Semi-supervised machine learning uses both unlabeled and labeled data sets to train algorithms. Generally, during semi-supervised machine learning, algorithms are first fed a small amount of labeled data to help direct their development and then fed much larger quantities of unlabeled data to complete the model. For example, an algorithm may be fed a smaller quantity of labeled speech data and then trained on a much larger set of unlabeled speech data in order to create a machine learning model capable of speech recognition. 

Semi-supervised machine learning is often employed to train algorithms for classification and prediction purposes in the event that large volumes of labeled data is unavailable.

Reinforcement Learning: In this the learning system, called an agent in this context, can observe the environment, select and perform actions, and get rewards in return (or penalties in the form of negative rewards). It must then learn by itself what is the best strategy, called a policy, to get the most reward over time. A policy defines what action the agent should choose when it is in a given situation.

Unlike supervised and unsupervised learning, reinforcement learning is particularly suited to problems where the data is sequential, and the decision made at each step can affect future outcomes.

For example, many robots implement Reinforcement Learning algorithms to learn how to walk.

Main Challenges of Machine Learning

  • Insufficient Quantity of Training Data: Machine Learning algorithms takes a lot of data to train properly. Even for very simple problems you typically need thousands of examples, and for complex problems such as image or speech recognition you may need millions of examples.
  • Data Bias and Fairness: ML algorithms are only as good as the data they are trained on. Biased data can lead to discriminatory outcomes, requiring careful data selection and monitoring of algorithms.
  • Interpretability and Explainability: Complex ML models can be difficult to understand, making it challenging to explain their decision-making processes. This lack of transparency can raise questions about accountability and trust.
  • Job layoffs: as some jobs are automated, workers in the impacted field will likely face layoffs that could force them to switch to a new career or risk long-term unemployment.

Reference

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