An Ultimate Guide to Machine Learning


Machine Learning, a subset of Artificial Intelligence, can simply be described as the study involved with computer algorithms which improve automatically through experience.

Algorithms are a set of rules which govern specific behaviour and forms an integral part of the processes according to which Artificial Intelligence and subsequently Machine Learning, operate.

Machine Learning algorithms have the ability to build a mathematical model based on simple data which is known as “training data” which enables the computer to make predictions or decisions without having been programmed to explicitly do so.

Through learning from data, computers can carry out certain task and there are three approaches or categories pertaining to machine learning, namely:

  • Supervised Learning where the computer is presented with inputs and their desired outputs as an example and the aim is for the computer to learn a general rule which maps both inputs and outputs.
  • Unsupervised Learning involves the process where there are no labels provided to the learning algorithm and the computer is left on its own to find its own structure and subsequent output.
  • Reinforcement Learning which allows the computer program to interact with a dynamic environment. In this environment, the computer must perform a certain goal and it is provided with feedback either as rewards, or as punishment.

Relationship to other fields

1.   Artificial Intelligence

Machine Learning, as a scientific endeavour, was first born out of the quest surrounding Artificial Intelligence. When AI was merely an academic discipline, some researchers showed keen interest in having machines learn from data.

The problem was initially approached with a variety of symbolic methods along with “terminal networks”, as it was termed at the time.

These consisted mostly of perceptrons along with other models later found to be reinventions of generalized linear statistic models.

Due to an increasing emphasis on the logical, knowledge-based approach, there was a rift caused between AI and Machine Learning.

Theoretical and practical problems associated with data acquisition and representation plagued probalistic systems and by 1980, AI was dominated by expert systems, placing statistics out of favour.

Despite this, there was still ongoing work on symbolic/knowledge-based learning within AI and this resulted in inductive logic programming although statistical lines of research continued in pattern recognition and information retrieval outside of AI.

By the 1990s, Machine Learning had been reorganized as a separate field and it had started to flourish. The goal changed from achieving AI to taking on solvable problems of a more practical nature.

2.   Data Mining

Both Machine Learning and Data Mining employ similar methods and both tend to overlap significantly with the difference that Machine Learning focuses on predictions which are based on known properties which are learned from training data.

Data Mining, on the other hand, focuses on the discovery of unknown properties which exist in the data itself.

Although, Data Mining employs a variety of Machine Learning methods with the exception that the goals are different.

Machine Learning subsequently employs Data Mining methods as part of unsupervised learning or as a pre-processing step to help improve the accuracy of the learner.

3.   Optimization

Machine Learning is closely tied to optimization as a great amount of learning problems are formulated as minimization as result of a loss function on a training set of examples.

Loss functions have the ability of expressing the discrepancy which exists between the predictions of the model receiving training and the actual problem instances.

The distinct differences between Machine Learning and Optimization is the goal of generalization as optimization algorithms can minimize the loss on a training set while Machine Learning is focused on minimizing the loss on unseen samples.

4.   Statistics

Both fields concerned are quite closely related in terms of methods used, but they are also distinct in their principal goal. Statistics can draw population inferences from a sample and Machine Learning finds patterns which are generalizable predictive.

Machine Learning and Theory

A learner’s core objective is associated with the ability to generalize from its experience as it is the ability of a learning machine to perform at the utmost accuracy on new examples and/or tasks that it has never seen after experiencing a learning data set.

The training examples provided are derived from some generally unknown probability distribution and requires the learner to build a general model concerning the specific space.

This enables the learner to produce accurate predictions in new cases more sufficiently.

Machine Learning algorithms and the computational analysis thereof in addition to their performance is a branch of theoretical computer science which is known as computational learning theory.

The future is uncertain and therefore training sets are finite and due to this, learning theory is unable to yield guarantees on the performance of algorithms and thus probalistic bounds associated with performance tend to be quite common.

When considering the best performance in the context of generalization, both the complexity of the hypothesis and the complexity of the function which underlies the data should match.

Should the hypothesis be less complex than the function, it results in the model underfitting the data. Should the complexity increase in response, the training error decreases. Overfitting occurs when the hypothesis is too complex and results in poor generalization.

Not only are performance bounds studied, but the time complexity and feasibility are also studied in detail by learning theorists.

Should a computation can be done in polynomial time, the computational theory is considered feasible.

Two kinds of time complexity results can be identified, positive results, which show a specific class of functions which can be learned in polynomial time, and negative results, which show that specific classes cannot be learned in polynomial time.


There are a great number of Machine Learning algorithms and the types differ in their approach along with the type of data they input and output, and lastly the type of task or problem that they intend to solve.

The types of learning algorithms commonly associated with Machine Learning include, but is not limited to:

  • Supervised learning – which build a mathematical model of a set of data consisting of both the inputs as well as the desired outputs.
  • Unsupervised learning where the algorithms take a set of data with only inputs and proceed to find a structure in the data. This allows the algorithms to learn from test data void of labels, classifications, or categories.
  • Semi-supervised learning involves training examples which are missing some training labels and it falls between unsupervised and supervised learning.
  • Reinforcement learning is based on software agents and how they should take actions in an environment so that the notion of cumulative reward can be maximised. This field is also studied by a variety of other disciplines.
  • Self-learning was introduced in 1982 as a machine learning paradigm and it involves learning with no external rewards or teacher advices.
  • Future Learning allows algorithms to discover better representations of the inputs which are provided during training and they often attempt to preserve information in their input while attempting to transform it to something useful.
  • Sparse dictionary learning, as a feature learning method, is where a training example is represented as a linear combination consisting of fundamentals and which is in addition, assumed to be a sparce matrix.
  • Anomaly detection is also known as outlier detection in Data Mining and it is the identification of items, events, or observations considered to be rare which may raise suspicions by differing significantly from most of the data.
  • Robot learning, specifically in development robotics, involve algorithms which generate their own sequences of learning experiences to acquire new skills by self-guided exploration along with social interactions with humans.
  • Association rules is a Machine Learning method which is rule-based and used to discover relationships that exist between different variables in large databases.

Machine Learning Models

To successfully perform Machine Learning, a model needs to be created. This model is trained on some training data and enables it to process additional data which can make predictions.

There are numerous models which have been used and researched specifically for machine learning systems such as:

  • Artificial neural networks
  • Decision trees
  • Support vector machines
  • Regression analysis
  • Bayesian networks, and
  • Generic algorithms

In addition, Machine Learning models also require a substantial amount of data which enables them to perform. To be able to train a Machine Learning model, there needs to be a significantly large and representative sample of data collected from a data set.

This data, which can be derived from the training set, can be varied either as corpus of text, a collection of images, or data which has been collected from individual users of a specific service.

How is Machine Learning used in Businesses?

The business applications of both AI and machine learning have not quite fully emerged until the past few years but today, Machine Learning can be used in a variety of business applications such as:

  • E-commerce including CLV modelling, dynamic pricing, and recommendation engines.
  • Smart hiring which allows for the screening of resumes along with cover letters to identify top performers.
  • Image recognition in using Machine Learning to train computers to read not only medical scans, but recognize specific products from pictures, and numerous others.

Machine Learning is also used in:

  • Online advertising
  • Software engineering
  • Marketing
  • Financial markets
  • Internet of Things, and numerous others.

Online Advertising

Where online advertising is concerned, Machine Learning is dramatically changing advertising in the following ways:

  • Companies can capitalize on unexpected insights
  • It allows for the improvement of ad creative
  • It can boost contextual relevance
  • It allows for the targeting of more defined segments, and
  • Allows for more strategic bidding.

Software Engineering

The impact that Machine Learning has on Software Engineering may still be small, but there are however significant changes emerging as Machine Learning is being applied more to aid in problems detected with Software Engineering.

Machine Learning is a more mature discipline which has superb modern introductions to the subject of Software Engineering and how it can boost the upgrades associated with Software Engineering.


Machine learning provides marketers with the ability to personalize the experience of the customer through the following:

  • Segmentation as well as targeting
  • Customer churn
  • Customer lifetime value
  • The recommendation of engines
  • Marketing mix modelling, and
  • Customer attribution

Financial Markets

The emergence and popularized use of algorithms in the financial markets has increased rapidly with the continued innovation in technology and there has been a monumental shift in the micro-structure of financial markets as computers take the place of human traders.

Computer programs have the ability to both buy and sell based on a series of rules and parameters which they have inferred themselves and which is based on data derived instead of the rules and parameters being set by humans.

The focus with such machine learning algorithms is based especially on that of recurrent neural networks as they take into account the sequence of a variable over a certain time when it makes predictions and should it succeed, this means greater profits for traders.

Internet of Things

There are numerous companies that make use of both Machine Learning and Internet of Things, more commonly known as IoT, by utilizing IoT sensor to collect data and feed it into Machine Learning models.

The resulting information is then used to change how the company operates and to refine the products and services that they offer to be more in line with customer needs and demands overall.

As there are more internet-connected sensors being built into a variety of devices such as cars, plains, buildings, businesses, and devices, there is a substantial amount more data drawn and utilized more efficiency to improve on quality and type of products and services.

Final Thoughts

Despite a rocky start, Machine Learning is being actively used to make predictions and forecast along with the ability to identify images correctly, improve AI speech, and so much more.

Machine Learning can become a major stride in business process optimization, something that major cloud providers already know, and which they are tapping into.

There is ongoing research and development in the field for Machine learning and numerous platforms being developed and released, along with various other innovations for the process and methods used to become more widespread and frequently used.

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