Machine Learning is a new trending field these days and is an application of artificial

intelligence. Machine learning uses certain statistical algorithms to make computers work in a
certain way without being explicitly programmed. The algorithms receive an input value and
predict an output for this by the use of certain statistical methods. The main aim of machine
learning is to create intelligent machines which can think and work like human beings.

First of all…
What exactly is machine learning?

What is Machine Learning?

Machine Learning is a branch of artificial intelligence that gives systems the ability to learn
automatically and improve themselves from the experience without being explicitly programmed
or without the intervention of human. Its main aim is to make computers learn automatically
from the experience.
 Requirements of creating good machine learning systems
So what is required for creating such machine learning systems? Following are the things
required in creating such machine learning systems:
 Data – Input data is required for predicting the output.
 Algorithms – Machine Learning is dependent on certain statistical algorithms to determine
data patterns.
 Automation – It is the ability to make systems operate automatically.
 Iteration – The complete process is iterative i.e. repetition of process.
 Scalability – The capacity of the machine can be increased or decreased in size and scale.
 Modeling – The models are created according to the demand by the process of modeling.

Methods of Machine Learning

Machine Learning methods are classified into certain categories. These are:
 Supervised Learning – In this method, input and output is provided to the computer along
with feedback during the training. The accuracy of predictions by the computer during training
 is also analyzed. The main goal of this training is to make computers learn how to map input
to the output.
 Unsupervised Learning – In this case, no such training is provided leaving computers to find
the output on its own. Unsupervised learning is mostly applied on transactional data. It is used
in more complex tasks. It uses another approach of iteration known as deep learning to arrive
at some conclusions.
 Reinforcement Learning – This type of learning uses three components namely – agent,
environment, action. An agent is the one that perceives its surroundings, an environment is the
one with which an agent interacts and acts in that environment. The main goal in
reinforcement learning is to find the best possible policy.

How does machine learning work?

Machine learning makes use of processes similar to that of data mining. Machine learning
algorithms are described in terms of target function (f) that maps input variable (x) to an output
variable (y). This can be represented as:
y=f(x)
 There is also an error e which is the independent of the input variable x. Thus the more
generalized form of the equation is:
y=f(x) + e
In machine the mapping from x to y is done for predictions. This method is known as predictive
modeling to make most accurate predictions. There are various assumptions for this function.
Benefits of Machine Learning

Everything is dependent on machine learning. Find out what are the benefits of machine
learning.
 Decision making is faster – Machine learning provides the best possible outcomes by
prioritizing the routine decision-making processes.
 Adaptability – Machine Learning provides the ability to adapt to new changing environment
rapidly. The environment changes rapidly due to the fact that data is being constantly updated.
 Innovation – Machine learning uses advanced algorithms that improve the overall decision-
making capacity. This helps in developing innovative business services and models.
 Insight – Machine learning helps in understanding unique data patterns and based on which
specific actions can be taken.
 Business growth – With machine learning overall business process and workflow will be
faster and hence this would contribute to the overall business growth and acceleration.
 Outcome will be good – With machine learning the quality of the outcome will be improved
with lesser chances of error.

Branches of Machine Learning

Computational Learning Theory – Computational learning theory is a subfield of machine
learning for studying and analyzing the algorithms of machine learning. It is more or less similar
to supervised learning.
Adversarial Machine Learning – Adversarial machine learning deals with the interaction of
machine learning and computer security. The main aim of this technique is to look for safer
methods in machine learning to prevent any form of spam and malware. It works on the
following three principles:
 Finding vulnerabilities in machine learning algorithms.
 Devising strategies to check these potential vulnerabilities.
 Implementing these preventive measures to improve the security of the algorithms.
Quantum Machine Learning – This area of machine learning deals with quantum physics. In
this algorithm, the classical data set is translated into quantum computer for quantum information
processing. It uses Grover’s search algorithm to solve unstructured search problems.
Predictive Analysis – Predictive Analysis uses statistical techniques from data modeling,
machine learning and data mining to analyze current and historical data to predict the future. It
extracts information from the given data. Customer relationship management (CRM) is the
common application of predictive analysis.
Robot Learning – This area deals with the interaction of machine learning and robotics. It
employs certain techniques to make robots to adapt to the surrounding environment through
learning algorithms.
Grammar Induction – It is a process in machine learning to learn formal grammar from a given
set of observations to identify characteristics of the observed model. Grammar induction can be
done through genetic algorithms and greedy algorithms.
Meta-Learning – In this process learning algorithms are applied on meta-data and mainly deals
with automatic learning algorithms.

Best Machine Learning Tools

Here is a list of artificial intelligence and machine learning tools for developers:
1. ai-one – It is a very good tool that provides software development kit for developers to
implement artificial intelligence in an application.
2. Protege – It is a free and open-source framework and editor to build intelligent systems with
the concept of ontology. It enables developers to create, upload and share applications.
3. IBM Watson – It is an open-API question answering system that answers questions asked in
natural language. It has a collection of tools which can be used by developers and in business.
4. DiffBlue – It is another tool in artificial intelligence whose main objective is to locate bugs,
errors and fix weaknesses in the code. All such things are done through automation.
5. TensorFlow – It is an open-source software library for machine learning. TensorFlow
provides a library of numerical computations along with documentation, tutorials and other
resources for support.
6. Amazon Web Services – Amazon has launched toolkits for developers along with
applications which range from image interpretation to facial recognition.
7. OpenNN – It is an open-source, high-performance library for advanced analytics and is
written in C++ programming language. It implements neural networks. It has a lot of tutorials
and documentation along with an advanced tool known as Neural Designer.
8. Apache Spark – It is a framework for large-scale processing of data. It also provides a
programming tool for deep learning on various machines.
9. Caffe – It is a framework for deep learning and is used in various industrial applications in
the area of speech, vision and expression.
10. Veles – It is another deep learning platform written in C++ language and makes use of python
language for interaction between the nodes.

Machine Learning Applications

Following are some of the applications of machine learning:
 Cognitive Services
 Medical Services
 Language Processing
 Business Management
 Image Recognition
 Face Detection
 Video Games
 Computer Vision
 Pattern Recognition

Machine Learning in Bioinformatics

Bioinformatics term is a combination of two terms bio, informatics. Bio means related to biology
and informatics means information. Thus bioinformatics is a field that deals with processing and
understanding of biological data using computational and statistical approach. Machine Learning
has a number of applications in the area of bioinformatics. Machine Learning finds its
application in the following subfields of bioinformatics:
Genomics – Genomics is the study of DNA of organisms. Machine Learning systems can help in
finding the location of protein-encoding genes in a DNA structure. Gene prediction is performed
by using two types of searches named as extrinsic and intrinsic. Machine Learning is used in
problems related to DNA alignment.
Proteomics – Proteomics is the study of proteins and amino acids. Proteomics is applied to
problems related to proteins like protein side-chain prediction, protein modeling, and protein
map prediction.
Microarrays – Microarrays are used to collect data about large biological materials. Machine
learning can help in the data analysis, pattern prediction and genetic induction. It can also help in
finding different types of cancer in genes.
System Biology – It deals with the interaction of biological components in the system. These
components can be DNA, RNA, proteins and metabolites. Machine Learning help in modeling
these interactions.
Text mining – Machine learning help in extraction of knowledge through natural language
processing techniques.
Deep Learning

Deep Learning is a part of the broader field machine learning and is based on data representation
learning. It is based on the interpretation of artificial neural network. Deep learning algorithm
uses many layers of processing. Each layer uses the output of previous layer as an input to itself.
The algorithm used can be supervised algorithm or unsupervised algorithm. Deep Learning is
mainly developed to handle complex mappings of input and output.

Deep Neural Network

Deep Neural Network is a type of Artificial Neural Network with multiple layers which are
hidden between the input layer and the output layer. This concept is known as feature hierarchy
and it tends to increase the complexity and abstraction of data. This gives network the ability to
handle very large, high-dimensional data sets having millions of parameters. The procedure of
deep neural networks is as follows:
1. Consider some examples from a sample dataset.
2. Calculate error for this network.
3. Improve weight of the network to reduce the error.
4. Repeat the procedure.

Applications of Deep Learning

Here are some of the applications of Deep Learning:
 Automatic Speech Recognition
 Image Recognition
 Natural Language Processing
 Toxicology
 Customer Relationship Management
 Bioinformatics
 Mobile Advertising

Advantages of Deep Learning

Deep Learning helps in solving certain complex problems with high speed which were earlier
left unsolved. Deep Learning is very useful in real world applications. Following are some of the
main advantages of deep learning:
 Eliminates unnecessary costs – Deep Learning helps to eliminate unnecessary costs by
detecting defects and errors in the system.
 Identifies defects which otherwise are difficult to detect – Deep Learning helps in
identifying defects which left untraceable in the system.
 Can inspect irregular shapes and patterns – Deep Learning can inspect irregular shapes and
patterns which is difficult for machine learning to detect.

Machine Learning Algorithms

For starting with Machine Learning, you need to know some algorithms. Machine Learning
algorithms are classified into three categories which provide the base for machine learning.
These categories of algorithms are supervised learning, unsupervised learning, and reinforcement
learning. The choice of algorithms depends upon the type of tasks you want to be done along
with the type, quality, and nature of data present. The role of input data is crucial in machine
learning algorithms.
Computer Vision
Computer Vision is a field that deals with making systems that can read and interpret images. In
simple terms, computer vision is a method of transmitting human intelligence and vision in
machines. In computer vision, data is collected from images which are imparted to systems. The
system will take action according to the information it interprets from what it sees.
Supervised Machine Learning
It is a type of machine learning algorithm in which makes predictions based on known data-sets.
Input and output is provided to the system along with feedback. Supervised Learning is further
classified into classification and regression problems. In the classification problem, the output is
a category while in regression problem the output is a real value.
Unsupervised Machine Learning
It is another category of machine learning algorithm in which input is known but the output is
not known. Prior training is not provided to the system as in case of supervised learning. The
main purpose of unsupervised learning is to model the underlying structure of data. Clustering
and Association are the two types of unsupervised learning problems. k-means and Apriori
algorithm are the examples of unsupervised learning algorithms.
Deep Learning
Deep Learning is a hot topic in Machine Learning. It is already explained above. It is a part of
the family of machine learning and deals with the functioning of the artificial neural network.
Neural Networks are used to study the functioning of the human brain. It is one of the growing
and exciting field. Deep learning has made it possible for the practical implementation of various
machine learning applications.
Neural Networks
Neural Networks are the systems to study the biological neural networks. The main purpose of
Artificial Neural Network is to study how the human brain works. It finds its application in
computer vision, speech recognition, machine translation etc. Artificial Neural Network is a
collection of nodes which represent neurons.
Reinforcement Learning
Reinforcement Learning is a category of machine learning algorithms. Reinforcement Learning
deals with software agents to study how these agents take actions in an environment in order to
maximize their performance. Reinforcement Learning is different from supervised learning in the
sense that correct input and output parameters are not provided.
Predictive Learning
In this technique, a model is built by an agent of its environment in which it performs actions.
There is another field known as predictive analytics which is used to make predictions about
future events which are unknown. For this, techniques like data mining, statistics, modeling,
machine learning, and artificial intelligence are used.
Bayesian Network
It is a network that represents probabilistic relationships via Directed Acyclic Graph (DAG).
There are algorithms in Bayesian Network for inference and learning. In the network, a
probability function is there for each node which takes an input to give probability to the value
associated with the node. Bayesian Network finds its application in bioinformatics, image
processing, and computational biology.
Data Mining
Data Mining is the process of finding patterns from large data-sets to extract valuable
information to make better decisions. This technology use method from machine learning,
statistics, and database systems for processing. There exist data mining techniques like
clustering, association, decision trees, and classification for the data mining process.