Machine learning

What is machine learning?

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.

Some machine learning methods

Machine learning algorithms are often categorized as supervised or unsupervised.

• Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.
• In contrast, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.
• Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiring gun labeled data generally doesn’t require additional resources.
• Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.

Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.

Advantage and disadvantage

Advantages of Machine learning

i. As machine learning has many wide applications. Such as banking and financial sector, healthcare, retail, publishing etc.

ii. Google and Facebook are using machine learning to push relevant advertisements. That advertisements are based on users past search behavior.

iii. Machine learning is used to handle multi-dimensional and multi-variety data in dynamic environments.

iv. Machine learning allows time cycle reduction and efficient utilization of resources.

v. If one wants to provide a continuous quality, large and complex process environments. There are some tools present because of machine learning.

vi. As there are too many things that come under the practical benefit of machine learning. Also, they involve the development of autonomous computers, software programs. Hence, it includes processes that can lead to the automation of tasks.

Disadvantages of Machine Learning

i. Machine learning has the major challenge called Acquisition. Also, based on different algorithms data need to be processed. And, it must be processed before providing as input to respective algorithms. Thus, it has a significant impact on results to be achieved or obtained.

ii. As we have one more term interpretation. That it results is also a major challenge. That need to determine the effectiveness of machine learning algorithms.

iii. We can say the uses of the machine algorithm is limited. Also, it’s not having any surety that it’s algorithms will always work in every case imaginable. As we have seen that in most cases machine learning fails. Thus, it requires some understanding of the problem at hand to apply the right algorithm.

iv. Like deep learning algorithm, machine learning also needs a lot of training data. As we can say it might be cumbersome to work with a large amount of data. Fortunately, there are a lot of training data for image recognition purposes.

v. One notable limitation of machine learning is its susceptibility to errors. Brynjolfsson and McAfee said that the actual problem with this inevitable fact. That when they do make errors, diagnosing and correcting them can be difficult. As because it will need going through the underlying complexities.

vi. There are fewer possibilities to make immediate predictions with a machine learning system. Also, don’t forget that it learns through historical data. Thus, the bigger the data and the longer it needs to expose to these data, the better it will perform.

vii. Lack of variability is another machine learning limitation. Brynjolfsson and McAfee said that machine learning deals with statistical truths. In situations where ML is not included in the historical data, it will be difficult to prove. That the predictions made by this system are suitable for all scenarios.

Top 5 best Programming Languages for Artificial Intelligence field

If it’s like you are working on a new artificial intelligence project and still have not decided which language you should use to program it, then you are at a right place.
Artificial Intelligence is a branch of engineering, which basically aims for making the computers which can think intelligently, in the similar manner the intelligent humans think. Here are the top languages that are most commonly used for making the AI projects:

1. Python
Python is considered to be in the first place in the list of all AI development languages due to the simplicity. The syntaxes belonging to python are very simple and can be easily learned. Therefore, many AI algorithms can be easily implemented in it. Python takes short development time in comparison to other languages like Java, C++ or Ruby. Python supports object-oriented, functional as well as procedure oriented styles of programming. There are plenty of libraries in python, which make our tasks easier. For example:- Numpy is a library for python that helps us to solve many scientific computations. Also, we have Pybrain, which is for using machine learning in Python.

2.R
R is one of the most effective languages and environment for analyzing and manipulating the data for statistical purposes. Using R, we can easily produce a well-designed publication-quality plot, including mathematical symbols and formulae where needed. Apart from being a general-purpose language, R has numerous packages like RODBC, Gmodels, Class, and Tm which are used in the field of machine learning. These packages make the implementation of machine learning algorithms easy, for cracking the business associated problems.

3.Lisp
Lisp is one of the oldest and the most suited languages for the development in AI. It was invented by John McCarthy, the father of Artificial Intelligence in 1958. It has the capability of processing the symbolic information effectively.
It is also known for its excellent prototyping capabilities and easy dynamic creation of new objects, with automatic garbage collection. Its development cycle allows interactive evaluation of expressions and recompilation of functions or file while the program is still running. Over the years, due to advancement, many of these features have migrated into many other languages thereby affecting the uniqueness of Lisp.

4.Prolog
This language stays alongside Lisp when we talk about development in the AI field. The features provided by it include efficient pattern matching, tree-based data structuring, and automatic backtracking. All these features provide a surprisingly powerful and flexible programming framework. Prolog is widely used for working on medical projects and also for designing expert AI systems.

5. Java
Java can also be considered as a good choice for AI development. Artificial intelligence has a lot to do with search algorithms, artificial neural networks, and genetic programming. Java provides many benefits: easy use, debugging ease, package services, simplified work with large-scale projects, a graphical representation of data and better user interaction. It also has the incorporation of Swing and SWT (the Standard Widget Toolkit). These tools make graphics and interfaces look appealing and sophisticated.

Applications of machine learning

Machine Learning Applications in Healthcare

Doctors and medical practitioners will soon be able to predict with accuracy on how long patients with fatal diseases will live. Medical systems will learn from data and help patients save money by skipping unnecessary tests. Radiologists will be replaced by machine learning algorithms. McKinsey Global Institute estimates that applying machine learning techniques to better inform decision making could generate up to $100 billion in value based on optimized innovation, enhanced efficiency of clinical trials and the creation of various novel tools for physicians, insurers, and consumers. Computers and Robots cannot replace doctors or nurses, however, the use of life-saving technology (machine learning) can definitely transform the healthcare domain. When we talk about the efficiency of machine learning, more data produces effective results – and the healthcare industry is residing on a data goldmine.

Machine Learning Applications in Finance

More than 90% of the top 50 financial institutions around the world are using machine learning and advanced analytics. The application of machine learning in Finance domain helps banks offer personalized services to customers at lower cost, better compliance and generate greater revenue.

Machine Learning Examples in Finance for Fraud Detection

You are watching “Game of Thrones” when you get a call from your bank asking if you have swiped your card for “$X” at a store in your city to buy a gadget. It was not you who bought the expensive gadget using your card – in fact, it has been in your pocket all noon. How did the bank flag this purchase as fraudulent? All thanks to Machine Learning! Financial fraud costs $80 billion annually, of which, Americans alone are exposed to a risk worth $50 billion per annum.

One of the core machine learning use cases in banking/finance domain is to combat fraud. Machine learning is best suited for this use case as it can scan through huge amounts of transactional data and identify if there is any unusual behavior. Every transaction a customer makes is analyzed in real-time and given a fraud-score that represents the likelihood of the transaction being fraudulent. If the fraud score is above a particular threshold, a rejection will be triggered automatically which would 

Otherwise be difficult without the application of machine learning techniques as humans cannot reviews 1000’s of data points in seconds and make a decision.

·         Citibank has collaborated with Portugal based fraud detection company Feedbag that works in real-time to identify and eliminate fraud in online and in-person banking by alerting the customer.

·         PayPal is using machine learning to fight money laundering. PayPal has several machine learning tools that compare billions of transactions and can accurately differentiate between what is a legitimate and fraudulent transaction amongst the buyers and sellers.

Machine Learning Applications in Retail

Machine learning in retail is more than just the latest trend, retailers are implementing big data technologies like Hadoop and Spark to build big data solutions and quickly realizing the fact that it’s only the start. They need a solution which can analyse the data in real-time and provides valuable insights that can translate into tangible outcomes like repeat purchasing. Machine learning algorithms process this data intelligently and automate the analysis to make this supercilious goal possible for retail giants like Amazon, Target, Alibaba, and Walmart.

Machine Learning Applications in Social Media

Machine learning offers the most efficient means of engaging billions of social media users. From personalizing news feed to rendering targeted ads, machine learning is the heart of all social media platforms for their own and user benefits. Social media and chat applications have advanced to a great extent that users do not pick up the phone or use email to communicate with brands – they leave a comment on Facebook or Instagram expecting a speedy reply than the traditional channels.