What is Machine Learning??

Nowadays Machine Learning and Artificial Intelligence is the top trending topic. But both have different things to different people. Machine Learning is a sub-part of artificial intelligence, which enables systems to automatically learn and become more accurate without being explicitly programmed. The primary aim is to focus on the development of computer programme automatically without human intervention and adjust actions accordingly. Actually, machine learning is a very vast subject and it grew rapidly.  Machine learning focuses on developing computer programs that can access data, analyze it and use it to learn.

Why Machine Learning is Important?

Day by day machine learning going to become more important and trendious technology of the recent time. There is a lot of new invention made in the field of machine learning so it’s not like the machine learning in the past. Many new popular machine learning algorithms have been come to solve complex mathematical calculations of big data in the faster way. Machine learning going to change the future. Now almost companies aware of the importance of machine learning but he has lack of expertise to use this technology. It’s the big challenge for companies that he facing. Machine learning is used for many things such as to improve customer service, finding anomalies, recommending new products and many more. In a world, about more than 80% companies planning to use machine learning to improve their customer experience.

There are many other advantages of machine learning

  • Machine learning is used in different applications such as banking and financial sector, healthcare, retail, publishing and social media, robot locomotion, game playing etc.
  • Machine learning is used by google and facebook to push relevant real-time advertisements based on users past search on web pages and mobile devices.
  • Machine learning can handle multi-dimensional and multi-variety data in dynamic or uncertain environments.
  • There is a number of tools available to provide a continuous quality improvement in large and complex process environments.
  • Rapidminer is a source program helps in the increased usability of algorithms for different applications.

On the other side, there are disadvantages of Machine Learning

Disadvantages of Machine Learning

Below are the disadvantages of using Machine Learning:

  • Acquisition of relevant data is the major challenge. Based on different algorithms data need to be processed before providing as input to respective algorithms. This has a significant impact on results to be achieved or obtained.
  • Interpretation of results is also a major challenge to determine the effectiveness of machine learning algorithms.
  • Based on which action to be taken and when to be taken, various machine learning techniques are needed to be tried.
  • Next level of machine learning technology is being researched.

Uses of Machine Learning  (Who’s Using it)

Whether we realize it or not, we encounter machine learning on a daily basis. Aside from in our day-to-day lives, industries from retail to government and more are depending on machine learning to get things done. Below is a short list of how different industries are utilizing machine learning.

FINANCE

Banking and financial service provider are using machine learning applications due to its quantitative nature. The technology is being used in thousands of ways in industry-wide, but here are a few of the most commonly used:

  • Fraud detection – Machine learning algorithms can analyze an enormous amount of transactions at a time, and learn a person’s typical spending patterns. If a transaction is made that is unusual, it will reject the transaction and indicate potential fraud.
  • Trading floors – With its ability to efficiently assess data and patterns, machine learning can assist with quick decision-making in real-time.
  • Credit and risk management – Typically assessing credit risk is labor intensive and is prone to human-subjected errors. With machine learning, certain algorithms can help to provide mitigation recommendations.

UTILITIES

Utility companies can utilize machine learning in a number of ways, including uncovering hidden energy patterns, learning customer’s energy behaviors, and more.

HEALTHCARE

  • Diagnosis – Machine learning can analyze data and identify trends or red flags within patients to potentially lead to earlier diagnoses and better treatments.
  • Patient information – Data can be collected from a patient’s device to assess their health in real-time.
  • Drug discovery – Given its ability to detect patterns within data, scientists are able to better predict drug side effects and results of drug experiments without actually performing them.

MARKETING AND SALES

  • Personalization – Machine learning helps to display advertising on web pages and mobile devices based on user browser and search history. It helps to give customers a unique and personalized experience.

OIL AND GAS

  • Energy sources – machine learning provides the potential to find new energy sources by analyzing different minerals on the ground.
  • Streamlining oil distribution – Algorithms work to make oil distribution more efficient and cost-effective.
  • Reservoir modeling – Reservoir modeling is a machine learning techniques focus on optimization of hydraulic fracturing, reservoir simulation etc.

TRANSPORTATION

  • Efficient transportation – Analysis of data can identify certain patterns and trends to make routes more efficient for public transportation, delivery companies, and more.

What are some popular machine learning methods?

There are different methods of machine learning but supervised and unsupervised are most widely used methods for machine learning. Let’s see in details one by one –

  • Supervised Machine Learning:

Supervised learning, is the type of system in which both input and output are clearly identified and algorithms are labeled for classification for future data processing. The name supervised learning came from the scientist act that he guide to teach the algorithm what conclusion should come with. It’s the same as a child might learn arithmetic from an instructor. Today, supervised machine learning going to become more common across a wide range of industry use cases.  

For example,  For image processing some vehicle pictures provided with labeled such as cars and trucks. After observation, the system should be able to distinguish between categorizing unlabeled images.

Supervised learning classified into two categories:

  1. Classification:
  2. Regression:
  • Unsupervised Machine Learning:

Unsupervised machine learning is a complex process. In unsupervised learning both input and output are unknown. Means it has the ability to solve complex problems using just the input data, there is no reference data at all. In unsupervised learning, the system doesn’t find out the right output, but it extracts the data and can draw inferences from datasets to identify the hidden structures from unlabeled data. Example of unsupervised learning includes self-organizing maps, principal and independent component analysis, k-means clustering, and association rules.

  • Semi-supervised Machine Learning:

Semi-supervised learning clearly suggests that it is a combination of both supervised and unsupervised learning. It uses both labeled and unlabeled data to find the output. It uses little bit amount of labeled data and a large amount of unlabeled data. In semi-supervised learning, different methods are used such as classification, regression, and prediction.

Examples of semi-supervised learning would be facing identification on a webcam and voice recognition techniques.

  • Reinforcement Machine Learning:

Reinforcement learning discovers through trial and error which actions yield the greatest rewards. The main characteristics of reinforcement learning are trial and find the error and delayed reward. This method allows machines and software agents to automatically identify the ideal behavior within a specific context in order to increase its performance over the given amount of time. This learning has three types of components i.e. the agent, the environment, the actions. The agent is the learner or the decision maker, the environment includes everything that the agent interacts with, and the actions are what the agent can do.

Some Machine Learning Algorithms And Processes

If you’re studying machine learning, you should aware of basic common machine learning algorithms and processes. These algorithms may help you quickly get value from big data. Choosing algorithms is very difficult, but it depends on quality, size, nature of the data. Depends on how much time you have? No one experience data scientist can’t tell which algorithm gives the best result.

Below are some list of machine learning algorithms –

  • Neural networks.
  • Decision trees.
  • Random forests.
  • Associations and sequence discovery.
  • Gradient boosting and bagging.
  • Support vector machines.
  • Nearest-neighbor mapping.
  • k-means clustering.
  • Self-organizing maps.
  • Local search optimization techniques (e.g., genetic algorithms.)
  • Expectation maximization.
  • Multivariate adaptive regression splines.
  • Bayesian networks.
  • Kernel density estimation.
  • Principal component analysis.
  • Singular value decomposition.
  • Gaussian mixture models.
  • Sequential covering rule building.

Tools and processes

As we know by now, it’s not just the algorithms. Ultimately, the secret to getting the most value from your big data lies in pairing the best algorithms for the task at hand with:

  • Comprehensive data quality and management.
  • GUIs for building models and process flows.
  • Interactive data exploration and visualization of model results.
  • Comparisons of different machine learning models to quickly identify the best one.
  • Automated ensemble model evaluation to identify the best performers.
  • Easy model deployment so you can get repeatable, reliable results quickly.
  • An integrated, end-to-end platform for the automation of the data-to-decision process.

What is the best language for machine learning:

While choosing the best programming language for machine learning it doesn’t matter. You should familiar with machine learning tools and libraries. There is a number of machine learning languages with different libraries available which helps to develop a product. Depends on your role in a company and the task you have to complete, certain language tools and libraries can be effective than others. A developer having good knowledge of Java, Python, R would be added advantageous and it is most important for developing a high-end software product. Hence it is important for you to first analyze the kind of use-case you are dealing with and then obtain any programming language.

Let’s discuss some basic languages

Java is a general-purpose programming language and widely used in enterprise programming. It is developed by James Gosling in 1991. It is generally used for front-end desktop application. If you are new in machine learning then Java is not the first choice for them, it’s for them who have background knowledge of Java programming. In industry to developed a machine learning application, Java tends to be used more than Python for network security, including in cyber attack and fraud detection use cases.

Below are some libraries used in Java programming:

  • WEKA: WEKA stand for Waikato Environment for Knowledge Analysis. WEKA is a library used for data mining task.
  • JDMP: JDMP stands for Java Data Mining Package. It is java library used for data analysis and machine learning.
  • MLlib (Spark): It is machine learning library for Apache Spark.

Python :

Python is general-purpose scripting language designed in 1991 by Guido van Rossum. It is open source and is used for web and Internet development, software development, and much more. It is very popular in the field of machine learning and data scientist. Python has some machine learning libraries and has the ability to train a variety of machine learning models. Code for these libraries written in C/C++ or in Fortran with python package serving as wrappers.

Let look some Python machine learning library:

  • Scikit-learn
  • TensorFlow
  • Theano
  • Caffe
  • GraphLab create

R :

R is a programming language built for statistical computing and graphics. It was developed by Robert Gentleman and Ross Ihaka in August 1993. It is used for statistical and graphical techniques such as linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, etc.  It provides a huge collection of packages which enable you to apply all types of machine learning algorithm and analysis procedures.It is a free software.

Below are some packages of R for machine learning

  • Caret
  • MLR
  • H2o

C++ :

C++ is a general-purpose object-oriented programming language. It is ideal for low-level software such as operating system components and networking protocol, it also used for low-level memory manipulation. It is suitable for beginners to developed machine learning applications.

Below are libraries of C++ for machine learning

  • Mlpack
  • Shark
  • Shogun

Difference between Artificial Intelligence, Machine Learning, and Deep Learning

Comparison between Artificial Intelligence, Machine Learning, and Deep Learning:

  Artificial Intelligence Machine Learning Deep Learning
Definition (TechTarget) The simulation of human intelligence processes by machines, especially computer systems Machine learning is a type of artificial intelligence (AI) which has the ability to learn without being explicitly programmed An aspect of artificial intelligence (AI) that is concerned with emulating the learning approach that human beings use to gain certain types of knowledge
Description An umbrella term that encompasses everything from robotic process automation to actual robotics Focuses on the development of computer programs that can change when exposed to new data. A way to automate predictive analytics, where the machine learns from a set of data and use it to make predictions.
Includes Learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction Data mining, extraction of data, analysis of data, and using that data to detect patterns in data and adjust program actions accordingly. Data mining, data extraction, data classification, data implementation
Scope Limited scope. Machines are programmed for a particular task and they typically cannot do anything other than follow programming. Large scope. Machines can learn to infer and make predictions by analyzing large sets of data. Unlimited scope. Machine can learn to emulate human intelligence and decision making by learning and analyzing large sets of data.
Capabilities Development of self-driving cars, face recognition, web search, industrial robots, missile guidance, and even tumor detection It is used for fraud detection, spam detection, handwriting recognition, image search, Text-based searches, speech recognition, Street View detection, and translation Computer vision, automatic speech recognition, natural language processing, audio recognition, and bioinformatics
Examples Computer playing chess against a human player DeepMind’s AlphaGo, which in 2016 beat Lee Sedol at Go, by analyzing a large data set of expert moves. Automatic Game Playing or Automatic Handwriting Generation

Based on the above comparison we can say that Artificial Intelligence is the computer’s attempt to imitate human intelligence. Whereas Machine Learning is used to analyzing large chunks of data and learning from it. On the other side, deep learning allows the computer to learn and differentiate and make decisions like a human.