Understanding The Concept Machine Learning
The major distinction between computers and humans is that humans learn from past experiences. Whereas machines or computers need to be told what they have to do and how they need to operate. The strict logic machines are computers without zero common sense. It means that if we tell them what to do, they offer step by step instructions and details about what the users want them to perform. This is the reason that programmers write in scripts to follow those kinds of step by step instructions. This is the time the machine learning concept comes. The concept of machine learning comprises of making the systems to learn from data of past experiences. Actually, machine learning is the artificial intelligence application which offers computers to learn and enhance experience without getting programmed in an explicit way. It concentrates on computer program development in accessing information and learn by themselves.
This procedure of learning starts with data or observations like instruction or direct experience. For looking for information patterns and making the best choices in the future depending on the specimens provided to the systems. The main objective is to enable the systems to learn in an automatic manner without human assistance or intervention and through adjusting various actions.
Various methods of machine learning
The algorithms of machine learning are divided as unsupervised algorithms and supervised.
- Unsupervised machine learning algorithm
This learning algorithms which are utilized when the data is used in training is neither labelled nor classified. These studies know how the computers can perform an operation for describing the structure which is hidden from data which is unlabelled.
- Supervised algorithm of machine learning
It is applied to the data of past experiences to learning new information utilizing examples for predicting the events of future. When you start the known training data set analysis, the algorithm creates an inferred operation for making predictions about the values of output.
- Reinforcement machine learning algorithms
This machine learning algorithm is studying about various techniques with the environment by creating the actions and find the rewards and errors. The search of trial and error reward are the relevant things of learning of reinforcement. This technique enables the software agents and machines to identify the genuine behavior with particular context to enhance its execution.
- Semi supervised machine learning algorithms
This is among the unsupervised and supervised learning as they utilize both data which is labelled and unlabelled for training. It is like using little labelled information and lots of unlabelled information. The computers that we utilize are capable to enhance the accuracy of learning.
Examples of Machine Learning
In today’s era, it is used in many types of applications. The example of it is Facebook news feed which utilizes machine learning for personalizing every feed of member. The programming is all about utilizing predictive and statistical analysis for identifying the data of users in patterns and utilize those patterns to enhance the news feed. Another common example is the matching feature of dating apps. As casual dating apps become more popular they gain more data to improve the machine learning algorithms that match prospective partners or hookup buddies. Both mainstream dating sites and adult hookup apps, like the one seen here, have strong data that help their algorithms better understand the type of dater or casual sex partner that they are looking for allowing them to offer a very tailored service that delivers. This is a very interesting space that relies heavily on data and machine learning to innovate and compete with other dating and social networking products and services.
- The customer relationship management systems utilize learning models for analyzing prompt and email sales team for responding to the crucial messages at first.
- The systems of human resource utilize learning designs for identifying the effective employee characteristics and depend on the data and knowledge to find better people for positions which are open.
This age is considered to be the Age of ‘Big Data’, much as the previous age was considered to be the Digital Age. Since the middle of the last century, the word Data has been used to mean computer information which is formatted in a special way that can be transmitted, stored and used for calculation. With the entry of the world of Big Data, the need for immense storage capabilities were felt and this situation continued until 2010. But now, ‘Hadoop’ and similar frameworks have solved the need for storage, and Big Data Processing is the next phase. The science derived from Big Data, called ‘Data Science’, is a new interdisciplinary field that uses Science to create methods, processes, systems and algorithms to extract insightful knowledge from large masses of both Structured and Raw Data. With the omnipresence of the Online, physically located schools for training are becoming time-consuming and therefore outdated. Online Schools for Data Science are now becoming accessible everywhere, and this gives our Generation X a wonderful opportunity to learn and practice a brand new profession, that is at the cutting-edge of information technology.
Why Is Data Science necessary?
Most of the Data that has been acquired, traditionally, has been comparatively small in size, and generally structured. Simple BI (Business Intelligence) tools were sufficient to process this Data. But as time goes on, the Data to be processed is mostly Unstructured Data, or at best Semi Structured. Data trends indicate that within the next few years, more than 75% to 85% of all Data will be Unstructured or Semi Structured. The bulk of this data will be generated from varied sources, with complex or no obvious interactions like — Financial Logs, Multimedia Forms, Text Files, Sensors, and direct inputs from instruments. This vast and varied mass of Data cannot be processed by the simple BI Tools that have been previously available. A vastly more advanced, complex and a complete science is required to tackle this Big Data. This is Data Science which can be used for predictive analytics. Weather Forecasting is a typical example. Data from multiple sources, such as, Satellites and Land Weather Stations, Ships and Aircrafts, Radars and Weather Balloons, can be collected and analyzed to build models that will not only forecast weather, but also help predicting the intensity, timing and route of major natural calamities. Customer’s preferences can be pinpointed from analysis of existing purchase history, age and income of the Customer. Companies also use valuable date to improve their products and services. For example adult apps like fuckbook and snapfuck among other local fuckbuddy apps can analyze data from their users to improve the functioning of their platforms. Data can be so pinpoint that they can specify to improve functioning for casual sex seekers using the mobile app in a specific location or age group of any other piece of data really.
The following background is necessary for Data Science Candidates – Statistics, Machine Learning, Programming with ‘R’ and ‘Python’, Multivariable Calculus and Linear Algebra, Data Wrangling, Visualizations and Communications and Intuition. The Candidate must have a Bachelor’s Degree in Mathematics, Physics, Computer Science, IT or similar discipline, and preferably Master’s Degree in Data and some experience in the expected field of work.
Online Schools are now available in almost all disciplines, sometimes in even the most advanced or cutting-edge fields. It is not surprising therefore that Data Science has gone Online, in a big way. But the general public is only dazzled by the paycheck for Data Scientists. They are not aware of the tough pre-qualification requirements to study this highly advanced field of learning (which has been outlined above). Some of the best sponsored
Online Schools for Data Science follows:
- University of California, Berkeley: Berkeley offers innovative program features like – Live Face-to-face Online Classes with Self-Paced Course work, MIDS Faculty-Administered Rigorous, Relevant Curriculum and finally a Degree from UC Berkeley, all without having to re-locate.
- University of Denver: The program from the Daniel Felix Ritchie School of Engineering and Computer Science can be attended by students without prior programming experience, by using three bridging courses.
- Southern Methodist University: Successful candidates will benefit from SMU’s vast connection to global business communities across a range of industries. It offers Project-based Approach, In-person Immersion, Interdisciplinary Curriculum and Live Online Classes.
- Syracuse University: Data Science at Syracuse is an 18-month Online Graduate Program featuring opportunities to network with Peers and Faculty, Face-to-face Online Weekly Classes, Immersive Course work fostering close collaboration.
There are several others offering Online courses like Bay Path University, Bellevue University, Cabrini University, CTU, CUNY, Drexel University, Elmhurst College, IIT Illinois and so on, which are also worth considering.
In your house you must have a place where you store items right? A garage, a store room, any kind of place? Well if you do then what do you keep in there? You could either keep valuable items, or some the worst junk you could possibly keep. But most of the time it is filled with the things that you will need and end up using most of the times.
If you consider a library, what is it stored with? Books, it’s stored with useful books. Now both these example are relating to the containment of something, the thing with this is that these containment options are visible to us. But what about the intangible one? Are there such things? Yes, yes there are, these are called databases, the library, your garage or your store room can also be considered as a database.
What you have to know is that there are different types of databases and how they all work and more. There are actually 4 main types of database explained, there can be more, but currently 4 is what is relevant than the others. There is much more to know about it, and all of these will be explained below.
What is a database?
Before learning about the various types it’s important it know what a database is first. Take the library example into account, a library is filled with books, as it stores books, it is a container for books, so you could say that the library is a database for books.
But the quite literal definition of it is that it is a computer structure that can save, organize, deliver and protect data. A system that contains a database is called a database management system, or a DBM. Most of the databases have multiple tables, and different fields and so on.
Each database will work differently based on what type of database it is. Most of the time it works through diagrams. On the down-low there are three key components that work with the management systems, the clients, the server and the database. Firstly there will be the external view which will be the clients, they will have a grasp of the conceptual logical layer which is the second layer in this diagram, the third layer being the physical diagram, which will work with the database. Simply this is how it will work, but there can be more clients and more paths that have to be added in and so on.
Types of databases
There are tons of data bases, but there are 4 main types of databases explained, these ones are the more popular ones. It can range from simple to difficult, overall you can use anything you prefer.
The first type is the text database, this is the simplest one yet so far, it is only about the text. The data can be organized into text files through rows, and columns. These text files can be used to store, protect, organize, and retrieve the data. One of the simplest things that can be done on this database it so save a list of names. Saving the first name followed by the last name will be filed in rows. Each row will represent a record, you can update these, change them, delete them and so on. Basically it is just text manipulation.
The second type is desktop database program, this is a more complex, however it can be used for single users. On this you can enter data, store it and protect it, and retrieve it as well. This is better than text based because it is you can change the data faster, and larger quantities of data can be stored. Some examples are Microsoft Excel spreadsheet, and Access.
The third is Relational Database, these are actually the most common types of database systems. This database is great because it does a great job of managing the performance, as the features are way better than the other two databases. On this there can be multiple users to work with the data at the same time. It also has advanced security systems to access the data, so not everybody can just come up and work with it. On this database the data is stored in columns and rows, which turns into tables. And a set of these table are considered as a schema, and a number of these will create a database. So this means that many databases can be created on a single server.
Finally the last type is the NoSQL, and the Objected-oriented databases. These are completely different from the others, it does not follow in a row, column, and table format. Instead bookshelves are built, the data is stored in these bookshelves and it allows access per bookshelf. This basically means that it will direct you to a bookshelf depending on the data you want. It will narrow down the database in order to see what you want. This methods is a great way for you to store in chunks.
The benefits and drawbacks of databases
Database are good to use because there are many benefits that you can gain out of it, like for the fact that it reduces the data redundancy, as you have places to store them now. It also reduces the updating errors this is good because not only do you have less errors, but it also increases the efficiency and consistency of the business. For example, the popular hookup site Fuck Book utilizes a lot of various data to match partners and employs databases to keep the application functioning smoothly. Furthermore there is a greater improved security, so it will be difficult for people to take the data.
However these systems can be complicated to work with and will take too much time if you’re trying to figure it out when you are working. Not only that but there will be hardware and software costs that you have to deal with. Also the cost can increase based on the conversion of the files, and the training that needs to be provided of working with databases regularly.
Big Data is big news these days. There is continuous streaming of Data, flooding each business on a given day. This Data consists of information that is collected from a huge number of sources, such as, Production, Market Prices, Finance, Material Flow, Supply Chain, Security Data and so on. These huge masses of Data may be Structured and Unstructured. But it is not the quantity of Data that directly interests the Data Scientists or the organizations that generate and collect them. It is what this Data can yield which may lead to better insights and strategic decisions for the whole organization. What follows next is therefore a brief Introduction To Big Data, its workings and its capabilities.
A Little History
Big Data as a fore-runner of a new branch of knowledge called Data Science, first gained currency in the early 2000s. In 2001, Industry Analyst Doug Laney first coined the term ‘Big Data’ and the ‘3Vs’ of Volume, Velocity and Variety. These 3Vs now form the core definition of Big Data.
How Does it Work?
Big Data is said to consist of the 3Vs standing for Volume, Velocity and Variety. Let us discuss each V first, as follows:
- Volume: Data is streamed from various sources and storage of this mass of information had become a major problem. Data is collected, by the organizations, from Industrial Equipment, Videos, Social Media, Business Transactions, Smart IoT (Internet of Things) Devices and many more. But modern storage that is at once cheap and capable of absorbing vast quantities of Data, like, Data Lakes and Hadoop, have made such storage a possibility at last.
- Velocity: The Velocity of Data streaming has suddenly entered a phase of rapid upsurge, riding on the back of the IoT. These speeds were truly un-thought of previously. With Censors, Smart Meters and RFID Tags pushing the boundaries of collecting masses of Data in real time, the need for rapid collection of Data is foremost.
- Variety: Data these days stream in with a vast Variety of formats. Traditional Data Bases providing Structured Data are more easily absorbed, but Unstructured Data, such as, Ticker Tape Data, Stock Figures, Audios, Videos, Emails and Financial Transactions create a mixed bag that needs special storage.
Two other Vs are also selected as important for Big Data. These are:
- Variability: The Data itself is often changeable and sometimes seasonal. In order to predict Trending, on a daily basis, Social Media and other such sources of Data generation need to be considered on Variable platform.
- Veracity: This is an important feature which evaluates the quality of the Data. In order to confirm the Veracity or Truth about the quality of Data received and it usefulness, businesses must be able to figure out relationships and linkages of Data on a mass scale. Otherwise overall control of Big Data may soon be lost.
Impact and Importance
In Big Data, the quantity of Data collected is less important than how the Big Data is handled and what it is used for. Analysis of Data can enable the Data Analyst to guide organizations to produce, firstly, Cost Reductions and Time Reductions. New products can be developed and optimized. Decision making thus becomes smart. Powerful Analytical Tools used by Data Scientists can yield insights on the following:
- Root causes of failures and defects can be determined in close to real time.
- The entire Risk Portfolio can be re-calculated in a matter of minutes.
- Fraudulent functioning can be detected before the organization is affected, or at its early stages.
The Future of Big Data
The future belongs to Big Data and DI (Data Integration). With so many different types and sources of Data, with operational time varying from Real Time to Streaming Time, Data Integration uses Data Science to extract meaningful insights by mining Big Data. This Introduction To Big Data is only a brief glimpse of how our future is being built by Big Data and Analytical Strategy. A future can be conceived where Cloud, Containers and On-demand Computation Power can create a situation, where organizations can depend fully on the reliability of the Big Data-driven decisions across lines of business. Big Data is our Business’s staircase to a Big Future.
There are many people who are interested in land in the field of data science. It is an easy-to-read subject that looks interesting for many graduate students who come from a wide range of backgrounds. To deep dive into the world of data science the candidates need to master the basic concepts of data science from a reputed institution that possess skilled and trained instructors. To gain more knowledge read this article as it covers everything you need to know right from the introduction to data science and the true benefits of learning this skill.
Data science is related to that form of study from where the information comes and how it is represented.
What are the basics of Data Science?
The data science field thus includes disciplines such as computer science, statistics, and, mathematics and also incorporates techniques like visualization, group analysis, data mining, and machine learning. Data science is that discipline, which encompasses statistics and related branches of mathematics, machine learning and other analytic processes, that increase borrows from high-performance scientific computing, to extract future insights from data to address new information or data.
What is the need to learn Data Science?
If you aspire to become a Data Scientist it is one of the essential knowledge which you need to master in order to execute the tasks using its skills to tackle real-world data analysis issues and challenges. Data Science is also related to machine learning which helps the participants to build up various skills that help the users to learn other programming essentials so that they can stay ahead of their competitors.
Not only this if the individual wishes to become a data scientist then they need to make a positive contribution to bring out the changes to help the society. Data science is sure to offer you attractive superpowers that are beyond once imagination. One of the major concerns right now is to restructure the industries in the field of healthcare as many people with inadequate facilities in rural areas are losing their lives.
Data scientists should also possess an amalgamation of statistical skills, data mining, machine learning, and, analytic, and hold experience in coding and algorithms. In addition to manage and interpret large data, most of the data scientists are skilled to handle the tasks that include creating a model for data visualization to help demonstrate the digital information’s business value.
Data scientists are required to draw digital information from various sources. They are required to utilize the list of growing channels that include social media, electronic gadgets such as smartphones, and internet of things (IoT) devices, surveys, purchases, internet behavior and searches.
I hope you got an idea of how data science is essential to face real-world problems. Many data scientists are thus working hard to recognize patterns that will bring solutions to varied issues by way of data mining. At present Data Science is used to reach company goals across a number of industries from agriculture to dating apps like tinder and banking institutions are utilizing mining data to boost fraud detection which helps refine and identify the right audiences.