Introduction To Machine Learning| Types Of Machine Learning

What Is Machine Learning

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Table OF Content

  1. What is Machine Learning?
  2. The Aim Of Machine Learning
  3. Why Choose Machine Learning?
  4. Future of Machine Learning
  5. Machine Learning Engineers Earning Pretty Penny
  6. Jobs On The Rise
  7. Machine Learning Job Positions
  8. Linked Directly To Data Science
  9. Types Of Machine Learning Algorithms
  10. Supervised Learning
  11. Unsupervised Learning
  12. Reinforcement Learning
  13. What you will learn in Machine Learning?
  14. Best Programming Language For Machine Learning
  15. Python
  16. C++
  17. Java and JavaScript
  18. R Programming
  19. Pay Scale Based on Machine Learning Job Profile
  20. Scope of Machine Learning

What Is Machine Learning?

Currently being one of the most preferred and hottest topics in software development, Machine Learning (ML) offers a world of new possibilities for developers, application owners, and the end users. ML technology has been invented to improve the features and performance of an application while changing the experience of using it in many different ways, such as from personalization to smarter suggestions, optimised search functions to artificial intelligence assistance, and making applications that are able to see, hear and react just like humans.

Normally, Machine Learning is the most amazing application of the AI (Artificial Intelligence) mainly provides the high-level systems having the ability for learning automatically and improving from the better experience even without explicitly as programmed. Machine Learning mainly enabled with the high-end developments of the Computer program which could mainly be accessed to use accordingly. Writing the code along with feeding data in generic algorithm also becomes much more efficient to build the logic with the high-end data. Normally, the process of leading mainly begins with the observation of the data that includes the direct experience, instruction and many others for enabling the pattern the data for making the better decision and allows the computer to easily learn it automatically without any hassle. When you like to know What is Machine Learning then here is the best solution.

For example, ML algorithm is analysing the video that you are watching in an application. With each video played, the algorithm keeps a check whether you are watching the whole video or skipping to the next one on the list. Meanwhile, the ML algorithm will be able to predict which new videos you are likely to watch and then make a recommendation on that basis. If these recommendations are good matches your choice, then you are likely to continue using the application and recommend it to your friends as well, which increases the number of application downloads for the owner. Programming language for Machine learning

The Aim Of Machine Learning:

Aim of Machine Learning

Primary Aim of the Machine Learning is to allow the computer to easily learn about the process automatically when without the human intervention or the assistance with adjusting the action in a much more efficient manner. Supervised Machine Learning algorithms could mainly be useful for applying and learning about the past new data with the labeled example for predicting future events. The complete start of the analysis is known with the datasets along with better functionality. Learning algorithm extensively produces the inferred functioning with making the right prediction on the output values. The main system is to offer better targets with the right input that is sufficient for the training in a more efficient manner. Learning Algorithm could also be useful for comparing the output in much more correct as well as intended output to easily find the errors as well as modify models accordingly. One of the biggest question that most of the people have in their mind is what is machine learning language along with the types that are involved in it.

Why Choose Machine Learning?

Better Career Opportunities:

Why Choose Machine Learning

According to the recent report on Tractica, the AI driven services mainly become worth of more than $1.9 billion in 2016 but it is mainly to be anticipated to increase about $2.7 billion in 2017 and mostly 23% of revenue mainly comes with the high end machine learning technology. TMR report states that Machine learning as a Service (MLaaS) have been expected to grow more than $19.9 billion at the end of 2025. With more number of industry are looking to apply the AI based on the domain, the studying machine learning has abundantly opened the new world of opportunities with the high-end machine learning application. Most of the machine learning companies have been on the verge of hiring the skilled ML engineers as well as become behind business intelligence. Know more about the machine learning examples here that includes more process.

Future of Machine Learning

Being one of the hottest IT skills to learn currently, Machine Learning is on every professional’s as well as employer’s priority list. As Per the recent report from Gartner, it is expected that by 2020, 2.3 million Machine Learning jobs will be created by AI. The future of machine learning is very promising and diversified since a ML scientist discover methods that helps in product demand, predicting product suggestions and also in exploring Big Data to automatically extract patterns. Apart from ML Scientists and ML Engineers job roles, Machine Learning Jobs that companies offer include positions like ML Analyst, NLP Data Scientist, and Data Scientists Lead.

Machine learning salary in India is as per the market standards where fresher’s earn between Rs. 699,807 to Rs. 891,326 annually. Those professionals who hold a complete knowledge of data analysis and algorithms along with a few years of experience in the same field can expect a salary of Rs. 9,00,000 per annum. The demand for ML experts is at an all-time high with its application from smartphones to chatbots, which makes it one of the fastest growing fields in the IT industry. Now, let’s focus on the job types, salary and future career prospects that Machine Learning holds for its learners that will help them in getting an edge over their competitors in the market.

World-class machine learning experts mainly related to more number of NFL quarterback prospect. Based on the recent report, Average Machine Learning Engineers salary is based on $142,000. Normally, the experienced machine learning engineer could easily earn up to $195, 752. Studying the best machine learning course lets you to become expert in the field.

Machine Learning Engineers Earning Pretty Penny:

World-class machine learning experts mainly related to more number of NFL quarterback prospect. Based on the recent report, Average Machine Learning Engineers salary is based on $142,000. Normally, the experienced machine learning engineer could easily earn up to $195, 752. Studying the best machine learning courselets you to become expert in the field.

Jobs On The Rise:

Most of the hiring is completely based on the top tech companies with the search of the especially experienced machine learning engineers to build the machine learning algorithms in a more excellent manner. Normally, the job market mainly has the machine learning engineers are quite sizzling. With the machine learning introduction, the number of people has been learning the new algorithm which gives you more benefits to the maximum.

Machine Learning Job Positions

The most common job positions an ML expert is hired for are ML Scientists and ML Engineers both in India and abroad. The future of machine learning job roles include Data architect, Data Scientist, Data Mining Specialist, Cloud Architects and Cyber Security Analysts among many others. Let us go through these various ML job profiles in details and get a brief about it.

  • Machine Learning Engineer: An ML Engineer develops amazing algorithms that are useful in decoding meaningful patterns from humongous amounts of data. The main focus of ML Engineers is on programming languages like Python, Java, Scala, C++ and JavaScript. They are capable of constructing highly-scalable distributed systems and work in teams that aims at personalization. They are required to design and implement ML applications/algorithms, such as clustering, anomaly detection, etc.
  • Data Engineer/Data Architect: An organization’s big data ecosystem is entrusted in the hands of Data Engineers or Architects. They hold a strong foundation in programming and are familiar with Hadoop, Data Streaming,MapReduce, MongoDB, Hive, NoSQL, SQL, MySQL, Cassandra, and Programming. They are also very proficient in programming languages like R, Python, C++, Java, etc. Development, construction, testing and maintenance of highly scalable data management systems is solely performed by data infrastructure engineers. Data engineers are also responsible for collecting and storing data, do real time or batch processing and then pass it to data scientists for analysis via an API. They are also able to develop custom analytics applications and software components.
  • Data Scientist: Data scientists are masters of R, SAS, MatLab, SQL, Python, Hive, Pig and Spark. With proficiency in Big Data technologies and analytical tools, they sift through large amounts of unstructured data with the help of coding in order to derive insights and help form future strategies. They also clean, manage, and structure big data from disparate sources.
  • Data Analyst: Data analysts or data miners usually possess a strong background in statistics, machine learning, mathematics, and programming. Because of such high qualifications, many organizations expect data analysts to know about data retrieval and strong systems, data visualization and data warehousing using ETL tools, Hadoop based analytics and concepts on business intelligence.

Linked Directly To Data Science:

Machine learning is considered as a Shadow of Data Science. The Machine Learning career mainly endowed with the two hats that include the machine learning engineer job as well as data scientist job. In fact, you could conveniently analyze the data by extracting the value and also glean insight in enabling more aspects.

Types Of Machine Learning Algorithms:

Types Of Machine Learning Algorithms

With the use of Machine Learning, no shortage of machine learning algorithms could be seen. It especially ranges from fairly simple into higher complex aspects.

Supervised Learning:

Supervised Machine Learning Algorithms is mainly suitable for applying the learned aspects of the past and setting the new data with labeled examples for predicting future events. With the best option for starting the analysis in much more secure aspects. With high-end aspects of the system to provide the targets for new input is more sufficient for the training to the maximum. with the better learning algorithm, it is also much more compare the output and intended output without any errors for modifying the models in a more efficient manner.

Unsupervised Learning:

Unsupervised Learning Process mainly learns with better observation along with finding the structures of data. Model is given at the dataset and automatically finds the patterns as well as relationships on the dataset with extensively creating the cluster on it.

Reinforcement Learning:

The Reinforcement Learning Process is the ability of the agent for the easily interacting environment along with finding out a better outcome. Class of machine learning algorithm mainly enabled with identifying the correlation.

What you will learn in Machine Learning?

As a part of Artificial Intelligence (AI), Machine Learning makes computers able enough to learn from data and thereby improve its performance progressively on specific tasks without depending on rules-based programming. Machine learning algorithms makes future decisions on the basis the natural patterns which they find within the data. The steps by which ML improves an application with the help of data and which can be learned in a Machine Learning training are explained below-

  1. Get Data
  2. Clean, Prepare  Manipulate Data
  3. Train Model
  4. Test Data
  5. Improvisation.

Best Programming Language For Machine Learning

Programming language for Machine learning

For beginners, the best language for machine learning is the one which has a good machine learning libraries along with a good run time performance, great community support and a pool of healthy supporting packages. As ML is gaining rapid importance, almost every mainstream language is extending support to make ML development tasks an easy process. So, there are many machine learning programming languages to choose from, but we have made the task easy for you by listing some of the most popular and the best programming languages, which are on every developers and app owners list.

1. Python

Python, which is an open source, high level, general purpose programming language is the best programming language for machine learning of recent times, which was created by Guido van Rossum in the year 1991. Termed as a dynamic programming language, Python supports object-oriented, imperative, functional and procedural development paradigms. It is one of the first programming languages to get the support of machine learning through a variety of libraries and tools, such as Scikit and TensorFlow, which are the two most popular machine learning libraries available to Python developers. Python is known for its concise and easily readable code.

2. C++

C++ being one of the oldest and top machine learning languages is used by the majority of the machine learning platforms including Tensor Flow. All the mechanisms required to construct and execute a data flow graph is provided by Tensor Flow’s C++ API. This API has been designed to be simple and concise where graph operations can be clearly expressed by using a functional construction style, which includes easy specification of names, device placement, etc., presenting a graph which can be efficiently run with desired outputs coming in a few lines of code. C++ is a lower level language, which is easy for the computers to read but hard for human programmers.

3. Java and JavaScript

A James Gosling invention, Java is a compiled language, which means reducing the code to a set of machine specific instructions before it is saved as an executable file. Here, the source code is made to pass through a program called a compiler, which helps in translating the code into a bytecode that are understandable by machines and can execute it. Java is a verbose programming language, which means that Java based applications require more lines of code to perform an operation. Currently, the world is filled with 9 million Java developers. Java Script is popular and best suitable programming language for machine learning. Several machine learning libraries and frameworks support both Java and JavaScript.

4. R Programming

First appeared in 1993, R programming language in the last few years has been widely accepted by data scientists and machine learning developers because of its functional and statistical algorithm features. R programming language is an array based, dynamic, object-oriented, functional, procedural, reflective and imperative computer programming language. R language is compatible with Linux, OS X, and Windows operating systems. The combination of R and TensorFlow let the user work productively by using the high level Keras and Estimator APIs and in case when the user requires more control, it provides full access to the core TensorFlow API.

Choosing the best machine learning language for a given machine learning application completely depends on what a developer wants to build and what problems he is trying to fix. It is true that specific technologies require specific technologies, which will give the developer the solution he is looking for. So, the developer needs to do proper research, outline his project’s goals and consult with experts to make an informed and sound decision.

Pay Scale Based on Machine Learning Job Profile

According to NASSCOM, The Indian Data Analytics Industry is currently worth $2 billion, which is expected to see an eight fold growth to be worth $16 billion by 2025. Fresher’s applying for machine learning jobs can expect a starting salary of Rs. 8 lacs, which may increase up to Rs. 10 to 15 lacs.

The machine learning salary varies from company to company with some top MNCs like Amazon India, Google and Flipkart ready to pay between Rs. 8 to 12 lacs per annum. For a data scientist in India, the median annual salary is expected to be Rs. 670,665. In India, Mumbai leads the race in terms of highest compensation package for machine learning jobs, offering a whopping Rs. 13.3 lacs annually. At the second position comes Bangalore offering Rs. 12.5 lacs and Delhi NCR in the third spot with Rs. 11.80 lacs annually.

Scope of Machine Learning:

The future of machine learning is extremely rewarding. Its demand is going to increase in the future. This unstoppable growth and demand of the ML is simultaneously increasing the demand for learned machine learning professionals whom companies are ready to hire at an exceeding and lucrative pay package. So, an ML expert is at gain from everywhere. Be it work or the pay scale, both will be enjoyed by the ML professional because neither the work is monotonous or tiring nor the pay is low compared to the job profile.

The scope of machine learning makes it a very vast and expanded field. A Bachelor of computer applications, programmer, a mathematics graduate or students with a Master’s Degree in Social Science or Economics can become a Machine Learning professional. A rewarding career in machine learning can only be achieved if an ML expert stays up to date with any changes or updates in the machine learning requirements. Staying updated with amendments infers staying abreast of the latest developments in ML tools like conferences, change log, , etc.; theory and algorithms like research papers, blogs, conference videos, etc.

There are various online communities that would help an ML expert to stay updated with the skill. He or she must read articles on Google File System, Google Map-Reduce, Unreasonable Effectiveness of Data and Google Big Table. There are also other great ways to hone one’s machine learning skills, some of which are hackathons. practice problems and participate in coding competitions and

We are sure by now that the above career details on machine learning have created a big impact on your mind and you definitely want to learn this fascinating and in demand IT skills to make a wealthy future for yourself. So, what are you waiting for? Call SSDN Technologies now and enroll yourself in the best machine learning training and become a certified machine learning expert.


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