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Why you should learn machine Learning?

Artificial Intelligence (AI) is indeed moving tremendously. Its prospects have left several upset, for it’s assumed that job automation portends nice danger to humanity. Arguing concerning the believably or otherwise of this claim is but not the intent of this text. Noteworthy, once mention is formed of AI, the average non-technical person thinks about high-end robots. This is but associate degree oversimplification of a rather broad thought.

It’s safe to mention AI could be a Brobdingnagian tree comprising varied however interlinking branches, among which include Natural Language Processing (NLP) and Machine Learning (ML). So, whereas self-driving cars ar AI applications, so is Siri on your iPhone as well as Youtube’s video recommendations. Being barely dis sociable from knowledge science, ML has particularly gained much attention in the business world. But what is it all about

What’s Machine Learning?

One of the foremost fashionable definitions of machine learning was given by Arthur Samuel in 1959, which considered it a sub field of Computer Science that gives “computers the ability to learn while not being expressly programmed.” This is apt but shouldn’t be taken to mean ML systems are built without any programming effort, or that they acquire knowledge on their own from scratch.

Instead, milliliter systems ar created to make upon already nonheritable data. This makes them perform approach higher than programs designed with hardcoded rules. For example, besides being a pain, a program designed to detect cats in pictures would be quite ineffective if built by a programmer who manually defined the features of various cat species. Such a program would possible miscarry once round-faced with factors (eg: obstruction, reflections and presence of unaccounted features) that distort pictures and defy pre-determined rules. Using machine learning, all that’s needed is to accumulate massive datasets (tons of cats’ footage, within the on top of case) to coach a model, then optimize results so a program will turn out the simplest results once round-faced with completely new sets of knowledge.

In this case, such program is taken into account to be ‘learning’ from knowledge. There ar 2 major varieties of issues ML Engineers attempt to solve: regression and classification issues. For the sake of simplicity, I won’t go in-depth into these or denote any milliliter algorithmic program. But the purpose is: you create use of ML merchandise daily, maybe while not realizing it.

When looking out Google, you’re interacting with associate degree algorithmic program that has learned (and continues to learn) a way to rank search results supported what’s thought-about to be most vital to your question. Facebook uses ML to suggest new friends to you, Netflix’s movies recommendation feature is built on top of it, Quora uses it to determine the type of questions you’d like to read about, just to mention a few examples.

Why You Should Learn Machine Learning

It’s a big deal: Machine Learning is the rave of the moment. Tons of companies are going all out to hire competent engineers, as ML is gradually becoming the brain behind business intelligence. Through it, businesses ar ready to master consumers’ preferences thereby increasing profits. In 2006, Netflix announced a prize of $1 million to the first person to improve the accuracy of its recommendation system by 10%. The prize is proof of the connection placed on ML and Netflix’s anticipation of considerable profits through a small improvement within the accuracy of its recommendations. It’s closely linked to data science: Just as humans learn from experience, ML systems learn from data. Thus, several ML engineers ar created to wear 2 hats (machine learning engineering and knowledge science) whereas endeavor their daily work , which is arguably a good thing. Recommended for You Webcast, March 13th: a way to Activate High-Value Customers investment language process and Machine Learning As you most likely apprehend, data science is rated as the sexiest job of the 21st century. Learning ML would make you more knowledgeable in data science and thus more attractive in the labor market. To become unwary of the hazards of AI: several things are same concerning AI and whether or not or not it might very snatch jobs. Fortunately, however, data of machine learning might take you a step towards protection from any foreseen dangerous outcome of mass scale AI implementation, because, as of these days, most systems are built by humans. Also, there’s likely to be a positive demand of engineers, come what may.

How to Get Started Gone are the days when ML knowledge used to be an exclusive preserve of Ph.D. researchers and students. Today, you can teach yourself ML without needing to enroll in a University — although a formal education may be quite beneficial. If you aren’t cut out for higher degrees, here are some useful tips to get started with ML. Learn a Programming Language: You NEED to have some programming knowledge under your belt to get started. Python comes in handy, because it’s used in many machine learning projects due to its possession of tons of data science libraries. It’s also relatively easy to learn and comprehend. Get a high-end PC: Chances are you’d make use of only small data sets when starting out. But as time passes by, you might want to delve into more complex projects. To get the best of the learning experience, you should ensure that your PC satisfies certain requirements, including possessing a good enough Random Access Memory (RAM) and storage. Also to play with Deep Learning (an ML algorithm), you’d need high-quality Graphical Processing Units (GPUs). Learn the prerequisites: Machine Learning draws a lot from three areas in Mathematics: Statistics, Linear Algebra and Calculus. If you aren’t comfortable with Math, don’t fret. Many of the things you actually need to learn in order to get started, are quite basic. Read ML Academic Papers: Many ML papers are published regularly, and reading tons of them is a good way to learn new things and keep up with the pace of ML research.

Read ML Academic Papers: Many ML papers are published regularly, and reading tons of them is a good way to learn new things and keep up with the pace of ML research. Learn from Videos: YouTube is your friend. Read Blogs and Follow Online Communities: Follow blogs and online communities that can help fast track the learning process. Reddit’s machine learning channel is a good example of the latter. Practice: Practice makes perfect, they say. So, try your hands at machine learning projects and participate in contests hosted on Kaggle and similar sites. In conclusion, there’s no stopping ML in today’s world.

If you’re forestall to supercharging your career, learning Machine learning may well be the thanks to go.

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massiveprincepirate · 3 years

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Which Career Should I Choose — Hadoop Admin or Spark Developer?

Today’s IT job market is revolving around Big data analytics, 60% of the highest paid jobs direct to Big data careers. However, the job market is ever changing in IT industry and organizations look for well-honed staffs. Hence, if you are looking for a career in Big data, you will be happy to know that Big data market is growing rapidly not only in IT sector but also in banking, marketing, and advertising sectors.

As per the statistics, there will be almost 50,000 vacancies related to Big data are currently available in different business sectors of India. Hadoop is a vast framework covering Hadoop administration and programming areas. It demands skills as Spark developer, Hadoop administration etc and opens up the horizon for a programmer and a non-programmer at the same time. Moreover, whether you are a fresher or experienced, you can step into Big data careers with proper training and certifications.

Which Big Data Career is Suitable for You?

We can answer this question from many angles.

Big data careers can be directed in two main streams –

Hadoop administration

Hadoop programmer

Hadoop administration is open to all in Big data careers. Whether you are a database administrator, non-programmer or a fresher you can explore this area. Moreover, if you are already in Big data careers and well acquainted with Hadoop ecosystem, Hadoop administration will add a feather in your cap. Whereas if you are not familiar with any programming languages like Java, Python, exploring Big data careers in Hadoop programming may be a little challenge for you. However, with proper training and practice, you can flourish Big data careers as a Spark developer easily. If you want to know more specifically what the job responsibilities of a Hadoop admin and a Hadoop programmer keep on reading the next sections. It is always easier to validate your position with the right information and data points.

What does a Hadoop Admin do?

With the increased adoption of Hadoop, there is a huge demand for Hadoop administrators to handle large Hadoop clusters in the organizations. A Hadoop admin performs a strong job role, he acts as the nuts and bolts of the business. A Hadoop admin is not only responsible to administrate manage Hadoop clusters but also manage other resources of the Hadoop ecosystem. His duties involve handling installation and maintenance of Hadoop clusters, performing an unaffected operation of Hadoop clusters, and manage overall performance.

Responsibilities of Hadoop Admin

Installation of Hadoop in Linux environment.

Deploying and maintaining a Hadoop cluster.

Ensuring a Hadoop cluster is up and running all the time

To decide the size of the Hadoop cluster based on the data to be stored in HDFS.

Creating or removing a new node in a cluster environment.

Configuring NameNode and its high availability

Implement and administer Hadoop infrastructure on an ongoing basis.

To deploy new and required hardware and software environments for Hadoop. In addition to that working on expanding existing environments.

Creating Hadoop users including Linux users for different Hadoop ecosystem components and testing the access. Moreover, as a Hadoop administrator, you need to set up Kerberos principals

Performance tuning in Hadoop clusters environment and also for Map Reduce.

Screening of Hadoop cluster performances

Monitoring connectivity and security in the cluster environment.

Managing and reviewing log files.

File system management.

Providing necessary support and maintenance for HDFS.

Performing necessary backup and recovery jobs in Hadoop

Coordinating with the other business teams like infrastructure, network, database, application, and intelligence to ensure high data quality and availability.

Resource management.

Installing operating system and Hadoop updates when required. Furthermore, collaborating with application team for such installations.

As a Hadoop admin working as Point of Contact for Vendor communications.

Troubleshooting

Hence, keeping in mind the above points you must possess the following skills to achieve Big data careers as Hadoop admin.

Required Skills for Hadoop Administration

Hadoop runs on Linux. Hence, you should have excellent working knowledge of LINUX

Good experience in shell scripting

Good understanding of OS levels like process management, memory management, storage management and resource scheduling.

Good hold on configuration management.

Basic knowledge of networking.

Knowledge of automation tools related to installation.

Knowledge of cluster monitoring tools

Programming knowledge of core java is an added advantage but not mandatory.

Good knowledge of networking

Good understanding of Hadoop ecosystem and its components like Pig, Hive, Mahout, etc.

What does a Hadoop Developer do?

Hadoop’s programming part is handled through Map Reduce or Spark. However, Spark is going to replace Map Reduce in near future. Hence, if you want to be a Spark developer, your first and foremost job responsibility should be understanding data. Big data careers are all about handling with the big chunk of data. Hence if you want to stand out as a developer you should understand data and its pattern. Unless you are familiar with data it will be hard for you to get a meaningful insight out of those data chunk. Furthermore, you can foresee the possible results out of those scattered chunks of data.

In a nutshell, as a developer, you need to play with data, transform it programmatically, and decode it without destroying any information hidden in the data. In addition to that, it is all about programming knowledge. You will receive either unstructured or a structured data and after cleaning through various tools will need to process those in the desired format. However, this is not the only job that you have to do as a Spark developer. There are many other jobs to do on daily basis.

Responsibilities of Spark Developer

Loading data using ETL tools from different data platforms into Hadoop platform.

Deciding file format that could be effective for a task.

Understanding the data mapping i.e. Input-output transformations.

Cleaning data through streaming API or user-defined functions based on the business requirements.

Defining Job Flows in Hadoop.

Creating data pipelines to process real-time data. However, this may be streaming and unstructured data.

Scheduling Hadoop jobs.

Maintaining and managing log files.

Hand holding with Hive and HBase for schema operations.

Working on Hive tables to assign schemas.

Deploying HBase clusters managing them.

Working on pig and hive scripts to perform different joins on datasets

Applying different HDFS formats and structure like to speed up analytics. For example Avro, Parquet etc.

Building new Hadoop clusters

Maintaining the privacy and security of Hadoop clusters.

Fine tuning of Hadoop applications.

Troubleshooting and debugging any Hadoop ecosystem at runtime.

Installing, configuring and maintaining enterprise Hadoop environment if required

Required Skills for Spark Developer

From the above-mentioned job responsibilities, you must have gained some overview of required skills you must possess as a Hadoop developer. Let’s look into the list to get a comprehensive idea.

A clear understanding of each component of Hadoop ecosystem like HBase, Pig, Hive, Sqoop, Flume, Oozie, etc.

Knowledge of Java is essential for a Spark developer.

Basic knowledge of Linux and its commands

Excellent analytical and problem-solving skills.

Hands on knowledge of scripting languages like Python or Perl.

Data modeling skills with OLTP and OLAP

Understanding of data and its pattern

Good hands-on experience of java scheduling concepts like concurrency and multi-threading programming.

Knowledge of data visualizations tools like Tableau.

Basic database knowledge of SQL queries and database structures.

Basic knowledge of some ETL tools like Informatica.

Salary Trend in the Market for Hadoop Developer and Administrator

The package does not vary much for different positions in Big Data. The average salary for a Hadoop admin is around $123,000 per year whereas for a Spark developer it could be $110,000. However, salary should not be the prime concern while choosing the Big Data careers. Because with experience it will increase automatically. Moreover, if you obtain a Hadoop certification it will give you an extensive knowledge along with a future scope in your Big data careers with an amazing salary.

Job Trend in the Market for Big data

This is an obvious fact that market demands for developers are more than the administrator in Big data careers. A developer can take over the job of a Hadoop administrator whereas an admin can’t play the role of a developer unless he has adequate programming knowledge. However, with the huge and complex production environment, now companies need dedicated Hadoop administrators.

Conclusion

If you are a programming savy then definitely Spark developer would be an easy transition and right fit for you. However, if you are a software administrator and want to continue to this role then go for Hadoop administration. Finally, the choice is solely up to you and your knack towards the Big Data careers you are looking for your future.

A good Training, Certifications in Big Data and 100% dedication can make anything possible. Remember one day you started from scratch!

Visit for more details — BEST BIGDATA HADOOP TRAINING IN PUNE AND MUMBAI

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