Learning the Machine Learning

First, a little introduction of evolution,
We live in such a technical world where for every task we have machines to perform. People back in decades could not even imagine machines performing various tasks, they had assumed Artificial intelligence as nothing but a fable. Scientists then came up with simple calculators that could perform basic arithmetic operations, this helped scientists to further imagine a better machine that could perform various better tasks. Popular countries including the US, started to bulge invest upon AI field, but due to lack of efficient programming languages, resources, and tools, scientists had to face ‘AI winters‘.

AI winters‘ is the time when the AI specialists found no growth except decrementation in their work, it stayed constantly for few years. After the AI drought, scientists could come up with efficient programming languages and reliable codes.

First most indelible AI factor was IBM’s Deep blue, it was the machine that was implemented with codes that could play chess without any external help, it became famous when it managed to defeat the chess champion in a battle. There the world was promptly introduced by the effectiveness of AI, and since then scientists are constantly coming up with experimental and unique AI’s.

MACHINE LEARNING
Machine learning is a prominent part of Artificial intelligence, the codes of which will make any machine cultivate the power to think on its own, to be able to understand the things by experiencing indifferent things.

As I mentioned earlier about IBM’s deep blue, that is the perfect example of Machine learning. The machine was programmed with the type of codes that could think about the chess moves, plot a strategy against the opponent according to their move and finally to checkmate the opponent. Usually, it is difficult for the average people to master in the game chess, a machine to play and to win over a chess champion is all the matter of effective codes and algorithms.

Why was Machine Learning invented?
Machine learning was important rather necessary, as the advancement in the field of computers grow the necessity of computers to have their own sense of working mind also becomes mandatory. The goal was to create machines that could be pensive about its environment and behave according to the situation without any external input of hint of solution. Computers now does a lot of work more proficiently than humans, with a dash of perfection.

Is studying Machine learning important?
Machine learning is a part of Artificial intelligence, rather a very imperative part. Artificial intelligence will increase its density in the coming days and so its necessity for machine learning. There are now a lot many countries working towards self-thinking machines, for example, self-driving cars are being concentrated by Japan, Human look-alike and human-like behaving robots are on a high demand. We have already met Sophia who is a human look-alike robot who apparently has an eligible card for a country.
The demand for machine learning experts are high, only within the average range experts are packing $146,085 per year. Pretty much pretty sum of money. By opting Machine Learning you could become a data scientist, Machine learning designer, software developer, and such like.

Now that you are informed all about the history of evolution, uses, popularity of Machine learning, let me now tell you how to become one.

Learnbay is an artificial intelligence and data science training institute that offers you the course of machine learning and artificial intelligence in a very less cost.

Why learnbay is the appropriate most institute for AI?

1. It offers popular courses in an easily affordable price. Check out the prices of Artificial Intelligence and Machine learning course in here,
https://www.learnbay.co/data-science-course/artificial-intelligence-certification/

2. The flow of topics in AI course is very interesting,

Python language, for its exceptional intuitive syntax, structured flow of algorithms and standard library that will be used as reliable coding language.
Machine learning, this subject is evident and obvious in Artificial Intelligence course.
Tensor flow for handling deep neural networks and machine learning algorithms.
IBM Watson is taught here, it is very rational of Learnbay to offer knowledge on such important topic. IBM Watson is a brilliant product of Machine learning which behaves according to its experience of environment.
Pytorch
Google cloud platform
Open CV

3. It has best EMI plas. This truly helps the student to afford the best things in best place.well equipped online coaching, generally online classes are considered not very effective but learnbay pays immense concentration even even towards online aspirants.

4. Students will soak the knowledge and on other hand will be certified by the prestigious company IBM.

What Is “Machine Learning” ?

An essential and perhaps even the driving force of AI, machine learning are algorithms that is used to program the computer with an ability to teach itself and also improve its performance of solving problems and performing specific tasks. In order to understand machine learning, it is important that one takes up online data structures course first.

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In essence, it’s all about appropriately analyzing big data. Machine learning is about automatic extraction of data and information and thereby using that information in making predictions, deciphering if the predictions made were correct, and if it turns out to be incorrect, learning from those in order to make more accurate predictions in future.

Amazon, Netflix, Google and many other online platforms use machine learning and data structures and algorithms in Bangalore and around the world to deliver better semantic results that are based on machine learning algorithms which analyzes the user’s searches, purchases, as well as viewing history in order to predict exactly what the user is looking for or is more like to purchase.

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The data and information that these platforms have at their disposals is really massive. It is estimated that more than 4 billion people were using internet to make searches, as in the year 2018. Each second, there’s approximately 40,000 internet searches processed and it equates to about 3.5 billion searches a day, or 1.2 trillion internet searches every year. Every year, the whole humanity is spending about one billion years online. That’s the amount of data that is being gathered every day, and only with the help of machine learning is it possible to process this much data.

 

The best way to learn Artificial Intelligence as a beginner!

The rapid technological transmute is being heavily backed up with AI and ML. Everyday problems are resolved with quick solutions, and these solutions are extract from sources that promise to exceed human intelligence way beyond the threshold. Artificial Intelligence is the new swing in the market which is soon becoming a necessity. AI reduces human effort and has been spreading to numerous sectors. You need to be prepared so that you can take part in this revolution of synthesizing information and providing an appropriate decision.

This answers your query on why to take up AI? However, before speculating further about AI, you need to know what is the best way to start learning AI? Let’s start from the base.

What is AI?

The definition of AI keeps on changing with time. But the core concept of Artificial Intelligence is to build machines that hold the ability to think like humans, even better. AI is a huge concept, it is an umbrella term that is the combination of Artificial Intelligence, Machine Learning and Deep Learning. It is known to be a tool that helps make decisions, based on data (factual information). What it’s yet to achieve is to have social intelligence, creative intelligence and general human intelligence.

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Types of AI :
While there are various kinds of AI, most of them are converged into these three.

  • Machine Learning

  • Neural Networks

  • Deep Learning

How do you get started with AI?
A good way to start learning about AI is to understand what is AI and how is it similar to any human, who performs complex activities throughout the day. Our oral communication is known as AI speech recognition, so the next time you say Hello to Google, you will know! Reading vernacular language is a human gift but an AI is doing so with the help of Natural Language Processing. Recognizing things with the power of sight is nothing unusual to humans however for AI, it is called Computer Vision. Now that you have acquired the basic knowledge of Artificial Intelligence. Let’s find out the pathway to learn Artificial Intelligence.

Road map to learn Artificial Intelligence:

  • The classic way- Free Books/PDF
    Start your journey with free resources from internet. Learn the fundamentals of Artificial Intelligence like algorithms and basics concepts of AI and Machine Learning. Pick up the terminologies and approaches that will help you through the journey. Books like Artificial Intelligence: The Modern Approach, AI Algorithms, Data Structures, and Idioms in Prolog, Lisp, and Java, Simply Logical: Intelligent Reasoning by Examples are books that will get you started with AI.
  • Brush up your math skills-
    The three concepts that will wrap your math in AI is Knowledge about these concepts is a must if you want to learn AI or Machine Learning. You only need to go deeper into Regression Analysis, and Stochastic Processes if you want to build a career in AI. Go through free websites, online coaching or YouTube tutorials to improve your math knowledge, since maths constitute a greater part of AI techniques.
  • Learn a programming language-
    The key to decode AI is learning programming languages. Python, C, C++, Java and R are the most popular programming languages that people trust upon. There are various websites that provide interactive courses and tutorials that help you learn a programming language easily. Python is the most popular among all as its libraries are better suited for Machine Learning.
  • Befriend a ChatBot-
    Try and create your own ChatBot. ChatBots are the first AI project or automated programmes that you need to master. Input text, sending button and output text are the three major components that are needed to ace the ChatBot game. If you successfully complete your project to build a fully operational ChatBot, you are sorted. Xpath and Regex are excellent tools that will help you with the job. Also, learn everything about ChatBots through The Complete Beginner’s Guide to Chatbots.
  • Learn Machine Learning-
    Get to know Machine Learning theorems and concepts before jumping into projects. Machine learning will provide you with the basic study of scientific algorithms and statistical models that allows computer systems to perform a specific task smoothly without using explicit instructions. You will learn to automate the computer and to learn from its experience, detect a pattern and thus recognize pathways. You can also get to know more about ML through classroom coaching are provided by various institutes.
  • Start to experiment through Open-Source Framework-
    This is the challenging part. To choose an appropriate open source framework is a real task. Open Source is a program in which the source code is available to the general public for use and for modification from its original design free of charge. You can build up your own programmes over it and change its interface. Experimenting over open-source gives you the liberty to work flexibly, speedily and securely.
  • Learn from Free Resources-
    You might want to open up accounts and profiles of free resources that help you learn better. It also helps you access essential learning materials, tools and technologies. You can also go through case studies of various other free programmes and take inspiration. Having access to training and development of latest technologies will help you learn to deploy neutral networks at ease.
  • Attend workshops, seminars and conferences on AI-
    Open yourself to other sources of learning, other than internet and books. These seminars and workshops help you a great deal to know about the latest development in the AI and Machine Learning sector. Broaden your understanding of the field and gain knowledge directly from the experts. Debates and live talks are also beneficial if you want to know better.

These points would get you a little closer to your goal to learn the basics of AI but only regular courses or classes would clear your concepts. Learnbay is one such institution in Bangalore that provides deep learning of Artificial Intelligence and Machine Learning. It is also one of the premium institutions that provides courses on Data Science and Analytics. To know more about AI, and to learn from the experts in the field, enroll today.

Now that you are more learned about how data science training in Bangalore can alter your career, it will be easier for you to opt it as your option,
be a data scientist:
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Is Data science a good career option?

One might say, follow your passion. Do what you enjoy, you will definitely succeed in life. As much as it is true, you need to consider other criteria as well, if you are choosing your career. You are in a crucial stage of your life and you can’t afford any mistakes.
The important three questions that you must ask yourself apart from will it give you happiness is, does the career you are about to choose have a positive outlook and prospect, does it have an income potential in the future and does the career fit your lifestyle?

If you want to have a career in Data science, you might be having a million questions in your head with not many who can you clear your doubts. However, is data science a good career option? According to a popular survey, India needs more than 2 lakh Data Scientists now. It is a combination of business understanding, programming, statistics, mathematics and communication skills, but does learning them continue to provide you with the best opportunities and scopes in future? Let’s explore all your chances to choose Data Science as your career, after graduation.

Why you should choose Data science?
What if you have volumes of encyclopedia filled with interesting facts and information but in Mandarin. Similarly, you might have tons of data accessible to you, but not knowing how to use it makes it useless. If you wish to succeed in life, you need to go with the flow. Data science is a flourishing career option and you must explore it to know if it suits your idea of an ideal career.

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  • The top priority of organisations- Job portals are overflowing with requirements of data scientists or analysts for various industries. Data is everywhere, now they need an expert to tame the data and make it to use. Healthcare, Retail, Media, Banking and Finance, Manufacturing, Agriculture, e-commerce and Chemical, all these industries are now looking for Data Scientist professionals. Almost 77% of the organisations consider data as an integral part of a business and finding niche data scientist experts is their priority of the moment.
  • Building a future- Healthcare, Agriculture and Manufacturing are three fields that constitute the backbone of a country’s economy. They help in building the future of the company. In healthcare, Data Science can improve diagnosis, while in agriculture and manufacturing industry, it would help to make informed decisions. Development of these fields are extremely important to lead the country to a high road. A country can always use such information for good. Choosing a career in Data Science also gives you a chance to choose your industry.
  • Entitlement to choose fields- Data science is an umbrella subject that qualifies you to join any field of your choice, any job profile you want to own. There is a broad range of field to choose as a career, Data Scientist, Data Analyst, Business Intelligence Developer, Metric and Analyst Specialist, Data Architect, Infrastructure Architect, Machine Learning Engineer etc. These fields have immense potential to make you grow in the future.
  • Provides freelancing opportunities- There is a demand for skill-based data scientist and analysts, but choosing a freelancing career in Data Science also gives you an option to diversify your source of income. If you find data science interesting, freelancing is the best option to find the perfect work-life balance. It is one job that can be done from any part of the world. You don’t need a dedicated desk to help a company in the business.
  • Prestigious job- Doctors, Engineers, Teachers and Scientists are always considered prestigious professions. Learning Data Science and getting into specific fields of the industry would gain you the title of being in a prestigious profession. Data Scientists use their skill specific expertise to provide impressive results to their clients, this in returns gives them a respectable position in the company.
  • Highly paid career- Data Science is a highly paid career option. Be a full time professional or a freelancer, you are eligible to get a handsome paycheck at the end of each project. In India alone, as it stands today, data analytics experts are paid on average 50% more than their counterparts in other IT-based professions. This also means that they constitute in being at the top of the game and the priority of not just the best but all organisations.
  • Put an end to arguments and assumption- You as a Data Scientist would help people make informed decisions with your statistics, facts and figures. The world is moving forward and to walk with its pace, you need to support your arguments with proof and evidence. Once you do that, you are putting an end to vague assumptions and move forward the business with informed deals that could bring in better revenue, understand customers and work for the growth of the business.

In order to understand if Data Science is a good career option for you, you need to know the limitations of the profession as well. Every story can be perceived in various ways, here the only cons with data science are:

  • Vague area of science– Data Science is a huge field and it requires a vast amount of domain knowledge to continue and stick to it. It is said that you can have a background knowledge or graduation in any stream, be it IT, social science or humanities, data science can be adopted by anyone. But actually, you need to have a fundamental domain knowledge to work smoothly. Like a data science analyst in the healthcare industry must have some idea about healthcare to prepare a complete report.
  • Scattered information- Still it’s challenging for a person from a different background to know what Data Science is all about. They can gather information from everywhere, but can’t see the complete picture. Though the concept is 7-8 years old, there is not much that you can gather.

However, these problems are present, there are solutions that can be managed through training and tutorials. Learn Bay is one such institution that provides advanced interactive training to improve your skills of Data Science, Machine Learning and AI.
The institutes proffer expert instructors who give online training as well as classroom training to aspiring Data scientists.

Now that you are more learned about how data science can alter your career, it will be easier for you to opt it as your option,
be a data scientist:
https://www.learnbay.in/shop/courses/data-science-training-courses-bangalore/

Will Artificial Intelligence threaten humanity in anytime?

The alarms rang in July 2017 when in a meeting of the National Governors Association, Elon Musk, a prominent celebrated figure pointed out, “I have exposure to the very cutting-edge AI, and I think people should be really concerned about it”. He further said,

“I keep sounding the alarm bell, but until people see robots going down the street killing people, they don’t know how to react, because it seems so ethereal.”

Though Sci-Fi movies like Terminator and Transformers have already taken us through time travel and shown us a glimpse of the future, the facts remain uncertain. Presently innovations and technology are fostering mankind. AI, Machine Learning has a long way to go before we determine the end of humanity. Although we are definitely preparing for an end to the human race with all the climate change and bee extinction, technology is not the one that is leading our way.

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The evolution of Artificial Intelligence:
The rapid advancement in the field of Artificial Intelligence has shaken up the world, where everything is directed towards automation, along with simple, useful solutions. The world needs AI for efficient, error-free operations, smooth performance and majorly to simplify complex actions. It is undeniably the best friend to mankind that solves major societal and health issues. It is justified to be paranoid before a change, but it is ignorant to take a step towards the future that awaits. Every revolution that history has encountered gives us one single lesson, once it starts, it’s inevitable. Change is the only constant and restraining this is not an ideal solution. All that should be done or must be followed is, AI should be developed in a safe and beneficial direction.

Stepping towards the needful:
A dramatic breakthrough with the help of AI in various fields is beneficial for the society, rather than being an existential threat. What is expected further is explained by professor Nicholas Christakis, from the University of Yale in his recent article in the Atlantic naming, ‘How AI will Rewire Us’. While humanity fears mass extinction, as per professor Christakis, “for better and for worse, robots will alter humans’ capacity for altruism, love, and friendship.” However, Christakis statement contradicts to what Ray Kurzweil, one of the best-known AI thinkers as to say. According to him, AI is helping to grow human communication and it will just grow with time. He says that “Hybrid Thinking,” is the next big thing in AI communication. Bridging the gap between perspectives. But what is Hybrid Thinking? It is a combination of human and cyber intelligence. Promoting creativity and science together.

Superintelligence:
AI will soon transform into Superintelligence. Will that transform into something frightening or destructive in the future? We don’t know. That’s not the objective at this point of time. Today, as we speak, the goal is to work towards positive development. Like Human- Machine interaction and collaboration. Your Siri, Google Now, Cortana and Alexa are examples of supreme AI technology. But, developers and experts are working to improve these systems so it detects human reactions. Complex algorithms are inserted to help these superintelligence systems and these are trained to learn through various types of learning. Supervised, unsupervised and reinforced learning could make such systems identify stimulation in their audience.

So, if it is said that AI or Superintelligence can be a threat to humanity, that is not true. Misaligned Intelligence is a real threat that could destroy every living being. Every creation has an objective or a goal. However, is it for the benefit of all?Probably not. A missile has an objective to destroy a colony or community, but to its defence, it is trained to detect heat and demolish. Thus, you must keep your goals aligned.

A Machine is never conscious:
Look around you and ask yourself a question, what is the basic difference that a machine and a human have? Conscious is one thing that machines or robots miss out. The human mind can also be called a machine, but it is conscious. It is related to emotional conduct. Machine consciousness, in general, is different from human consciousness, it is not induced by emotions of love and hate. A machine conscious can be limited to specific trajectories. A self-driving car is aware of its environment and moves along the guidelines. If it struck a person, the person won’t have a thought about subjective conscious. Thus it’s irrelevant to say AI is a risk.

What is expected of AI is to bring productivity out of its super-human intelligence and nothing else. So, we should not be concerned about AI taking over our job opportunities but program AI’s to bring about eventful insights and help in your job and make it easy. It should be developed to enhance and not erase human actions. Not just AI, technology should always have a standard that passes both, safety and security, to avoid accidents and misuse, all at once.

The more important questions:
So, rather than asking questions like if AI is a threat to humanity, there are more important questions that must be asked. What kind of future are you looking up to? A future with no innovation and automation? Will AI fulfil all the criteria of human collaboration? Will it hamper human communication and evoke emotion implicitly? Continue to contribute to the betterment of society or just help in prediction?

Data Science, Machine Learning and AI have become an integral part of our existence, but it would never be the cause of our extinction. It is silly to accuse a robot or a machine that is human trained at the grassroot level. So, our combat is not human versus robots. Our real combat is intelligence vs intelligence. Misaligned human intelligence needs no metal bound structure to bring destruction. Machines will never control humans, as intelligence enables control and the ball is in our court till we imbibe catastrophic goals into the system. As humans, we know better, of course, with conscious.

Sounds interesting? get into the field of Artificial intelligence, check in here,
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Choosing the Right Institute for Data Structure Course

All software engineers and IT professionals don’t necessarily require extensive knowledge of each and every component and aspect of the project that they are involved in. At the same time, it is beneficial and even necessary at time to have a basic understanding of data structures and algorithms. It lets them know how computers really work internally and how the information and data are managed. This information is often taught with data science courses in Bangalore. You may also join an institute and get a data structures course there.

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Here are some tips to help you choose the right institute:

  1. Track record. It is important that you find out how old the institute really is and what position it holds in the market currently. It is not necessary that older institutions always are better. Contact the alumnus of that institute to know the quality of education being provided there. You may even speak to current students there.
  2. Course material. A really great training institute would provide you with the latest and updated course material. The syllabus needs to be updated every session too. If the course material is old, then you wouldn’t be market ready once you have completed the course.
  3. Quality of faculty. Teachers are what distinguishes a classroom setting with data structures course online, else all would have opted for online courses. Mentoring is an aspect that is highly respected and many spend extra money just because they need it. So make sure that the institute has highly-skilled and educated teachers.

Things to Consider While Choosing a Centre For Data Structure Training

While software engineers and IT professionals don’t necessarily require full knowledge of each component of their project, it could be still beneficial for them to have a basic understanding or even an in-depth knowledge of how their computer operates and about data structure and algorithms. For this, it is necessary that they get data structures and algorithms training in Bangalore.

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Here are some points that you must keep in mind while looking for an institute for data structures and algorithms training in Bangalore:

 

  • Track record. How long has a specific institute been operating for, and the position it holds in the market are two of the main factors that needs to be considered while choosing a training institute. You may also contact alumnus and take their reviews to check their track record.
  • Course material. Course material that any institute provides needs to be updated after each session gets over. Also, latest teaching materials should be provided. The online data structures course offers latest course material and learning material. You need to consider the method of teaching as well and see if it meets your needs and preferences.
  • Course material surely isn’t everything. If only course material was sufficient then people would have simply bought books and completed course at home itself. So, when it comes to on-site training institutes, the faculty matters a lot. It is for personal mentoring that people choose institutes for training rather than going for online courses, and this is why it needs to have good lecturers.

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Choosing the Best Institute for Data Structure Course

All IT professionals and software engineers don’t necessarily require to have extensive knowledge in each and every aspects and details of a given projects. At the same time, it is never harmful and sometimes, it is actually useful, to have at least a basic understanding of, take for instance, data structures and algorithms. It allows the engineer to know how the computer works internally and how data is managed. To learn this, one may take an online data structures course or go for a course from a reputed institution. Learning data structure and algorithms can be especially useful for software and application designers.

Here are some basic tips to help you find a good institute for data structures:

Track record

Check at what position or where does the institution stand in the market and long has it been operating. It is important to ascertain that the education and training imparted at the institution is of high-quality. You may consider contacting the alumnus of that institute to get a first-hand review of the place and may also speak to current students.

Course material

At a good-quality training institute, only the latest and updated course material would be provided. The syllabus needs to be updated every new session. Also, you need to check if the course material that they have provided is latest as well. After you complete the course, it is important that are market-ready, with all the latest knowledge about the subject; latest and updates course material will make sure of that.

Difference between Data Science, ML and AI

“The breeze feels lovely today”, said Atul as he sprawled on the couch. He was on one of his weekend visits to my place where we generally hang out over coffee and discuss cool stuff. Atul and I go way back to our college days when he joined year junior to me in Statistics discipline. The senior junior camaraderie grew into a strong thought partnership over the years we knew each other.

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“The weather is just right for a great evening on the balcony”, I quipped handing him over his cup of coffee.

“Ah the coffee is amazing. To add to it, I have got a few questions to pick your brain”, Atul said as he fished out his notebook from his technical training sessions at office.

“Fire away. I’ll do my best. I believe  you have enrolled for data science training in bangalore. How is it going?”

“Raj, I must say, the course is amazing but there are areas that needs a bit of unclogging. For example, what is the difference between ‘Data Science’ and Data Analytics’? Are these interchangeable or one is a subset of another?” said Atul sipping coffee with the gentle spring breeze blowing into our faces?

“Good question Atul. Let’s see. Data Analytics is a branch within Data Science that will enable you answer specific questions related to business portfolios. Data Analytics is practice that leads to insightful dashboard connects various aspects of customer lifecycle”, I said.

“Let’s take an example. If I try find out what are top revenue generating marketing channels for Product X and Y over the last 12 months, I would be performing Analytics on the data to get to the answer. Therefore, any analysis that leads to insights related to comparison and trend over time would fall under data analytics! Isn’t it? “, said Atul

“That is right Atul, Data Analytics helps you look at past data and requires you to perform Descriptive Analysis on it to find out insights.  But remember that insights that are not actionable would be very useful for business as they would just be ‘good to know’ facts. To get past that problem, one must always link the business objective to the overall approach.

“Thanks Raj, that helps. Then there is Machine Learning and AI! How different are these concepts from each other? Are they related to Data Analytics in some way? I mean as a part of Data Science course, would I automatically learn AI or is it supposed be separate branch of study”, Atul fires a barrage of questions.

“Slow down dear friend. Sip in the coffee and let it sink in. It is going to take some time to answer all that!”, I said smiling.

“Based on my experience Atul, Machine Learning can be described as a meeting place of Art and Science. We already discussed that analysing past data can reveal insights that are actionable. However, these insights are often disparate and needs weaving to structure a data story. Let me explain with a Customer Attrition use case. Say, you are interested to know if chances of customer attrition increase with tenure or not, you’d be performing analytics on customer data. You can look at the customer data in multiple ways and study the relationship between Attrition and individual behaviour attributes. All of this will lead to individual yet powerful behaviour insights”, I said taking a pause to finish the remaining coffee.

Placing it on table I lean over the balcony railing and say,” Machine Learning is much beyond that. Like I said earlier it is meeting place of Art and Science where a Data Scientist uses technical and creative skills to create a quantitative framework for not only describing past events but also enable future prediction”

“Raj, that sounds good and perplexing at the same time. Help me out here! Would you mind explaining with an example?”, said Atul his expression giving away his confused state of mind.

“Not at all. In the attrition use case, say we want to link up various observed customer behaviour attributes and compute probability of attrition in the next 6 months. If we embark on such a problem to solve then we would be stepping into the domain of Machine Learning. We usually ask ourselves two fundamental questions at this point: A – Is my framework supposed to predict the likelihood of a certain event or B – Is the framework supposed to calculate a future value of a metric. A lead to a Classification Model and B leads to a Regression Model. The Attrition model is a Classification Problem. Now, among the host of techniques available in the Machine Learning arsenal, there is no one technique that works best all the time. You may have to try out a few algorithms before finalizing the model”,

“In case of Machine Learning, by using various explanatory features, we may leverage algorithms to link to a certain outcome, in this case Customer Attrition. Training the model on voluminous data, the model picks up specific characteristics that maximises the likelihood of differentiating customers who are likely attritors vs those who are less likely to attrite.”

“Makes sense Raj”, said Atul,” I am beginning to piece this puzzle together. Machine Learning essentially creates rules to define the Decision Boundaries. Nevertheless, so far it feels like Machine Learning and AI are not different from each other. Are they interchangeable terms?”

“Well Atul. You can think of ML as a precursor to AI. What it means it that when ML is deployed at a scale which enables algorithms to self-adjust to underlying data to optimize decision boundaries, we achieve AI in our decision systems. AI tries to mimic the way humans learn themselves. Let’s take the Customer Attrition case again. Say for example, in our Attrition model ‘low engagement in the 0-3 months tenure segment’ is highly predictive of attrition in the next 6 months. Now, for the next 9-month timeframe this predictive behaviour held true and so the model predicted accurately. After 9 months, if there is a marketing offer made on the product that lasts for the initial 3-month period, then this model would start under predicting because of the increased engagement from new customers and it does not re-adjust its weights unless retrained. If AI is enabled, then the model would follow and adaptive methodology and readjust its weights as per the changes in the underlying data. It then becomes an autonomous learning system and would not require manual intervention in re-training of the model. Sounds good does it not? I said looking at the sunset over skyscraper filled city horizon as flock of homeward bound birds tweeted goodbye to us.

“Raj, thank you for explaining the differences to me and now I think I am much clearer about how best to place each of these items in a relation to one another. Here let me show you”, Atul said grabbing a pen and drawing the following diagram to summarize our conversation.

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“Excellent Atul! You’ve been following closely indeed. I’d like to add another layer to it if you don’t mind”, I said adding another circle within the AI universe and the following diagram emerged.

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“Okay. Deep Learning huh? What’s that?” asked a curious Atul.

“That’s for our next discussion. Let’s catch up again next week”, I winked, ”Wait for it!”

“Thanks Raj, I must be on my way now. Look forward to our next connect. Till then happy coding to you! Bye for now”, Atul said before leaving for the day.

Difference between Data Analyst And Data Scientist

Data Analysts and Data Scientists both work with information (i.e. data). But ‘what’ and ‘how’ they handle the statistics is the main difference between them. Whereas data analysts and data scientists have a few likenesses, there are a some major characteristic differences between the careers which has to be kept in mind before entering any of these specific fields. Data analysts look at huge informational indexes to distinguish patterns, create outlines, and make visual introductions to enable organizations to settle on more vital choices. Data scientists, then again, plan and build new procedures for information demonstrating and generation utilizing models, calculations, prescient models, and custom analysis.

By and large, listed below are a few major points of differences:

  •   A data scientist is somebody who can foresee the future dependent on past examples though a data analyst is somebody who just gathers significant bits of knowledge from the information.
  •   A data scientist work includes evaluating the ambiguous while a data analyst work includes looking at the identified information from additional points of view.
  •   A data analyst attends to business issues but a data scientist not simply addresses the business issues but rather grabs those issues that will have the most business advantage once they are dealt with.
  •   Data analysts are the person who do the everyday examination stuff yet data scientists have the what’s and uncertainties.
  •   The activity job of a data scientist calls for robust business sharpness and information perception abilities to change the knowledge into a business advantage while a data analyst isn’t relied upon to have business discernment and complex information representation aptitudes.
  •   If a business has questions, the data analyst can provide answers to them using his information collected from the data, whereas data scientist can foresee and verbalize such questions whose answers can provide profits or advancements to the business.
  •   It is not anticipated from the data analyst to have active or first-hand machine learning knowledge or to construct factual models but the primary duty of a data scientist is to construct measurable models and be experienced with machine learning.

Skills requirement/comparison:

Data Analyst

Data Scientist

Math & Statistics

Math & Statistics

Programming languages: Python, R , SQL, HTML, JavaScript

Programming languages: Python, R, SAS, SQL, Pig, Hive, and Scala.

Spreadsheet Tools (Microsoft Excel, Google Sheets, PDF Tables etc.)

Business Expertise & Insights

Data Visualization Tools (Tableau, Raw, Dygraphs, Plotly etc.)

Narrating & Data Visualization

Distributed Processing Frameworks (Hadoop, Giraph, HAMA, Signal/Collect etc.)

Machine Learning

Responsibilities: Data Analyst:

  •   Deciphering information, examining outcomes utilizing the statistical methods.
  •   Creating and executing information analysis, data collection frameworks and other
  • techniques that enhance measurable effectiveness and quality
  •   Obtaining information from crucial or supplementary information sources and keeping up
  • databases Data Scientist:
  •   Constructing and evolving a program or algorithm utilizing machine learning methods.
  •   Achieving new information from the large pre-existing databases.
  •   Enhancing information gathering techniques to incorporate data that is important for
  • building investigative frameworks.
  •   Handling, purifying, and confirming the integrity of information utilized for investigation
  •   Doing quick investigation and displaying the outcome in a neat and clear method.
  • Salary difference:

The normal compensation of an information examiner relies upon what sort of an information investigator one is – financial experts, statistical surveying expert, tasks expert or other. But it is clear that data scientists win altogether more salary than their data analyst partners.

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