Machine Learning Engineer Career Path in 2023 | Machine Learning Tutorial | Edureka Rewind - 6

hello everyone this is aniruth from edureka I welcome you all to this session on one of the most interesting career paths you can take yes guys I am talking about machine learning I'm sure you guys are curious about how you can become a machine learning engineer too right so guys let's get started well let me quickly walk you through the agenda for this session we'll Begin by quickly understanding what machine learning in exist right so later we'll move on to understanding what a machine learning engineer does next I'll walk you through all the various roles responsibilities and skills needed to become a machine learning engineer we'll end the session by checking out the salaries and the market Transfer Machine learning so we can make sure you can start your learning as soon as possible Right so let's begin by quickly walking you guys through a bit of machine learning well guys in my opinion machine learning is one of the most exciting and the recent Technologies there is you probably use it dozens of times a day even without noticing it right and you're wondering how guys you tell me right so let's keep this session interactive head to the live chat and just put in your thoughts on how you can think machine learning is being used around you right now but there are two major things that came into my mind as soon as I thought of machine learning well YouTube recommendations and Facebook image recognition well with YouTube let's say you're watching edureka's newly launched Python tutorial video as soon as that's done you probably get the statistics for data science using python video as a recommendation correct so how does YouTube know what it should recommend to you well guys it's really complex what YouTube does but it analyzes everything from what you've watched previously towards the keywords in the video that you have watched right so this is really amazing so similarly consider this you and your friends went on a vacation or something you clicked a lot of pictures and you want to upload them on Facebook and you did but now wouldn't it take so much time just to find your friends faces and tag them in each and every picture well Facebook is intelligent enough to actually tag people for you and I really like this well Machine learning has been so subtly integrated into our lives so much already that we do not even know its presence right so machine learning is basically a type of artificial intelligence itself as you can see from this picture deep learning and machine learning Branch out from artificial intelligence machine learning is the subset of artificial intelligence and deep learning is a subset of machine learning it's as simple as that guys so to sorry so to sum it up machine learning provides computers with an ability to learn well not just the ability to learn but the ability to learn without being explicitly programmed at all so next up let's quickly check out how machine learning actually works so guys it is pretty simple let me explain first we have some training data it can be anything that acts as a data set right so consider for example a set of images of cats and dogs and where you want the machine to tell you which is a cat and which image is of a dog you're getting it right so once the data set is established we train the algorithm iteratively by providing it the input and teaching it to attain better accuracy and next up would be to actually model the input data and by this step the machine is already trained will provide new input data as well and we let the algorithm check if it's similar to our existing data by making comparisons and eventually by this we'll be making predictions based on the same if the predictions are correct then a model was really successful in performing this task that is comparison for us however if it failed then the input it doesn't match the data set enough or something is different or it might need more training right so that's about it guys an overview of machine learning workflow so it is at this point that I would like you to sorry so so guys at this point I would like to tell you that we have a huge playlist with some really good videos on the channel so make sure you check them out for all of these in-depth tutorials we have after this session so next up we need to look at generalization so what do I mean by generalization well check this out guys what will happen when we do not provide a proper input to our model will it break will everything be fine well not to panic guys by generalization we make sure to produce a reasonable output even for the inputs the model that has never seen before so we will not end up with an error case for most of the time but we will be providing an actual reasonable output let's look at an example for some better Clarity right well who here doesn't watch TV shows I am sure Netflix just reminds us all of a tub of popcorn and the weekend but did you know Netflix has so many complex algorithms that they use well everything from suggestions to automatic content checking and all of that so here I have a case for you guys it all starts out with a film crew providing us with a data set which eventually gets turned into a movie or a TV show right so let's generalize and call it content and this content is encoded into its respective format and the inspections which are needed for the same are done automatically and by automatic I mean yes by machines and not by humans case so here is where our machine learning steps in and does an automatic screening of this content for us if it passes then the content is said to be as pronounced if the model attains a fail State then intervention by manual quality control is done and lastly it goes live on the Netflix site well here I just simplified a very complex process for you guys but it is straightforward man machine learning is really easy and I'm sure each and every one of you guys here can grasp all the concepts that are needed to learn so now that we've established a foothold on machine learning let's begin to understand who a machine learning engineer actually is so who exactly is a machine learning engineer guys guys what does com what comes into your mind when you think of a machine learning engineer right head to the live chat and let me know well so for now here is what we can learn machine learning Engineers are sophisticated programmers who develop machines and systems that can learn and apply some knowledge without any specific Direction let's simplify that right so this is enthusiastic computer science programmers but their focus goes beyond specifically programming machines but to program specific tasks they create programs that will enable machines to take actions without being specifically directed to perform these tasks well now that that's done what is the goal of a machine learning engineer well to put it in the simplest terms it is to achieve artificial intelligence guys well everything else is a subset of this actually if you ask my opinion so now let's talk about your goals so whenever I actually give my sessions I always get a lot of questions afterwards from developers who want to get started in machine learning but feel stuck well usually the only thing that is holding them back is a self-limiting belief guys so they come to me saying that their machine is not good enough to do this or they're just a student they're not a very good programmer or they're just too busy with their work and all of that well guys these are definitely self-limiting reasons right well take on small things and do not be overwhelmed with all the concepts of machine learning it is really simple so for now we've established the goals of a machine learning engineer and a learner as well so now the question is what does a machine learning engineer do well we already know that the data science team is full of ideas right you have to make sure that no technology is limiting them as good and customizable as the current ml Frameworks are sooner or later your teammates will actually run out of the cases because you're not able to achieve any machine learning with them right well not with the standard apis at least but let's say when you dig into their internals tweak them a little and mix in a library or two right so you mix two or more libraries so you basically you'll be abusing the Frameworks and you'll be using them to their fullest potentials guys this requires both extensive programming machine learning knowledge something that is quite unique in your role to the team correct and even when the framework provides all of the pro all of the programming wise there still might be some issues with the lack of computation power well large neural networks take a large amount of time to train this precious time could be reduced by an order of a large magnitude if you used GPU Frameworks running on powerful machines correct well to be honest with you you are the one to scout the possibilities to see the pros and the cons of various Cloud options and to choose the most suited one so machine learning engineer is different from a data scientist I am sure you guys will agree but you're wondering how right so let us check out what they do so when a company or an organization has an issue or question that they need to solve by gathering the data they hire a data scientist these professionals meet with the stakeholders and the leaders to study the economic efficiency and the customer goals as well and while using this information so what data scientists usually do is they actually develop computer programs using Java or any other language of their choice software providing complex algorithms is basically to help these business savvy techs find some pattern and a large data set they have right so this data is then used to know more about the viewership customer engagement sales workflow any other issues well here are four job responsibilities of a data scientist removing errors from data set to avoid skewed results looking for only the pertinent numbers analyzing the data using statistical methods and writing a report the stakeholders can use inform changes creating graphs charts and other visual displays of the data well Machine learning Engineers are creators of these algorithms that allow a machine to find pattern in its own programming data teaching it to understand commands and even think of itself right so think about it guys the artificial intelligence that is seen in vacuum cleaners and self-driving cars are the thought process of all of these Engineers working well here are some of the highlights from the machine learning Engineers job well researching a new technology and implementing them in the machine learning programs obviously finding the best design and Hardware to use when building the robot or say a computer as well developing tangible prototypes to show stakeholders it can also be putting the machines through various tests to ensure that they function as planned at all times right so these are some of the major comparison factors between data scientists and machine learning engineers so next up we need to check out the roles and responsibilities of a machine learning engineer it's pretty straightforward so let's check it out so guys we've already discussed the roles here but everything here is generalized in just three steps the first and the most important role is to actually create artificial intelligence products for the team to use you agree right and secondly this is achieved only when you're able to create machine learning models of your own what's more important is that we need to build efficient applications right so just not building applications but building efficient applications the efficiency here definitely plays a really big role guys and now let's quickly look at some of the responsibilities starting out we need to be able to study some of the prototypes and then transform them into applications we have to be able to design and build our own machine learning systems at any point of time based on the requirement right we have to be in a position where we put in some research to find appropriate algorithms and the tools necessary for production and yes we'll be developing machine learning application based on what's required correct also what's important is to select the right data set and to find correct data representation models we also need to learn machine learning tests and experiments and we need to keep experimenting on these Concepts to help improve all of our accuracies and the implementation for our particular use cases and lastly we need to train the systems for top-notch accuracy but sometimes so you'll have to retain them again based on the changes in the requirement right so guys let's move on to finding what are the skills needed to become a machine learning engineer I am sure everyone here is curious of this right it's pretty simple so let me begin well firstly it has to be the fundamentals and the programming skills you will request some basic knowledge on data structures such as Stacks uh queues multi-dimensional arrays and uh what else even trees and graphs and some of the basic algorithms like searching sorting optimization and even dynamic programming to certain extent you will need to know a little bit about memory Concepts such as a bandwidth cage Deadlocks and all of these as well and second we have probability and statistics well here as well guys some Basics and conditional probability the concept of Independence and all of that is needed well Machine learning will require a few techniques such as base Nets hidden Markov models and all of these Concepts and then the statistics is really simple right so think about it it's all about mean median variants and all of that even distributions like say normal distribution binomial distributions and what else so yeah poison distribution and even uniform distribution correct so all of these are actually needed guys and do not worry this is extremely simple to learn so next up we have data modeling and evaluation as well right so data modeling is the process of estimating and underlying structure of a given data set and the goal here is to actually find useful patterns such as correlations and clusters a key part of this estimation process is to continuously evaluate how good our model is and depending on the task at hand you will actually need to choose an appropriate accuracy measure so let's say for classification we have log loss and we have something called as SSE that is a sum of squared errors for regression and all of that and the next one will be applying machine learning algorithms and libraries so you already know right we have a lot of packages we have a lot of libraries and apis like Sky kit learn thiano and tensorflow but applying them effectively involves actually choosing a suitable model a learning procedure to fit the data and understanding hyper parameters and all that well next up is software engineering guys so this is really important at the end of the day us machine learning Engineers typical output or delivery or deliverable product is software right and often it is a small component that fits into a larger ecosystem of products and services you need to understand how these different pieces work together and fit together right so they basically communicate with each other and these are used to build appropriate interfaces for your component that the other companies and the other teams will depend on you for careful system design may be necessary to avoid bottlenecks and let your algorithms scale well with increasing volumes of data while software engineering includes some of the best practices right so let's say there is requirement analysis or system Design This testing there are documentations and all of that right so why are these important well these are important for good production and collaboration with good quality and good maintainability and all of these so this really plays a high role in learning to become a machine learning engineer guys and now that we know all about machine learning we need to see where there is a requirement for us machine learning Engineers correct so there are many companies hiring and I just couldn't fit it all here the number of opportunities are exponentially growing and this is amazing because you'll be trending and you'll become a machine learning engineer and you'll be really paid well as well well I will come back to salaries in just a few minutes and it is definitely mind-blowing well everyone from Amazon Google Walmart Labs Apple Facebook Uber Netflix plan Twitter Salesforce all of these guys are hiring on a constant basis and they're ready to pay really good salaries for this case so next I'm sure you have these questions too as well right so what is the future of machine learning well I personally remember asking many questions back when I was actually starting out with machine learning so what is perhaps the most compelling about machine learning is its seamless and Limitless applicability correct well there are already so many fields that are being impacted by Machine learning including education Finance computer science and so much more again I couldn't fit all of these here on one slide guys well there are virtually no fields to which machine learning doesn't apply at all in some cases well Machine learning techniques are in fact desperately needed we'll consider Health case sorry consider health care right so Healthcare is an obvious example and the world is unquestionably changing in Rapid and dramatic ways do you guys agree and the demand for machine learning Engineers is going to keep increasing exponentially as I've kept on mentioning the world's challenges are complex and they will require some complex systems to solve them and we machine learning Engineers are here to build these systems right now so if this is your future and there's no time like present to start actually mastering the skills and developing the mindset you're going to need to succeed guys well Machine learning is one amazing thing in a bubble period so yes salaries entrance you guys were really curious so here it is so guys as a fresher there is a median salary of almost 13 lakhs and Rising for a machine learning engineer this is one of the trendiest and the coolest jobs to have as per a survey conducted earlier this year and a machine learning engineer in the USA gets an annual pay of about 140 000 it's about fifty thousand pounds in the United Kingdom and it's and it's about 13 lakhs in rupees in India well this definitely is a lot of money in my opinion and the opportunities are endless well look at this trend chart I have for you guys well it keeps going up and up your value as a machine learning engineer will keep on increasing and you can make a lot of money being a machine learning engineer as I've kept on mentioning so that is it for the small session guys I hope you took away some real good points from here and that this helped you in your machine learning path