Bayesian Machine Learning – Exploring A Paradigm Shift In Statistical Data Modelling

With nearly 74 million US and Canada-based subscribers and 200 million global subscribers, Netflix is the leader in the streaming arena.  Netflix was founded in 1997 as a movie rental service. They used to ship DVDs to customers by mail, and in 2007, they launched their online streaming service. The rest is history. Currently, the company’s market cap is well beyond $200 billion and has come a long way.  What’s the secret behind their phenomenal success?  Some might say they can innovate, while others might say they are successful only because they were the first. However, not many know that the biggest reason behind Netflix’s success is that it started leveraging ML before its competitors did.  Get Best Machine Learning Certifications online from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Program in ML & AI to fast-track your career. But before we talk about how Netflix has been using machine learning to get ahead in the industry, let’s first get ourselves familiar with machine learning:  What Is Machine Learning?  Machine learning refers to the study of computer algorithms that improve automatically through data and experience. They execute tasks and learn from their execution by themselves without requiring human intervention.  Machine learning has numerous applications in our daily lives, such as image recognition, speech recognition, spell-checks, and spam filtering.  Apart from Netflix, there are plenty of other companies and organisations that use machine learning to enhance their operations. These include Amazon, Apple, Google, Facebook, Walmart, etc.   What Things Does Machine Learning Affect In Netflix?  You’d be surprised to know how deep machine learning runs through Netflix’s infrastructure. From user experience to content creation, machine learning has a role to play in nearly every Netflix aspect.  You can find the impact of machine learning in the following areas of Netflix:  Netflix Homepage When you open Netflix, you are first greeted with your homepage, filled with shows you watched and shows Netflix recommends you to watch. Do you know how Netflix determines what shows it should recommend to you?  You guessed it – they use machine learning.  Netflix uses an ML technology called a “recommendation engine” to suggest shows and movies to you and other users. As the name suggests, a recommendation system recommends products and services to users based on available data.  Netflix has one of the world’s most sophisticated recommendation systems. Some of the things their recommendation systems consider to suggest a show to you are: Your chosen genres (the genres you choose while setting up the account). The genre of the shows and movies you have watched The actors and directors you have watched. The shows and movies people with a similar taste to yours watch. There are probably a ton of other factors Netflix uses to determine which shows to recommend. Their goal: to keep you stuck to the screen as long as possible.  Thumbnails The thumbnails you see for a show or movie aren’t necessarily the ones your best friend sees when they scroll through their homepage.  Netflix uses machine learning to determine which thumbnails you have the highest chance to click on. They have different thumbnails for every show and movie, and their ML algorithms constantly test them with the users.  The thumbnails that get the most clicks and generate the most interest get preference over those that don’t get clicks. Machine learning enables Netflix to give personalised auto-generated thumbnails for every show and movie. Their chosen thumbnail depends on your preferences and watches history to ensure they have the highest chance of getting clicked on.  For example, Riverdale can have two thumbnails, a serious mystery one and a romantic one. The one you’ll see would depend on which genre you prefer the most. Clicking on a thumbnail increases your chances of watching the show or movie. This is why Netflix focuses heavily on showing you the thumbnail you’d like the most.  The Streaming Quality When you’re watching a show, what’s the worst thing that can happen? Buffering. Buffering can be a huge issue no matter what streaming service you use. People tend to immediately exit the platform after waiting for a few seconds because of buffering. Netflix is well aware of this issue. Buffering can ruin a customer’s experience and make it difficult for Netflix to get their valuable time back. Moreover, the customer might switch platforms and start watching something on their competitors’ platforms, such as Hulu, Amazon Prime, HBO MAX or Disney+.  They have implemented many solutions to counter this problem, one of which is machine learning.  Machine learning enables them to keep a close eye on their subscribers’ usage of their services. These algorithms predict their users’ viewing patterns to determine when most people use their service and when this number is the lowest.  Then, they use this information to cache regional servers closest to the viewers, ensuring that no buffering (or minimal buffering) occurs when those users use the service.  The Location of a Show (or movie) Netflix isn’t just a streaming platform for showing movies and shows. They are also a production company. Producing unique content helps to increase their revenue and profitability.  So far, this strategy has worked amazingly well because, over the years, the amount of Netflix-original content has increased substantially. In 2019, they produced 2,769 hours of original content, 80% more than the previous year.  Every show requires a shooting location. Netflix uses machine learning to determine which shooting location would be perfect for a particular show or movie.  They employ machine learning algorithms to check the cost & schedules of the crew & cast, shooting requirements (city, desert, village, etc.), weather, the possibility of getting a permit, and many other relevant factors. Machine learning enables them to quickly check and analyse these numerous factors, ensuring they quickly find a suitable shooting location.  The Creativity Probably the biggest application of machine learning in Netflix is in content creation. Unlike most production companies, Netflix behaves as a tech enterprise. They don’t create content solely based on the creativity of a few writers or content creators. Instead, they use machine learning algorithms to conduct market research and find which type of content would be the most suited for a particular market segment.  ML algorithms help them stay ahead of market trends and create shows and movies for everyone. Their approach has helped them substantially as eight out of the top 10 most popular original video series from streaming providers in the US are by Netflix.  Their research helps them penetrate different market segments. For example, the content preference of teenagers would differ drastically from that of married couples. Through thorough market research and ML implementation, Netflix can successfully satisfy a diverse audience base’s content requirements.  The Secret Is Out Now you know the secret behind Netflix’s phenomenal success. They use the latest technologies like machine learning and data science in almost every avenue of their business.  This helps them stay ahead of their competition and offer a better user experience. It’s a prominent reason why they are the biggest streaming service provider in the US.  What do you think about Netflix and its use of machine learning? Which machine learning application did you find the most intriguing?  With all the learnt skills you can get active on other competitive platforms as well to test your skills and get even more hands-on. If you are interested to learn more about the course, check out the page of Master of Science in Machine Learning & AI and talk to our career counsellor for more information.

Machine learning has become an integral part of multiple industries. From autonomous vehicles to e-commerce stores, machine learning finds applications in nearly every aspect of our daily lives.  However, when we talk about machine learning, an industry that rarely comes to mind is education which begs the question, “Are there any applications of machine learning in the education sector?” As it turns out, there are plenty of applications of machine learning technology in education. This article will share some of the most prominent ML technology applications in teaching and education and show how bright the future of these two is.  Before we start talking about machine learning and education’s relationship, let’s first discuss the technology itself.  Join the Best Machine Learning Course online from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Program in ML & AI to fast-track your career. A Brief Introduction To Machine Learning In machine learning, you create machines that can execute tasks and learn from them without requiring any human intervention.  What does it mean?  It means the machine doesn’t require you to enter the task every time you use it or make changes to its operation. The machine will learn to better its performance with each task and implement the necessary changes in the next iteration. Sounds fascinating.  The education sector isn’t the only area where we use machine learning. It has a ton of applications in our daily lives. The face recognition lock on your iPhone uses machine learning to identify your face.  Similarly, Google Assistant learns every time you use it to give you a better experience. When a spam email gets filtered out automatically in your Gmail account, you can thank machine learning for it.  Other prominent industries that use machine learning are manufacturing, transport, finance, healthcare, and many others.  Applications of Machine Learning in Education The education and e-learning industries can benefit highly from incorporating machine learning and artificial intelligence. Following are some of the primary areas of education that can benefit from the use of machine learning:  Reduced Bias in Grading Machine learning can help teachers in examining student assessments and assignments. They can determine if there is any plagiarism and find other similar errors. Machine learning tools can grade students and provide suggestions on improving the grade, making the teacher’s job much easier. Moreover, machine learning implementations can reduce bias in grading, which can be a considerable flaw. A teacher’s attitude towards a student shouldn’t affect the grades they allot to students. An ML framework designed to evaluate students would perform grading unbiasedly, solely based on their performance. However, that doesn’t mean they wouldn’t need human intervention. The educator would still have the final say as they can keep other factors in consideration, such as the student’s behaviour and their in-class participation.  Machine learning grading/evaluation applications would make the grading process much efficient and easier to manage. This would allow educators to shift their focus on other crucial areas of teaching, which leads us to our next point.  More Efficient Operations A big reason why artificial intelligence and machine learning have become so popular is they allow organisations to automate operations. Automation increases operation efficiency substantially.  E-learning companies and educational institutes can use ML to automate their day-to-day tasks and optimise their operations. They can use virtual assistants to help students find relevant courses and study material much quickly. Similarly, they can automate daily tasks such as storing student-related data and scheduling by using ML tools. According to MIT (Massachusetts Institute of Technology), more than 96% of MOC (Massive Online Courses) students give up their courses. Using ML can help organisations enhance their learning experience and rectify this issue.  Career Path Prediction Another prominent application of machine learning in education is career path prediction. Predictive analysis is a core component of machine learning, where we use ML algorithms to predict an outcome accurately. You can train ML algorithms to take input from students and chart out customised career paths for them. They can study the data gained from teachers and parents to get more insight into an individual student’s interests and career aspirations.  They can use personality tests and IQ tests to help generate career paths for students, allowing them to find careers they will excel in and enjoy. The technology can also predict students’ problem areas and assist them, such as extra classes or workshops, to succeed professionally. Such machine learning implementation will allow students to get rid of career-related confusion and make better-informed decisions about their profession. Students will be able to identify their strengths and maximise their potential. Similarly, they can find their weaknesses early and strengthen those areas with optimal performance.  Enhanced Learning Experience Every student is unique in that each grasps concepts differently, at a different pace. Incorporating machine learning can help institutes and e-learning providers to offer better and more personalised learning experiences to their students. ML can allow you to develop detailed logs for every student, providing them with learning material based on their specific interests and requirements. It can help educators understand how well each student understands different concepts.  They can use this information to customise the study material and plans for each student, allowing them to learn steadily and effectively.   Artificial Intelligence and Machine Learning can help students get personalised courses based on their exact requests. This can save a lot of time and make the learning experience highly efficient.  Recommender systems are a prominent application of machine learning and AI. They focus on giving personalised recommendations to a user, depending on the user’s interests and history. E-learning providers can use recommender systems to suggest courses that match a user’s interests and requirements. Many major companies use recommender systems such as Amazon and Netflix, which allow them to give a better user experience to their customers. Recommender systems in E-learning will make it easier for people to find courses for their career aspirations and interests.  How Is The Future of Machine Learning In Education?  Machine learning can solve many problems in the education sector. It can simplify a teacher’s job, reduce stress, and enable them to offer more personalised learning experiences to their students.  Some educational institutes and companies have started using ML already. For example, Cram101 is a service that uses ML to create study guides and chapter summaries of textbooks to make them easy to understand.  Another prominent solution is Netex Learning, which allows education institutes to create curriculums and integrate video and audio with their study material.  Many organisations have started implementing ML technologies in innovative ways. Thus, rest assured, you can certainly expect to have a future-proof career in Machine Learning. Moreover, a machine learning engineer’s average salary is $112,852, so it’s undoubtedly a very lucrative career. If you’re interested in a career in education, you can enter as an ML expert. What do you think about the future of machine learning in education? What other impacts can it have on this field? Read more about machine learning salary. With all the learnt skills you can get active on other competitive platforms as well to test your skills and get even more hands-on. If you are interested to learn more about the course, check out the page of Executive PG Programme in Machine Learning & AI and talk to our career counsellor for more information.

The last decade has seen tremendous growth in Artificial Intelligence and smarter machines. The field has given rise to many sub-disciplines that are specializing in distinct aspects of human intelligence. For instance, natural language processing tries to understand and model human speech, while computer vision aims to provide human-like vision to machines.  Since we’ll be talking about Convolutional Neural Networks, our focus will mostly be on computer vision. Computer vision aims to enable machines to view the world as we do and solve problems related to image recognition, image classification, and a lot more. Convolutional Neural Networks are used to achieve various tasks of computer vision. Also known as CNN or ConvNet, they follow an architecture that resembles the patterns and connections of neurons in the human brain and are inspired by various biological processes occurring in the brain to make communication happen.  The biological significance of a Convoluted Neural Network CNNs are inspired by our visual cortex. It is the area of the cerebral cortex that is involved in visual processing in our brain. The visual cortex has various small cellular regions that are sensitive to visual stimuli.  This idea was expanded in 1962 by Hubel and Wiesel in an experiment where it was found that different distinct neuronal cells respond (get fired) to the presence of distinct edges of a specific orientation. For instance, some neurons would fire on detecting horizontal edges, others on detecting diagonal edges, and some others would fire when they detect vertical edges. Through this experiment. Hubel and Wiesel found out that the neurons are organized in a modular manner, and all the modules together are required for producing the visual perception.  This modular approach – the idea that specialized components inside a system have specific tasks – is what forms the basis of the CNNs.  With that settled, let’s move on to how CNNs learn to perceive visual inputs.   Convolutional Neural Network Learning Images are composed of individual pixels, which is a representation between numbers 0 and 255. So, any image that you see can be converted into a proper digital representation by using these numbers – and that is how computers, too, work with images.  Here are some major operations that go into making a CNN learn for image detection or classification. This will give you an idea of how learning takes place in CNNs.   1. Convolution Convolution can mathematically be understood as the combined integration of two different functions to find out how the influence of the different function or modify one another. Here’s how it can be defined in mathematical terms:  The purpose of convolution is to detect different visual features in the images, like lines, edges, colors, shadows, and more. This is a very useful property because once your CNN has learned the characteristics of a particular feature in the image, it can later recognize that feature in any other part of the image.  CNNs utilize kernels or filters to detect the different features that are present in any image. Kernels are just a matrix of distinct values (known as weights in the world of Artificial Neural Networks) trained to detect specific features. The filter moves over the entire image to check if the presence of any feature is detected or not. The filter carries out the convolution operation to provide a final value that represents how confident it is that a particular feature is present.  If a feature is present in the image, the result of the convolution operation is a positive number with a high value. If the feature is absent, the convolution operation results in either 0 or a very low-valued number.  Let’s understand this better using an example. In the below image, a filter has been trained for detecting a plus sign. Then, the filter is passed over the original image. Since a part of the original image contains the same features that the filter is trained for, the values in each cell where the feature exists is a positive number. Likewise, the result of a convolution operation will also result in a large number.  However, when the same filter is passed over an image with a different set of features and edges, the output of a convolution operation will be lower – implying there wasn’t any strong presence of any plus sign in the image.  So, in the case of complex images having various features like curves, edges, colours, and so on, we’ll need an N number of such feature detectors.  When this filter is passed through the image, a feature map is generated which is basically the output matrix that stores the convolutions of this filter over different parts of the image. In the case of many filters, we’ll end up with a 3D output. This filter should have the same number of channels as the input image for the convolution operation to take place. Further, a filter can be slid over the input image at different intervals, using a stride value. The stride value informs how much the filter should move at each step. The number of output layers of a given convolutional block can therefore be determined using the following formula:  2. Padding One issue while working with convolutional layers is that some pixels tend to be lost on the perimeter of the original image. Since generally, the filters used are small, the pixels lost per filter might be a few, but this adds up as we apply different convolutional layers, resulting in many pixels lost.  The concept of padding is about adding extra pixels to the image while a filter of a CNN is processing it. This is one solution to help the filter in image processing – by padding the image with zeroes to allow for more space for the kernel to cover the entire image. By adding zero paddings to the filters, the image processing by CNN is much more accurate and exact.  Check the image above – padding has been done by adding additional zeroes at the boundary of the input image. This enables the capture of all the distinct features without losing any pixels.  3. Activation Map The feature maps need to be passed through a mapping function that is non-linear in nature. The feature maps are included with a bias term and then passed through the activation (ReLu) function, which is non-linear. This function aims to bring some amount of nonlinearity into the CNN since the images that are being detected and examined are also non-linear in nature, being composed of different objects.   4. Pooling Stage Once the activation phase is over, we move on to the pooling step, wherein the CNN down-samples the convolved features, which help save processing time. This also helps in reducing the overall size of the image, overfitting, and other issues that would occur if the Convoluted Neural Networks are fed with a lot of information – especially if that information is not too relevant in classifying or detecting the image.  Pooling is basically of two types – max pooling and min pooling. In the former, a window is passed over the image according to a set stride value, and at each step, the maximum value included in the window is pooled in the output matrix. In the min pooling, the minimum values are pooled in the output matrix.  The new matrix that’s formed as a result of the outputs is called a pooled feature map.  Out of min and max pooling, one benefit of max-pooling is that it allows the CNN to focus on a few neurons which have high values instead of focusing on all the neurons. Such an approach makes it very less likely to overfit the training data and makes the overall prediction and generalization go well.  5. Flattening After the pooling is done, the 3D representation of the image has now been converted into a feature vector. This is then passed into a multi-layer perceptron to produce the output. Check out the image below to better understand the flattening operation:  As you can see, the rows of the matrix are concatenated into a single feature vector. If multiple input layers are present, all the rows are connected to form a longer flattened feature vector.   6. Fully Connected Layer (FCL) In this step, the flattened map is fed to a neural network. The complete connection of a neural network includes an input layer, the FCL, and a final output layer. The fully connected layer can be understood as the hidden layers in Artificial Neural Networks, except, unlike hidden layers, these layers are fully connected. The information passes through the entire network, and a prediction error is calculated. This error is then sent as feedback (backpropagation) through the systems to adjust weights and improve the final output, to make it more accurate.   The final output obtained from the above layer of the neural network doesn’t generally add up to one. These outputs need to be brought down to numbers in the range of [0,1] – which will then represent the probabilities of each class. For this, the Softmax function is used.  The output obtained from the dense layer is fed to the Softmax activation function. Through this, all the final outputs are mapped to a vector where the sum of all the elements comes out to be one.  The fully connected layer works by looking at the previous layer’s output and then determining which feature most correlates to a specific class. Thus, if the program predicts whether or not an image contains a cat, it will have high values in the activation maps that represent features like four legs, paws, tail, and so on. Likewise, if the program is predicting something else, it will have different types of activation maps. A fully connected layer takes care of the different features that strongly correlate to particular classes and weights so that the computation between weights and the previous layer is accurate, and you get correct probabilities for distinct classes of output.  A quick summary of the working of CNNs Here’s a quick summary of the entire process of how CNN works and helps in computer vision:  The different pixels from the image are fed to the convolutional layer, where a convolution operation is performed.  The previous step results in a convolved map.  This map is passed through a rectifier function to give rise to a rectified map.  The image is processed with different convolutions and activation functions for locating and detecting different features.  Pooling layers are used to identify specific, distinct parts of the image.  The pooled layer is flattened and used as an input to the fully connected layer.  The fully connected layer calculates the probabilities and gives an output in the range of [0,1].  In Conclusion The inner functioning of CNN is very exciting and opens a lot of possibilities for innovation and creation. Likewise, other technologies under the umbrella of Artificial Intelligence are fascinating and are trying to work between human capabilities and machine intelligence. Consequently, people from all over the world, belonging to different domains, are realizing their interest in this field and are taking the first steps.  Luckily, the AI industry is exceptionally welcoming and doesn’t distinguish based on your academic background. All you need is working knowledge of the technologies along with basic qualifications, and you’re all set!  If you wish to master the nitty-gritty of ML and AI, the ideal course of action would be to enroll in a professional AI/ML program. For instance, our Executive Programme in Machine Learning and AI is the perfect course for data science aspirants. The program covers subjects like statistics and exploratory data analytics, machine learning, and natural language processing. Also, it includes over 13 industry projects, 25+ live sessions, and 6 capstone projects. The best part about this course is that you get to interact with peers from across the world. It facilitates the exchange of ideas and helps learners build lasting connections with people from diverse backgrounds. Our 360-degree career assistance is just what you need to excel in your ML and AI journey! Lead the AI Driven Technological Revolution

Amazon Web Services, short for AWS, is a comprehensive cloud-based platform offered by Amazon. It provides various offerings in the form of SaaS (Software as a Service), PaaS (Platform as a Service), and IaaS (Infrastructure as a Service). AWS was launched in 2006 in an attempt to help businesses across the globe get access to all the technologies and infrastructure they need to empower their operations. AWS was one of the earliest pay-as-you-go models that could help businesses scale storage, throughput, and computation powers based on their needs. Amazon Web Services offers cloud-based services from different data centres and availability zones spread across the globe. Each availability zone contains various data centres in itself. Customers are given the ability to set their virtual machines and replicate their data in different data centres – to have a system that is resistant to a server or data centre failure.  A Brief Introduction to Amazon Web Services In the olden days, for businesses to work with technologies, they needed to have a personal data centre to store and host the different computers and an IT team to take care of this entire setup and infrastructure. Businesses had to take care of power, backups, temperature controls, and other essential things required to keep such a technical ecosystem in motion. As a result of this, a lot of resources, effort, time, and money went into the software and the equipment required by businesses to enter the technology space. This presented an obvious barrier for young companies, innovators, and entrepreneurs, who do not have access to such resources.  In the early 90s, Amazon was one of the most prominent players in the e-commerce industry. AWS was born out of their need to build such a scalable technological architecture. Amazon required each of its distinct departments to operate as a mini-company. So, if there was a requirement for data from another department, they needed to develop enterprise-grade interfaces to collect this data. They expanded on this idea and built data centres with all of the hardware, power, and IT teams to manage them. Then they made this infrastructure available for businesses globally. With this, companies didn’t need to build the infrastructure for themselves. They could essentially rent Amazon’s infrastructure, making it possible for new players to enter the market. With AWS, businesses don’t need to have on-site IT teams and data centres – they can rely on AWS for its availability, scalability, and security.  Amazon Web Services includes several services, ranging from website hosting to database management to strict security to Augmented Reality and game development. Companies need to figure out which AWS suite they require and pick that one, to begin with!  What all is included in the Amazon Web Services Spectrum? The offerings of Amazon Web Services are divided into separate services – and each can be customized based on the user’s needs. The AWS portfolio consists of more than 100 services for different domains like database management, infrastructure management, security, computation, application development, and more. Some of these service categories include:  Database management Computation powers Migration Networking Development tools Security Big data management Governance Mobile development Messages and notifications  Using Amazon Web Services While there’s an initial learning curve in terms of setting up and using Amazon Web Services, it gets easier with time. Talking in terms of web development, companies tend to employ continuous deployment and integration using third-party vendors like Travis CI or Jenkins. Once the configuration is completed, the web developers start working on top of AWS by pushing and merging their codes to AWS data centres.  Likewise, larger companies utilize AWS in different ways. They generally have DevOps engineers responsible for configuring, setting up, and maintaining various AWS services like S3, RDS, CE2, Route 53, and more.  Even government and national agencies use AWS for supporting their technical requirements – and the US government and CIA are just two such examples. AWS has a lot of users across the world, some of the big names among them include:  NASA Netflix Slack Adobe Comcast Expedia Adobe The best part about AWS is that companies don’t need to completely give up on their previously used technology stacks as AWS accommodates most of the legacy tech stacks. One of the fundamental elements of Amazon Web Services is Amazon Machine Image (AMI). With AWS, people can create AMIs of whatever tech stack they have been using or want to use. AMIs are quickly and easily adaptable to any other tech stack a company wants to use. It isn’t like AWS is the only company in this space. It has some cloud space competitors like Google Cloud, Microsoft Azure, and Oracle Cloud Services. However, none of these services come close to AWS and its offerings. Amazon started by building these services for themselves to meet their needs and then branched this out for every organization across the globe to benefit from. This approach has ensured that all the services they offer are relevant for businesses and easy to use and adopt!  Getting Started with Learning AWS If you’re looking for a career in Machine Learning and Artificial Intelligence, it’s advised that you have some understanding of different AWS services along with how they work. However, if you’re a complete beginner, you don’t need to focus on AWS fully – you just need to focus on it enough to get a working knowledge of it. When you start as a fresher coder, you should focus more on getting the fundamentals of logical flow and understanding algorithm optimizations and data structures.  However, it’s always important to know that there’s a much broader ecosystem available in the engineering world beyond just coding, and it supports, maintains, and makes the code accessible to people around the globe. As a result, broadening your scope beyond programming languages and coding is vital in today’s technologically driven world.  Considering that AWS is a collection of various distinct services, it’s recommended that you thoroughly clear some basics before trying to work your way around AWS. Here are some things for you to look into:  Client-server technology: How does your laptop browser (the client) communicate with the server (the machine that handles all the requests?  Network protocols: How do different network protocols like HTTP, HTTPS, FTP, and more can be used for safe and secure communication between the client and the server?  IP address details: How does IP address work, and how are they used to identify different assets on the internet?  Domain Name System: What are Domain Name Systems, and how can they be used to convert a URL into an IP address?  The questions listed above aren’t beginner questions, but they are indeed ones that’ll help you transition and broaden your understanding of how technologies work around the web. With this knowledge, you’ll find yourself in a much more comfortable position to understand AWS and work with these services.  In Conclusion The importance of AWS can’t be overstated today in 2021. With most companies – from industry giants to freshers – using the features of AWS, the requirement of AWS experts has also increased in the workplace. Many exciting job opportunities have therefore been opened up in AI and ML due to the features, advancements, and requirements of AWS. As a result of this, people from all over the world, belonging to different domains, realize their interest in this field and are taking the first steps.  At upGrad, we’ve helped many students realize their dream of working in the AI domain by offering them personalized training, a collaborative learning environment, and lectures from industry experts. Our Executive Programme in Machine Learning and AI is designed to help you start from scratch and reach your full potential. Our global learner base of 40,000+ paid learners and 500,000+ working professionals will ensure that you enjoy a complete peer-to-peer learning experience. Our 360-degree career assistance is just what you need to excel in your ML and AI journey!  Reach out to upGrad and experience a 360-degree learning atmosphere that helps you thrive and level up in your career! 

Machine learning is an AI branch that focuses on developing systems that can perform specific tasks and improve themselves automatically without requiring human intervention. Machine learning has become one of the most popular tech skills in the market.  The professionals who primarily help companies in developing and implementing machine learning-based solutions are machine learning engineers. Companies rely on them for handling their AI and ML requirements. Due to this, their salary is sky-high.  The following points will throw light on the average machine learning engineer salary, what factors affect it, and how you can enter this sector. Let’s get started! What is the average machine learning engineer salary? The average machine learning engineer salary in the US is $112,837 per year. Their pay starts from $76,000 per year and goes up to $154,000 per annum. Bonus for this role can go up to $24,000, and the shared profit can go up to $41,000. This role attracts such a high salary because while companies across the globe are looking for AI and ML professionals, their market supply is relatively low.  Image Source According to a Forrester report, AI and ML will generate new and innovative roles in multiple industries because companies would want to push AI to new frontiers. Companies would focus on implementing AI use cases faster to get ahead of their competitors.  Another reason why the demand for machine learning engineers is increasing is that more than a third of companies looking for adaptation and growth in 2024 will employ AI to solve their automation and augmentation problems.  Similarly, an Analytics Insight report found that the global skills gap in the AI sector is 66%. Certainly, there’s a shortage of skilled AI and ML professionals. That’s why the average machine learning engineer salary is substantially high all across the globe.  What does a Machine Learning Engineer do? A machine learning engineer works with large quantities of data to create models that solve their organization’s particular problems. Their role is quite similar to that of a data scientist as both use large amounts of data. However, machine learning engineers have to create self-running solutions that perform predictive model automation. Their created solutions learn from every iteration to improve their effectiveness and optimize their results to get better accuracy. Machine learning engineers have to program models that can perform their tasks with minimum or no human intervention. They work with data scientists to identify the requirements of their organization and create the required solutions.  Machine learning engineers usually work in teams. Thus, they must have strong communication skills. Machine learning engineers have to develop ML-based apps that match their client’s or customer’s requirements.  They explore and visualize data to find distinctions in data distribution that could affect model performance during a deployment. ML engineers are also responsible for researching, experimenting with, and employing the necessary ML algorithms.  They have to perform statistical analysis, find datasets for their training and train their ML systems as required.  Factors affecting the average machine learning engineer salary Skills Recruiters are always on the lookout for candidates that have the latest and in-demand skills. To get attractive pay as a machine learning engineer, you must stay on top of the industry trends and develop the necessary skills.  For example, the most popular skills among machine learning engineers in the US are deep learning, natural language processing (NLP), Python, and computer vision.  Having certain skills can help you get a pay bump. One such highest-paying skill for machine learning engineers in the US is Scala. ML engineers with the Scala skill earn 26% more than the national average. Other skills that offer help you get higher pay in this field are:  Data modeling (16% more than the average) Artificial intelligence (11% more than the average) PyTorch (11% more than the average) Image processing (7% more than the average) Apache Spark (15% more than the average) Big data analytics (5% more than the average) Software development (3% more than the average) Natural language processing (3% more than the average) Image Source Knowing which skills offer better pay can help you strategize your career progress and boost your growth substantially.  Experience Experience plays a crucial role in determining how much you earn as a machine learning engineer. According to the statistics, entry-level ML engineers make 17% less than the average, while a mid-career professional in this field earns 21% more than the same.  Machine learning engineers with less than a year’s experience make $93,000 per annum on average, whereas those with one to four years of professional experience earn $112,000 per annum on average.  On a similar note, ML engineers with five to nine years of experience make $137,000 per year on average. Professionals with 20+ years of experience earn $162,000 per annum. As you can see, in machine learning, gaining more experience will help you bag higher pay.  City Every city has a distinct culture, demographic, and cost of living. Hence, the city you work in can be a huge determinant of how much you make as a machine learning engineer. Several cities in the US offer significantly higher salaries than the average. Working there might help you get higher-paying roles in reputed companies as an ML engineer.  Cities with the highest average salaries for this role are: San Francisco (18% more than the national average) San Jose (16.9% more than the national average) Palo Alto (10% more than the national average) Seattle (7% more than the national average)  Similarly, you’ll find cities that offer below-average salaries for this role. These include Chicago (20% less than the national average) and Boston (8.9% less than the national average). You should always keep the city in mind while estimating how much you can expect to earn in this role.  Organization Your machine learning engineer salary would vary from company to company. It depends on many factors such as the company’s size, its work environment, its offered benefits, etc. Companies that offer the highest salaries for machine learning roles are JP Morgan Chase and Co (average pay for this role is $137,344), Apple (average pay for this role is $129,149), and Amazon.com Inc (average salary for this role is $114,795). Similarly, some companies offer lower salaries for this role due to their job requirements. Those companies include Lockheed Martin Corp (the average salary for this role is $104,228) and Intel Corporation (the average pay for this role is $92,964).  How to become a machine learning engineer? Machine learning engineers are in high demand, and you can easily bag a job with lucrative pay in this field. To become a machine learning engineer, you must be familiar with the basic and advanced concepts of artificial intelligence, machine learning,  You must also be familiar with different machine learning tools and libraries so you can create ML models efficiently. The best way to learn these various subjects and develop the necessary skills for becoming a machine learning engineer is by taking an ML course. At upGrad, we offer the Master of Science in Machine Learning and Artificial Intelligence program with the Liverpool John Moores University and the International Institute of Information Technology, Bangalore.  The course lasts for 18 months and offers 40+ hours of live sessions and six capstone projects. Some of the subjects you’ll learn during this program are statistics, exploratory data analytics, natural language processing, machine learning algorithms, etc. Each student will receive multiple benefits, including career coaching, interviews, one-on-one mentorship, and networking opportunities with peers from 85+ countries.  You must have a bachelor’s in statistics or mathematics with 50% or equivalent marks with one year of professional work experience in analytics or programming.  Conclusion Machine learning is the skill of the future. ML technology allows companies to automate processes, develop better solutions, and advance their growth. Due to these reasons, the demand for machine learning engineers is increasing globally, improving the average pay for this role. If you’re interested in becoming a machine learning engineer, we recommend checking out our Master of Science in Machine Learning and Artificial Intelligence program!

Whether you’re studying machine learning or are an AI enthusiast, you must’ve heard of TensorFlow. It’s among the most popular solutions for machine learning and deep learning professionals and has become an industry staple.  This means if you want to pursue a career in the field of AI and ML, you must be well-acquainted with this technology. If you’re wondering about questions such as what TensorFlow is and how it works, you’ve come to the right place as the following article will give you a detailed overview of this technology.  What is TensorFlow? TensorFlow is an open-source library for deep learning. The people at the Google Brain Team had initially created it to perform large computations. It wasn’t created particularly for deep learning. However, they soon realized that TensorFlow was beneficial for deep learning implementations, and since then, they have made it an open-source solution.  TensorFlow bundles multiple machine learning and deep learning algorithms and models. It allows you to use Python for machine learning and offers a front-end API to build applications. You can use C++ with TensorFlow to execute those applications and enjoy high performance.  With TensorFlow, you can easily train and run deep neural networks for various ML applications. These include word embeddings, handwritten digit classification, recurrent neural networks, image recognition, natural language processing, and partial differential equation simulations.  Along with such versatile applications, TensorFlow also lets you perform production prediction at scale as you can use the same models for training.  It accepts tensors, which are multi-dimensional arrays of higher dimensions. They are quite helpful in managing and utilizing large quantities of data.  What are the Components of TensorFlow?  To understand what is TensorFlow, you should first be familiar with the components of this technology:  1. Tensor The most important component in TensorFlow is called a tensor. It is a matrix or vector of multiple dimensions that represent all data types. All the values in a tensor have identical data types with a partially or completely known shape. The shape of data refers to the dimensionality of the array or matrix. All the TensorFlow computations use tensors. They are the building blocks for the software. A tensor can originate from computation as a result or as the input data for the same. All the operations in TensorFlow take place in a graph. In TensorFlow, a graph is a set of successive computations.  Every operation in TensorFlow is called an op node, and they are interlinked to each other. A graph outlines the connections between the various nodes and the ops. Keep in mind that it doesn’t show the values. Every edge of a node is the tensor. In other words, an edge of a node allows you to populate it with data.  2. Graph framework Operations in Tensorflow use a graph framework. The graph would collect and describe the different computations taking place during the training. It offers various benefits.  The graphs in Tensorflow make it possible to use the software on multiple GPUs or CPUs. It also allows you to use the software on a mobile operating system. Its portability enables you to preserve the computations for later use. You can save a graph so you can run it in the future, making your tasks much more manageable.  Computations in graphs take place by connecting tensors. Every tensor has an edge and a node. The node carries the operation and generates an endpoint output. The edge explains the input-output relationship between the nodes.  How Does it Work? You can build data flow graphs by using TensorFlow. A data flow graph is a structure that explains how data moves through a series of processing nodes or a graph. Every node in a graph stands for a mathematical operation.  TensorFlow gives you all of this information to the programming through the Python language. Python is easy to learn and use language. Moreover, it’s pretty easy to explain how you can high-level abstractions together through Python. In Python, the nodes and tensors of TensorFlow are Python objects, and all the TensorFlow applications are Python applications.  However, you don’t perform the actual mathematical operations in Python. The transformation libraries available in TensorFlow are high-performance C++ binaries. Python simply directs the traffic between those pieces and gives you high-level programming abstractions so you can connect them.  Because you can run TensorFlow applications on any target such as Android or iOS devices, local machines, clusters in the cloud, etc., you can run the resulting models on different devices too.  The recent version of TensorFlow, called TensorFlow 2.0, has changed how you can use this technology substantially. It introduced the Keras API, which makes it much simpler to use TensorFlow and offers support for TensorFlow Lite that allows you to deploy models on a larger spectrum of platforms.  The only catch is you’ll have to rewrite the code rewritten for the previous TensorFlow version.  Benefits of using TensorFlow TensorFlow is among the most popular machine learning and deep learning technologies. The main reason behind its widespread popularity is the various advantages it offers to businesses. The following are the primary benefits of using TensorFlow:  1. Open-source TensorFlow is an open-source solution. This means it’s free to use, which has enhanced its accessibility substantially as companies don’t have to invest much to start using TensorFlow.  2. Use of Graph Computation Graph computation allows you to visualize a neural network’s construction through the Tensorboard. Through the visualization, you can examine the graph and generate the required insights.  3. Flexible TensorFlow is compatible with various devices. Moreover, the introduction of TensorFlow lite has made it much more flexible as it has become compatible with more devices. You can use TensorFlow from anywhere as long as you have a compatible device (laptop, PC, cloud, etc.). 4. Versatile TensorFlow has many APIs to build at scale deep learning architectures. Moreover, it’s a Google product, giving it access to Google’s vast resources. TensorFlow can integrate easily with many AI and ML technologies, making it highly versatile. You can use TensorFlow for various deep learning applications due to its multiple features.  Learn more about TensorFlow and other AI topics There are many applications of TensorFlow. Understanding how it operates and how you can use it in deep learning are advanced concepts. Moreover, you must also know the fundamentals of artificial intelligence and machine learning to use this software correctly.  Hence, the most efficient way to learn TensorFlow and its relevant concepts is by taking a machine learning course. Taking such a course will give you access to a detailed curriculum and learn from experts.  upGrad offers the Executive PG Programme in Machine Learning and AI with IIIT-B to help you significantly in learning and understanding TensorFlow.  It’s a 12-month course and requires you to have a bachelor’s degree with 50% marks with mathematics or statistical background and one year of professional work experience in programming or analytics. The program offers 40+ live sessions and 25+ expert sessions to streamline your learning experience.  During the course, you’ll be working on 14 assignments and projects that will help you test your knowledge of AI, ML, and other related subjects. You’ll get peer-to-peer networking opportunities during the program. upGrad has a learner base in over 85 countries. Through this platform, you can network globally and accelerate your career growth significantly. Along with these advantages, you’ll also receive career coaching, one on one industry mentorship, and just-in-time interviews so you can pursue a promising career in this field.  Conclusion TensorFlow is a popular AI technology, and if you’re interested in becoming an AI or ML professional, you must be familiar with this software.  TensorFlow uses tensors and allows you to perform graph computations. If you’re interested in learning more about TensorFlow, we recommend checking out the course we have shared above.

Python handles variables in a very maverick manner. While many programming languages consider variables as global by default (unless declared as local), Python considers variables as local unless declared the other way round. The driving reason behind Python considering variables as local by default is that using global variables is generally regarded as poor coding practice.  So, while programming in Python, when variable definition occurs within a function, they are local by default. Any modifications or manipulations that you make to this variable within the body of the function will stay only within the scope of this function. Or, in other words, those changes will not be reflected in any other variable outside the function, even if the variable has the same name as the function’s variable. All variables exist in the scope of the function they are defined in and hold that value. To get hands-on experience on python variables and projects, try our data science certifications from best universities from US. Through this article, let’s explore the notion of local and global variables in Python, along with how you go about defining global variables. We’ll also look at something known as “nonlocal variables”. Read on!  Global and Local Variables in Python Let’s look at an example to understand how global values can be used within the body of a function in Python:  Program:  def func():      print(string)  string =  “I love Python!” func() Output  I love Python! As you can see, the variable string is given the value “I love Python!” before func() is called. The function body consists just of the print statement. As there is no assignment to the string variable within the body of the function, it will take the global variable’s value instead.  As a result, the output will be whatever the global value of the variable string is, which in this case, is “I love Python!”. Now, let us change the value of string inside the func() and see how it affects the global variables: Program:  def func():      string =  “I love Java!”     print(string)  string =  “I love Python!”  func() print(string) Output: I love Java! I love Python! In the above program, we have defined a function func(), and within it, we have a variable string with the value “I love Java!”. So, this variable is local to the function func(). Then, we have the global variable as earlier, and then we call the function and the print statement. First, the function is triggered, calling the print statement of that function and delivering the output “I love Java!” – which is the local variable for that function. Then, once the program is out of the function’s scope, the value of s gets changed to “I love Python”, and that is why we get both the lines as output.  Now, let us add the first two examples, and try to access the string using the print statement, and then try to assign a new value to it. Essentially, we are trying to create a string as both a local and global variable.  Fortunately, Python does not allow this confusion and throws an error. Here’s how:  Program: def func():     print(string)    string =  “I love Java!”    print(string) string =  “I love Python!” func() Output (Error):  ————————————————————————— UnboundLocalError                         Traceback (most recent call last) <ipython-input-3-d7a23bc83c27> in <module>       5        6 string =  “I love Python!” —-> 7 func() <ipython-input-3-d7a23bc83c27> in func()       1 def func(): —-> 2    print(string)       3    string =  “I love Java!”       4    print(string)       5  UnboundLocalError: local variable ‘s’ referenced before assignment Evidently, Python does not allow a variable to be both global and local inside a function. So, it gives us a local variable since we assign a value to the string within the func(). As a result, the first print statement shows the error notification. All the variables created or changed within the scope of any function are local unless they have been declared as “global” explicitly. Defining Global Variables in Python The global keyword is needed to inform Python that we are accessing global variables. Here’s how:  Program:  def func():     global string     print(string)     string =  “But I want to learn Python as well!”     print(string) string =  “I am looking to learn Java!”  func() print(string) Output: I am looking to learn Java! But I want to learn Python as well! But I want to learn Python as well! As you can see from the output, Python recognizes the global variables here and evaluates the print statement accordingly, giving appropriate output.  Also, Check out all trending Python tutorial concepts in 2024. Using Global Variables in Nested Functions Now, let us examine what will happen if global variables are used in nested functions. Check out this example where the variable ‘language’ is being defined and used in various scopes:  Program: def func():     language = “English”     def func1():         global language         language = “Spanish”     print(“Before calling func1: ” + language)     print(“Calling func1 now:”)     func1()     print(“After calling func1: ” + language)    func() print(“Value of language in main: ” + language) Output: Before calling func1: English Calling func1 now: After calling func1: English Value of language in main: Spanish As you can see, the global keyword, when used within the nested func1, has no impact on the variable ‘language’ of the parent function. That is, the value is retained as “English”. This also shows that after calling func(), a variable ‘language’ exists in the module namespace with a value ‘Spanish’.  This finding is consistent with what we figured out in the previous section as well – that a variable, when defined inside the body of a function, is always local unless specified otherwise. However, there should be a mechanism for accessing the variables belonging to different other scopes as well.  That is where nonlocal variables come in!  Nonlocal Variables Nonlocal variables are fresh kinds of variables introduced by Python3. These have a lot in common with global variables and are extremely important as well. However, one difference between nonlocal and global variables is that nonlocal variables don’t make it possible to change the variables from the module scope.  Check out the following examples to understand this: Program: def func():     global language     print(language)     language = “German” func() Output: German As expected, the program returns ‘Frankfurt’ as the output. Now, let us change ‘global’ to ‘nonlocal’ and see what happens:  Program: def func():     nonlocal language     print(language) language = “German” func() Output:  File “<ipython-input-9-97bb311dfb80>”, line 2     nonlocal language     ^ SyntaxError: no binding for nonlocal ‘language’ found As you can see, the above program throws a syntax error. We can comprehend from here that nonlocal assignments can only be done from the definition of nested functions. Nonlocal variables should be defined within the function’s scope, and if that’s not the case, it can not find its definition in the nested scope either. Now, check out the following program:  Program: def func():     language = “English”     def func1():         nonlocal language         language = “German”     print(“Before calling func1: ” + language)     print(“Calling func1 now:”)     func1()     print(“After calling func1: ” + language)     language = “Spanish” func() print(“‘language’ in main: ” + language) Output: Before calling func1: English Calling func1 now: After calling func1: German ‘language’ in main: Spanish The above program works because the variable ‘language’ was defined before calling the func1(). If it isn’t defined, we will get an error like below:  Program: def func():     #language = “English”     def func1():         nonlocal language         language = “German”     print(“Before calling func1: ” + language)     print(“Calling func1 now:”)     func1()     print(“After calling func1: ” + language)     language = “Spanish” func() print(“’language’ in main: ” + language) Output: File “<ipython-input-11-5417be93b6a6>”, line 4     nonlocal language     ^ SyntaxError: no binding for nonlocal ‘language’ found However, the program will work just fine if we replace nonlocal with global:  Program: def func():     #language = “English”     def func1():         global language         language = “German”     print(“Before calling func1`: ” + language)     print(“Calling func1 now:”)     func1()     print(“After calling func1: ” + language)     language = “Spanish” func() print(“’language’ in main: ” + language) Output: Before calling func1: English Calling func1 now: After calling func1: German ‘language’ in main: German If you notice, the value of the global variable (language) is also getting changed in the above program! That is the power that nonlocal variables bring with them!  In Conclusion In this article, we discussed local, global, and nonlocal variables in Python, along with different use cases and possible errors that you should look out for. With this knowledge by your side, you can go ahead and start practising with different kinds of variables and noticing the subtle differences.  Python is an extremely popular programming language all around the globe – especially when it comes to solving data-related problems and challenges. At upGrad, we have a learner base in 85+ countries, with 40,000+ paid learners globally, and our programs have impacted more than 500,000 working professionals. Our courses in Data Science and Machine Learning have been designed to help motivated students from any background start their journey and excel in the data science field. Our 360-degree career assistance ensures that once you are enrolled with us, your career only moves upward. Reach out to us today, and experience the power of peer learning and global networking!

Data mining can be understood as the process of exploring data through cleaning, finding patterns, designing models, and creating tests. Data Mining includes the concepts of machine learning, statistics, and database management. As a result, it is often easy to confuse data mining with data analytics, data science, or other data processes.  Data mining has had a long and rich history. As a concept, it emerged with the emergence of the computing era in the 1960s. Historically, Data Mining was mostly an intensive coding process and required a lot of coding expertise. Even today, data mining involves the concepts of programming to clean, process, analyze, and interpret data. Data specialists need to have a working knowledge of statistics and at least one programming language to accurately perform data mining tasks. Thanks to intelligent AI and ML systems, some of the core data mining processes are now automated. If you are a beginner in python and data science, upGrad’s data science programs can definitely help you dive deeper into the world of data and analytics. In this article, we’ll help you clarify all the confusions around data mining, by walking you through all the nuances, including what it is, key concepts to know, how it works, and the future of data mining! To begin with – Data Mining isn’t precisely Data Analytics It is natural to confuse data mining with other data projects, including data analytics. However, as a whole, data mining is a lot broader than data analytics. In fact, data analytics is merely one aspect of data analytics. Data mining experts are responsible for cleaning and preparing the data, creating evaluation models, and testing those models against hypotheses for business intelligence projects. In other words, tasks like data cleaning, data analysis, data exploration are parts of the entire data mining spectrum, but they are only the parts of a much bigger whole.  Key Data Mining Concepts Successfully carrying out any data mining task requires several techniques, tools, and concepts. Some of the most important concepts around data mining are:  Data cleaning/preparation: This is where all the raw data from disparate sources is converted into a standard format that can be easily processed and analyzed. This includes identifying and removing errors, finding missing values, removing duplicates, etc.  Artificial Intelligence: AI systems perform analytical activities around human intelligence, such as planning, reasoning, problem-solving, and learning.  Association rule learning: Also known as market basket analysis, this concept is essential for finding the relationship between different variables of a dataset. By extension, this is an extremely crucial component to determine which products are typically purchased together by customers.  Clustering: Clustering is the process of dividing a large dataset into smaller, meaningful subsets called clusters. This helps in understanding the individual nature of the elements of the dataset, using which further clustering or grouping can be done more efficiently.  Classification: The concept of classification is used for assigning items in a large dataset to target classes to improve the prediction accuracy of the target classes for each new data.  Data analytics: Once all the data has been brought together and processed, data analytics is used to evaluate all the information, find patterns, and generate insights.  Data warehousing: This is the process of storing an extensive collection of business data in ways that facilitate quick decision-making. Warehousing is the most crucial component of any large-scale data mining project.  Regression: The regression technique is used to predict a range of numeric values, such as temperature, stock prices, sales, based on a particular data set. Now that we have all the crucial terms in place let’s look at how a typical Data MIning project works. How Does Data Mining Work? Any data mining project typically starts with finding out the scope. It is essential to ask the right questions and collect the correct dataset to answer those questions. Then, the data is prepared for analysis, and the final success of the project depends highly on the quality of the data. Poor data leads to inaccurate and faulty results, making it even more important to diligently prepare the data and remove all the anomalies.  The Data Mining process typically works through the following six steps:  1. Understanding the Business This stage involves developing a comprehensive understanding of the project at hand, including the current business situation, the business objectives, and the metrics for success.  2. Understanding the data Once the project’s scope and business goals are clear, next comes the task of gathering all the relevant data that will be needed to solve the problem. This data is collected from all available sources, including databases, cloud storage, and silos. 3. Preparing the data Once the data from all the sources is collected, it’s time to prepare the data. In this step, data cleaning, normalization, filling missing values, and such tasks are performed. This step aims to bring all the data in the most appropriate and standardized format to carry out further processes.  4. Developing the model Now, after bringing all the data into a format fit for analysis, the next step is developing the models. For this, programming and algorithms are used to come up with a model that can identify trends and patterns from the data at hand.  5. Testing and evaluating the model Modeling is done based on the data at hand. However, to test the models, you need to feed it with other data and see if it is throwing the relevant output or not. Determining how well the model is delivering new results will help in achieving business goals. This is generally an iterative process that repeats till the best algorithm has been found to solve the problem at hand.  6. Deployment Once the model has been tested and iteratively improved, the last step is deploying the model and making the results of the data mining project available to all the stakeholders and decision-makers.  Throughout the entire Data Mining lifecycle, the data miners need to maintain a close collaboration between domain experts and other team members to keep everyone in the loop and ensure that nothing slips through the cracks.  Advantages of Data Mining for Businesses Businesses now deal with heaps of data on a daily basis. This data is only increasing as time passes, and there’s no way that the volume of this data will ever decrease. As a result, companies don’t have any other choice than to be data-driven. In today’s world, the success of any business largely depends on how well they can understand their data, derive insights from it, and make actionable predictions. Data Mining truly empowers businesses to improve their future by analyzing their past data trends and making accurate predictions about what is likely to happen.  For instance, Data Mining can tell a business about their prospects that are likely to become profitable customers based on past data and are most likely to engage with a specific campaign or offer. With this knowledge, businesses can increase their ROI by offering only those prospects that are likely to respond and become valuable customers. All in all, data mining offers the following benefits to any business:  Understanding customer preferences and sentiments. Acquiring new customers and retaining existing ones.  Improving up-selling and cross-selling.  Increasing loyalty among customers.  Improving ROI and increasing business revenue.  Detecting fraudulent activities and identifying credit risks.  Monitoring operational performance. By using data mining techniques, businesses can base their decisions on real-time data and intelligence, rather than just instincts or gut, thereby ensuring that they keep delivering results and stay ahead of their competition.  The Future of Data Mining Data mining, and even other fields of data sciences, has an extremely bright future, owing to the ever-increasing amount of data in the world. In the last year itself, our accumulated data grew from 4.4 zettabytes to 44 zettabytes. If you are enthusiastic about data science or data mining, or anything to do with data, this is the best time to be alive. Since we’re witnessing a data revolution, it’s the ideal time to get onboard and sharpen your data expertise and skills. Companies all around the globe are almost always on the lookout for data experts with enough skills to help them make sense of their data. So, if you want to start your journey in the data world, now is a perfect time!  At upGrad, we have mentored students from all over the world, belonging to 85+ countries, and helped them start their journeys with all the confidence and skills they require. Our courses are designed to offer both theoretical knowledge as well as hands-on expertise to the students belonging from any background. We understand that data science is truly the need of the hour, and we encourage motivated students from various backgrounds to commence their journey with our 360-degree career assistance.  You could also opt for the integrated Master of Science in Data Science degree offered by upGrad in conjunction with IIT Bengaluru and Liverpool John Moore’s University. This course integrates the previously discussed executive PG program with features such as a Python programming Bootcamp. Upon completion, a student receives a valuable NASSCOM certification that helios in global access to job opportunities.

TensorFlow is an open-source library used to build machine learning models. It is an incredible platform for anyone passionate about working with machine learning and artificial intelligence. Furthermore, with the steady growth that the machine learning market is witnessing, tools like TensorFlow have come to the spotlight as tech companies explore the diverse capabilities of AI technology. No doubt, the global machine learning market is projected to reach a valuation of US$ 117.19 billion by 2027. But on the outset, it is pertinent to know what is TensorFlow and what makes it a popular choice among developers worldwide.  What is TensorFlow? TensorFlow is an end-to-end open-source platform for machine learning with a particular focus on deep neural networks. Deep learning is a subset of machine learning that involves the analysis of large-scale unstructured data. Deep learning differs from traditional machine learning in that the latter typically deals with structured data.  TensorFlow boasts of a flexible and comprehensive collection of libraries, tools, and community resources. It lets developers build and deploy state-of-the-art machine learning-powered applications. One of the best things about TensorFlow is that it uses Python to provide a convenient front-end API for building applications while executing them in high-performance, optimized C++.  The Google Brain team initially developed the TensorFlow Python deep-learning library for internal use. Since then, the open-source platform has seen tremendous growth in usage in R&D and production systems.  Some TensorFlow Basics Now that we have a fundamental idea of what is TensorFlow, it’s time to delve into some more details about the platform.  Following is a brief overview of some basic concepts related to TensorFlow. We’ll begin with tensors – the core components of TensorFlow from which the platform derives its name. Tensors In the TensorFlow Python deep-learning library, a tensor is an array that represents the types of data. Unlike a one-dimensional vector or array or a two-dimensional matrix, a tensor can have n dimensions. In a tensor, the values hold identical data types with a known shape. The shape represents dimensionality. Thus, a vector will be a one-dimensional tensor, a matrix is a two-dimensional tensor, and a scalar would be a zero-dimensional tensor. Source Shape In the TensorFlow Python library, shape refers to the dimensionality of the tensor.  Source In the above image, the shape of the tensor is (2,2,2). Type The type represents the kind of data that the values in a tensor hold. Typically, all values in a tensor hold an identical data type. The datatypes in TensorFlow are as follows: integers floating point unsigned integers booleans strings integer with quantized ops complex numbers Graph A graph is a set of computations that take place successively on input tensors. It comprises an arrangement of nodes representing the mathematical operations in a model. Session A session in TensorFlow executes the operations in the graph. It is run to evaluate the nodes in a graph.  Operators Operators in TensorFlow are pre-defined mathematical operations.  How Do Tensors Work? In TensorFlow, data flow graphs describe how data moves through a series of processing nodes. TensorFlow uses data flow graphs to build models. The graph computations in TensorFlow are facilitated through the interconnections between tensors.  The n-dimensional tensors are fed to the neural network as input, which goes through several operations to give the output. The graphs have a network of nodes, where each node represents a mathematical operation. But the edge between the nodes is a multidimensional data array or a tensor. A TensorFlow session allows the execution of graphs or parts of graphs. For that, the session allocates resources on one or more machines and holds the actual values of intermediate results and variables. Source TensorFlow applications can be run on almost any convenient target, which could be CPUs, GPUs, a cluster in the cloud, a local machine, or Android and iOS devices. TensorFlow Computation Graph  A computation graph in TensorFlow is a network of nodes where each node operates multiplication, addition, or evaluates some multivariate equation. In TensorFlow, codes are written to create a graph, run a session, and execute the graph. Every variable we assign becomes a node where we can perform mathematical operations such as multiplication and addition.  Here’s a simple example to show the creation of a computation graph: Suppose we want to perform the calculation: F(x,y,z) = (x+y)*z.  The three variables x, y, and z will translate into three nodes in the graph shown below: Source Steps of building the graph: Step 1: Assign the variables. In this example, the values are: x = 1, y = 2, and z = 3 Step 2: Add x and y. Step 3: Multiply z with the sum of x and y. Finally, we get the result as ‘9.’ In addition to the nodes where we have assigned the variables, the graph has two more nodes – one for the addition operation and another for the multiplication operation. Hence, there are five nodes in all. Fundamental Programming Elements in TensorFlow In TensorFlow, we can assign data to three different types of data elements – constants, variables, and placeholders. Let’s look at what each of these data elements represents. 1. Constants As evident from the name, constants are parameters with unchanging values. In TensorFlow, a constant is defined using the command tf.constant(). During computation, the values of constants cannot be changed. Here’s an example: c = tf.constant(2.0,tf.float32) d = tf.constant(3.0) Print (c,d) 2. Variables Variables allow the addition of new parameters to the graph. The tf.variable() command defines a variable that must be initialized before running the graph in a session. Here’s an example: Y = tf.Variable([.4],dtype=tf.float32) a = tf.Variable([-.4],dtype=tf.float32) b = tf.placeholder(tf.float32) linear_model = Y*b+a 3. Placeholders Using placeholders, one can feed data into a model from the outside. It allows later assignment of values. The command tf.placeholder() defines a placeholder. Here’s an example: c = tf.placeholder(tf.float32) d = c*2 result = sess.run(d,feed_out={c:3.0}) The placeholder is primarily used to feed a model. Data from outside is fed to a graph using a variable name (the variable name in the above example is feed_out). Subsequently while running the session, we specify how we want to feed the data to the model. Example of a session: The execution of the graph is done by calling a session. A session is run to evaluate the graph’s nodes, called the TensorFlow runtime. The command sess = tf.Session() creates a session. Example: x = tf.constant(3.0) y = tf.constant(4.0) z = x+y sess = tf.Session() #Launching Session print(sess.run(z)) #Evaluating the Tensor z In the above example, there are three nodes – x, y, and z. The node ‘z’ is where the mathematical operation is carried out, and subsequently, the result is obtained. Upon creating a session and running the node z, first, the nodes x and y will be created. Then, the addition operation will take place at node z. Hence, we will obtain the result ‘7’. Advance Your Career in ML and Deep Learning with upGrad Looking for the best place to know more about what is TensorFlow? Then upGrad is here to assist you in your learning journey. With a learner base covering 85+ countries, upGrad is South Asia’s largest higher EdTech platform that has impacted more than 500,000 working professionals globally. With world-class faculty, collaborations with industry partners, the latest technology, and the most up-to-date pedagogic practices, upGrad ensures a wholesome and immersive learning experience for its 40,000+ paid learners globally. 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