{"id":4819,"date":"2026-05-18T09:55:49","date_gmt":"2026-05-18T09:55:49","guid":{"rendered":"https:\/\/cloudxlab.com\/blog\/?p=4819"},"modified":"2026-05-19T08:30:07","modified_gmt":"2026-05-19T08:30:07","slug":"why-ml-tutorials-dont-work","status":"publish","type":"post","link":"https:\/\/cloudxlab.com\/blog\/why-ml-tutorials-dont-work\/","title":{"rendered":"I Watched 200 Hours of ML Tutorials – Here’s What Finally Changed"},"content":{"rendered":"\n

The Night I Realized I Hadn’t Actually Learned Anything<\/strong><\/h2>\n\n\n\n

It was a Tuesday evening, about eight months into what I had been calling my “machine learning journey.”<\/p>\n\n\n\n

A colleague who knew I had been studying ML seriously casually forwarded me a small dataset of customer transactions and said, “Hey, can you build a quick churn prediction model on this? Nothing fancy. Just want to see if there’s a pattern.” I opened my laptop with confidence. At this point, I had watched somewhere north of 200 hours of machine learning tutorials. I had completed three full courses on two different platforms. I had a notes folder with over 80 pages of summarized concepts. I understood gradient descent. I could explain what a confusion matrix was. I had watched someone build a churn model on YouTube just three weeks earlier.<\/p>\n\n\n\n

I stared at the blank Jupyter notebook for forty-five minutes and produced nothing useful.<\/p>\n\n\n\n

Not because the problem was hard. Not because I was missing tools. But because I genuinely did not know how to start<\/em> when nobody was guiding me step by step. Every tutorial I had ever watched began with a cleaned dataset, a clear objective, and an instructor who already knew the answer. I had learned to follow. I had never learned to lead.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n\n\n\n\n

That night was one of the most clarifying and humbling moments of my learning journey. And, having talked to thousands of learners since then, I’ve come to realise: it is not a personal failure. It is the entirely predictable outcome of a broken learning method.<\/p>\n\n\n\n

This is the story of what the tutorial trap actually is, why it catches almost everyone, and what genuinely changed when I shifted to a different way of learning.<\/p>\n\n\n\n

What the Tutorial Trap Actually Is<\/strong><\/h2>\n\n\n\n

The tutorial trap is not about watching too many videos. It is about a specific cognitive illusion that passive learning creates, and it is one of the most well-documented phenomena in learning science.<\/p>\n\n\n\n

It works like this.<\/p>\n\n\n\n

When you watch an expert walk you through a solution, your brain follows along. Each step makes sense. The code is logical. The reasoning is clear. As you watch, you experience genuine comprehension and the feeling of understanding. Your brain registers this feeling as learning having occurred.<\/p>\n\n\n\n

But there is a crucial difference between recognising a correct solution when you see one<\/em> and being able to produce a solution when you need one<\/em>.<\/p>\n\n\n\n

Tutorials train the first skill almost exclusively. Real work requires the second.<\/p>\n\n\n\n

Psychologists call this the fluency illusion,<\/strong> the mistaken belief that because you can follow something easily, you could also generate it independently. It is why students who re-read their notes before an exam often feel prepared but underperform. It is why someone can watch a tutorial on building a neural network and feel confident right up until the moment they open a blank notebook.<\/p>\n\n\n\n

In machine learning specifically, the fluency illusion is particularly powerful because:<\/p>\n\n\n\n

The code looks clean in tutorials.<\/strong> Real datasets are messy, inconsistent, full of missing values and formatting errors. Tutorial datasets are pre-cleaned by the instructor before the video starts. You never see that work, so you never learn how to do it.<\/p>\n\n\n\n

The problem is always clearly defined.<\/strong> The instructor tells you exactly what you’re predicting, which columns are features, and what metric to optimise. In real work, figuring those things out is most of the job.<\/p>\n\n\n\n

The solution always works.<\/strong> Tutorials are recorded after the instructor has already solved the problem. You watch a confident, linear path from problem to solution. Real machine learning is iterative, non-linear, and full of dead ends that you have to back out of.<\/p>\n\n\n\n

The result is a learner who has watched hundreds of hours of correct, polished ML work and has no experience with the messy, uncertain, iterative process that actual ML work involves.<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

Why Most Learning Platforms Make This Worse, Not Better<\/strong><\/h2>\n\n\n\n

Here is an uncomfortable truth about the online learning industry: completion rates and engagement metrics are optimized for the feeling of progress, not actual skill development.<\/p>\n\n\n\n

Platforms want you to finish courses. They want you to feel good about your learning. They want high ratings and positive reviews.<\/p>\n\n\n\n

The problem is that the things that make learning feel good: clear explanations, well-structured content, pre-cleaned datasets, guided walkthroughs with the solution visible, are often the exact opposite of what produces durable skill.<\/p>\n\n\n\n

Real skill is built through struggle. Through attempting a problem you’re not sure how to solve, getting stuck, thinking carefully, trying something, evaluating whether it worked, and iterating. This process is uncomfortable. It does not feel like smooth progress. It does not generate good immediate reviews.<\/p>\n\n\n\n

But it is the only process that produces genuine capability.<\/p>\n\n\n\n

This is not a new insight; it is backed by decades of research in learning science. The testing effect<\/strong> (also called retrieval practice) consistently shows that attempting to produce an answer even before you know it confidently produces far better long-term retention and skill transfer than studying the same material passively. The research is unambiguous. Doing beats watching, every single time, for skill acquisition.<\/p>\n\n\n\n

The problem is that most platforms are not designed around this research. They are designed around what sells.<\/p>\n\n\n\n

The Specific Mistakes I Was Making (And That Most Beginners Make)<\/strong><\/h2>\n\n\n\n

Looking back at those eight months of tutorial-heavy learning, the patterns are clear.<\/p>\n\n\n\n

I was optimising for coverage, not depth.<\/strong> I wanted to learn as many topics as possible, supervised learning, unsupervised learning, neural networks, NLP, and computer vision. I skimmed the surface of everything and went deep on almost nothing. Real competence requires going deep enough on core skills that you can apply them without guidance. I had gone wide instead.<\/p>\n\n\n\n

I was skipping the boring parts.<\/strong> Data cleaning and preprocessing make up 60\u201380% of real data science work. Every tutorial skips or minimises this because it is not exciting to watch. I had absorbed this bias. I thought the interesting part was building models, not preparing data. This was completely backwards.<\/p>\n\n\n\n

I was watching instead of pausing and trying.<\/strong> A better approach to any tutorial is to pause the video before the instructor shows the solution, attempt it yourself, and only then watch what they did. I never did this. I watched straight through, feeling the comprehension, absorbing none of the productive struggle.<\/p>\n\n\n\n

I was not building anything that didn’t already have instructions.<\/strong> Every project I had done was either from a tutorial or from Kaggle competitions with detailed starter notebooks. I had never started with a raw problem and a blank file and figured out my own path.<\/p>\n\n\n\n

I was mistaking notes for knowledge.<\/strong> My 80 pages of summarised concepts were, in hindsight, a monument to the fluency illusion. I had written down things I understood when I read them. I had never tested whether I could use them.<\/p>\n\n\n\n

What Actually Changed: The Shift to Hands-On Lab Practice<\/strong><\/h2>\n\n\n\n

The turning point came when I joined CloudxLab<\/a>.<\/p>\n\n\n\n

What struck me immediately was that it was different from other platforms, but not in the way I expected. I had assumed that the answer to passive learning was being left alone with raw problems and no help. CloudxLab proved that assumption wrong.<\/p>\n\n\n\n

The program starts from the absolute basics. Every concept, the mathematics behind the algorithm, the intuition for why it works, and the code that implements it is taught with genuine clarity in live weekend sessions. The lead instructor takes personal accountability for whether you understand. If something does not click, it gets explained again from a different angle. A 24\u00d77 doubt forum means you can ask questions at any hour and get real answers. A community of fellow learners means you are never isolated.<\/p>\n\n\n\n

The support is real, structured, and always available.<\/p>\n\n\n\n

But that support exists to enable doing not to replace it.<\/p>\n\n\n\n

From the very first module, concepts are introduced and then immediately applied to real problems. Not toy datasets with obvious answers, but actual industry-relevant challenges where you have to make decisions: which features matter, which metric to optimise, how to handle missing values, what to do when your model behaves unexpectedly. The cloud lab environment is pre-configured and accessible from any browser. You don’t waste time on setup. You spend your time on the problem.<\/p>\n\n\n\n

What this produces is something tutorials never could: the experience of solving, not just following.<\/p>\n\n\n\n

I got stuck on data cleaning issues that would have been invisible in any tutorial I had watched. I chose the wrong evaluation metric on an early project and had to rethink my entire approach. I built models that overfit and had to understand why. But every time I hit a wall, the support structure was right there. The doubt forum, the instructor’s session, and the community. The struggle never became abandonment, because there was always somewhere to turn.<\/p>\n\n\n\n

And by working through those moments, not around them, something changed that eight months of tutorials had never touched: I stopped recognizing correct solutions when I saw them and started generating them independently.<\/p>\n\n\n\n

The 34+ industry projects across the programme build this capability progressively. Each project is harder than the last. Each one requires applying what you have learned to a new domain, a new dataset, a new business question. By the time I reached my capstone project, a substantial, self-directed piece of work I designed and built entirely from scratch, I was not starting cold. I had already solved dozens of real problems. The capstone brought everything together: my own problem statement, my own data pipeline, my own model choices, my own evaluation.<\/p>\n\n\n\n

Something I could show an employer and say with full confidence: I built this. Every decision in it was mine.<\/p>\n\n\n\n

That is what structured, hands-on learning actually produces. Not the memory of watching someone else build something. The confidence that comes from having built it yourself with the right guidance, exactly when you needed it.<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

The Four Principles That Actually Build ML Skills<\/strong><\/h2>\n\n\n\n

Based on what I learned and what CloudxLab’s curriculum is built around, here are the four principles that produce genuine machine learning capability.<\/p>\n\n\n\n

Principle 1: Teach the Why Before the How<\/strong><\/h3>\n\n\n\n

Most platforms begin with a tool such as scikit-learn. Here is what it can do. CloudxLab begins with the concept: here is the mathematical intuition, here is why this algorithm works, here is what it is actually doing under the hood. Only then does the code appear.<\/p>\n\n\n\n

This order matters enormously. When you understand why<\/em> an algorithm works, you know when to use it, when not to, and what to look for when it behaves unexpectedly. When you only know how<\/em> to call a function, you are stuck the moment the scenario changes from what the tutorial showed you.<\/p>\n\n\n\n

Understanding the why taught clearly, from first principles, before the library, is what converts a tool user into an engineer.<\/p>\n\n\n\n

Principle 2: Work With Real Data From the Start<\/strong><\/h3>\n\n\n\n

The ability to take a raw, real-world dataset with missing values, inconsistent formatting, ambiguous columns, and unclear provenance and turn it into something a model can learn from is one of the most valuable and most underdeveloped skills in most beginners.<\/p>\n\n\n\n

You cannot develop this skill by working with pre-cleaned tutorial data. You develop it by working with real data regularly, building intuition for the patterns of messiness and the appropriate responses to each. CloudxLab’s projects use industry-relevant datasets that reflect what data actually looks like when it arrives from the real world.<\/p>\n\n\n\n

Principle 3: Evaluate Everything, Assume Nothing<\/strong><\/h3>\n\n\n\n

One of the hallmarks of an experienced ML practitioner is a deep habit of evaluation, constantly checking whether the model is doing what you think it is, whether the metric you are optimising actually reflects what you care about, and whether your validation approach is sound.<\/p>\n\n\n\n

Beginners tend to train a model, see an accuracy number, and feel done. Experienced practitioners know that high accuracy on the wrong metric is worthless. That a model performing well on validation data that leaked from the training set is not performing at all. That evaluation is not the end of the process; it is woven through every step.<\/p>\n\n\n\n

This habit is built through practice on real problems where the consequences of getting the evaluation wrong are visible. It cannot be built by watching someone else evaluate an already-correct model.<\/p>\n\n\n\n

Principle 4: Build, Get Stuck, Get Support, and Solve<\/strong><\/h3>\n\n\n\n

The most valuable learning experiences in ML are the ones where something does not go as expected, and you have to figure out why.<\/p>\n\n\n\n

A model that overfits teaches you more about regularisation than a lecture on regularisation ever will. A feature engineering decision that introduces data leakage and the process of diagnosing and fixing it teaches you more about data integrity than any textbook.<\/p>\n\n\n\n

CloudxLab’s projects are designed to put you in exactly these situations. Real challenges where the path is not pre-resolved and where thinking is required. But crucially, the doubt support and instructor access mean you always have a route out of genuine confusion. The thinking stays yours. The getting-unstuck never has to happen alone.<\/p>\n\n\n\n

What the Research Says About How People Actually Develop Expertise<\/strong><\/h2>\n\n\n\n

The science of expertise and skill acquisition has been studied seriously for decades, and its conclusions are consistent.<\/p>\n\n\n\n

Expertise in any cognitive domain, such as chess, medicine, programming, or data science, is not built by accumulating information. It is built by developing mental models<\/strong>: rich, interconnected representations of how a domain works that allow you to recognise patterns, diagnose problems, and generate solutions in novel situations.<\/p>\n\n\n\n

Mental models are not built by reading or watching. They are built by doing specifically, by working on problems at the edge of your current capability, receiving feedback on your performance, and adjusting. This process is sometimes called deliberate practice<\/strong>, and it is the mechanism by which all genuine expertise is developed.<\/p>\n\n\n\n

Notice what is not on that list: watching videos, reading notes, or following guided walkthroughs.<\/p>\n\n\n\n

This does not mean conceptual learning is useless. Understanding the theory of gradient descent matters, but it matters because<\/em> it helps you diagnose why your model is not converging, not because knowing it in the abstract is valuable. Theory becomes powerful when it is connected to practice. Practice without theory is blind. Theory without practice is inert.<\/p>\n\n\n\n

The best learning environments weave them together: concepts taught clearly, immediately applied in a real context, with feedback and support that connects the result back to the concept.<\/p>\n\n\n\n

This is the design philosophy behind CloudxLab’s curriculum. The instructor teaches the concept. The live session makes sure you understand it. The lab is where it becomes real. The doubt, support and community make sure you never get stuck alone.<\/p>\n\n\n\n

The Practical Shift: What to Do Differently Starting Now<\/strong><\/h2>\n\n\n\n

If you recognise yourself in the tutorial trap, here is the specific, actionable shift to make.<\/p>\n\n\n\n

Stop adding more passive content before applying what you already have.<\/strong> You almost certainly have enough conceptual knowledge to start building something. The deficit is not in information; it is in practice. Stop accumulating and start applying.<\/p>\n\n\n\n

Find real data on a problem you actually care about.<\/strong> Kaggle, government open data portals, your own company’s data, find something real. Spend a session just exploring it: what is in it, what is missing, what patterns you can see. Build the habit of sitting with data before writing any modelling code.<\/p>\n\n\n\n

Reframe your question from “what should I learn next?” to “what should I build next?”<\/strong> Pick a problem. Define what success looks like. Attempt it. Get unstuck with resources and support, but keep the problem-first direction non-negotiable.<\/p>\n\n\n\n

Choose a structured programme with real support, not more self-study.<\/strong> Isolated self-study compounds the tutorial trap because there is nobody to tell you when your approach is wrong, nobody to answer your doubts at 11 pm, and no milestones to keep you accountable. A programme with live instruction, 24\u00d77 doubt support, peer community, and real assessed projects is not a luxury. It is the structure that converts practice into genuine, calibrated skill.<\/p>\n\n\n\n

Work toward a defined endpoint: the capstone.<\/strong> One of the most motivating things about a well-designed programme is knowing what you are working toward. The capstone project is that endpoint: a real, substantial piece of work you design, build, and own entirely. Every project that comes before it prepares you for it. Having that on the horizon changes how you engage with every step of the journey.<\/p>\n\n\n\n

Ten Months Later<\/strong><\/h2>\n\n\n\n

Ten months after that humbling Tuesday evening with a blank notebook and a dataset I couldn’t work with, I completed my capstone project, a full end-to-end machine learning pipeline on a real problem, designed by me, built by me, evaluated by me, and backed by an IIT Roorkee PG Certificate that I had genuinely earned.<\/p>\n\n\n\n

I went back to that same colleague. I asked her if she had any more data problems she wanted explored. She had three. I worked through all of them over the following two weeks, not perfectly, not without getting stuck, but from start to finish, with real, useful output.<\/p>\n\n\n\n

The difference between those two versions of me was not more information. I had plenty of information after 200 hours of tutorials.<\/p>\n\n\n\n

The difference was structured, progressive, hands-on learning with every concept taught clearly from first principles, every doubt answered, and real problems that demanded real thinking at every stage.<\/p>\n\n\n\n

If you are in the tutorial trap right now, if you have been learning for months and still cannot build independently, the shift is not a harder tutorial. It is a completely different kind of learning.<\/p>\n\n\n\n

No more watching. More building. With the right guidance alongside you every step of the way.<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

Start Building With CloudxLab<\/strong><\/h2>\n\n\n\n

The Post Graduate Certificate in AI & Machine Learning<\/a> by CloudxLab, delivered in partnership with E&ICT Academy, IIT Roorkee, is built around exactly this philosophy. Every concept is taught from first principles. Every doubt answered through live sessions and 24\u00d77 support. 34+ real-world industry problems to solve. And a capstone project that is entirely your own work.<\/p>\n\n\n\n

Whether you are starting from zero programming experience or levelling up into deep learning and agentic AI, the program takes you from where you are to where you want to be, with the credentials, skills, and portfolio to prove it.<\/p>\n\n\n\n

Explore the Program at CloudxLab<\/strong><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

The Night I Realized I Hadn’t Actually Learned Anything It was a Tuesday evening, about eight months into what I had been calling my “machine learning journey.” A colleague who knew I had been studying ML seriously casually forwarded me a small dataset of customer transactions and said, “Hey, can you build a quick churn … Continue reading “I Watched 200 Hours of ML Tutorials – Here’s What Finally Changed”<\/span><\/a><\/p>\n","protected":false},"author":50,"featured_media":4829,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[28],"tags":[273,102,276,16,274,275],"yoast_head":"\nI Watched 200 Hours of ML Tutorials - Here's What Finally Changed<\/title>\n<meta name=\"description\" content=\"I spent 200 hours watching ML tutorials and couldn't build a single thing. Here's the one shift that finally helped me learn ML properly.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/cloudxlab.com\/blog\/why-ml-tutorials-dont-work\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"I Watched 200 Hours of ML Tutorials - Here's What Finally Changed\" \/>\n<meta property=\"og:description\" content=\"I spent 200 hours watching ML tutorials and couldn't build a single thing. Here's the one shift that finally helped me learn ML properly.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/cloudxlab.com\/blog\/why-ml-tutorials-dont-work\/\" \/>\n<meta property=\"og:site_name\" content=\"CloudxLab Blog\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/cloudxlab\" \/>\n<meta property=\"article:published_time\" content=\"2026-05-18T09:55:49+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-05-19T08:30:07+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/blog.cloudxlab.com\/wp-content\/uploads\/2026\/05\/featured-200hr.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1107\" \/>\n\t<meta property=\"og:image:height\" content=\"559\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@CloudxLab\" \/>\n<meta name=\"twitter:site\" content=\"@CloudxLab\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\">\n\t<meta name=\"twitter:data1\" content=\"15 minutes\">\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebSite\",\"@id\":\"https:\/\/cloudxlab.com\/blog\/#website\",\"url\":\"https:\/\/cloudxlab.com\/blog\/\",\"name\":\"CloudxLab Blog\",\"description\":\"Learn AI, Machine Learning, Deep Learning, Devops & Big Data\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":\"https:\/\/cloudxlab.com\/blog\/?s={search_term_string}\",\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-US\"},{\"@type\":\"ImageObject\",\"@id\":\"https:\/\/cloudxlab.com\/blog\/why-ml-tutorials-dont-work\/#primaryimage\",\"inLanguage\":\"en-US\",\"url\":\"https:\/\/cloudxlab.com\/blog\/wp-content\/uploads\/2026\/05\/featured-200hr.png\",\"contentUrl\":\"https:\/\/cloudxlab.com\/blog\/wp-content\/uploads\/2026\/05\/featured-200hr.png\",\"width\":1107,\"height\":559},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/cloudxlab.com\/blog\/why-ml-tutorials-dont-work\/#webpage\",\"url\":\"https:\/\/cloudxlab.com\/blog\/why-ml-tutorials-dont-work\/\",\"name\":\"I Watched 200 Hours of ML Tutorials - Here's What Finally Changed\",\"isPartOf\":{\"@id\":\"https:\/\/cloudxlab.com\/blog\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/cloudxlab.com\/blog\/why-ml-tutorials-dont-work\/#primaryimage\"},\"datePublished\":\"2026-05-18T09:55:49+00:00\",\"dateModified\":\"2026-05-19T08:30:07+00:00\",\"author\":{\"@id\":\"https:\/\/cloudxlab.com\/blog\/#\/schema\/person\/281cb842adba701236b004e97ac0bd13\"},\"description\":\"I spent 200 hours watching ML tutorials and couldn't build a single thing. Here's the one shift that finally helped me learn ML properly.\",\"breadcrumb\":{\"@id\":\"https:\/\/cloudxlab.com\/blog\/why-ml-tutorials-dont-work\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/cloudxlab.com\/blog\/why-ml-tutorials-dont-work\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/cloudxlab.com\/blog\/why-ml-tutorials-dont-work\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"item\":{\"@type\":\"WebPage\",\"@id\":\"https:\/\/cloudxlab.com\/blog\/\",\"url\":\"https:\/\/cloudxlab.com\/blog\/\",\"name\":\"Home\"}},{\"@type\":\"ListItem\",\"position\":2,\"item\":{\"@id\":\"https:\/\/cloudxlab.com\/blog\/why-ml-tutorials-dont-work\/#webpage\"}}]},{\"@type\":\"Person\",\"@id\":\"https:\/\/cloudxlab.com\/blog\/#\/schema\/person\/281cb842adba701236b004e97ac0bd13\",\"name\":\"Nistha Saini\",\"image\":{\"@type\":\"ImageObject\",\"@id\":\"https:\/\/cloudxlab.com\/blog\/#personlogo\",\"inLanguage\":\"en-US\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/7bb4e5e747a5c5022b1b32411c60766a?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/7bb4e5e747a5c5022b1b32411c60766a?s=96&d=mm&r=g\",\"caption\":\"Nistha Saini\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","_links":{"self":[{"href":"https:\/\/cloudxlab.com\/blog\/wp-json\/wp\/v2\/posts\/4819"}],"collection":[{"href":"https:\/\/cloudxlab.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cloudxlab.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cloudxlab.com\/blog\/wp-json\/wp\/v2\/users\/50"}],"replies":[{"embeddable":true,"href":"https:\/\/cloudxlab.com\/blog\/wp-json\/wp\/v2\/comments?post=4819"}],"version-history":[{"count":6,"href":"https:\/\/cloudxlab.com\/blog\/wp-json\/wp\/v2\/posts\/4819\/revisions"}],"predecessor-version":[{"id":4862,"href":"https:\/\/cloudxlab.com\/blog\/wp-json\/wp\/v2\/posts\/4819\/revisions\/4862"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/cloudxlab.com\/blog\/wp-json\/wp\/v2\/media\/4829"}],"wp:attachment":[{"href":"https:\/\/cloudxlab.com\/blog\/wp-json\/wp\/v2\/media?parent=4819"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cloudxlab.com\/blog\/wp-json\/wp\/v2\/categories?post=4819"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cloudxlab.com\/blog\/wp-json\/wp\/v2\/tags?post=4819"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}