{"id":13031,"date":"2026-07-08T20:52:23","date_gmt":"2026-07-08T15:22:23","guid":{"rendered":"https:\/\/www.scaler.com\/blog\/?p=13031"},"modified":"2026-07-08T20:52:27","modified_gmt":"2026-07-08T15:22:27","slug":"ai-ml-syllabus","status":"publish","type":"post","link":"https:\/\/www.scaler.com\/blog\/ai-ml-syllabus\/","title":{"rendered":"The Complete AI & ML 2026 Syllabus Before You Enroll in Any Course"},"content":{"rendered":"\n

Every AI\/ML syllabus floating around the internet seems to either drown you in math you’ll never directly use, or skip straight to \u201cbuild a chatbot in 10 minutes\u201d and call it a curriculum. Neither actually gets you job-ready. Somewhere between \u201cprove this theorem\u201d and \u201ccopy-paste this API call\u201d is the syllabus that actually works, and that’s what this is.<\/p>\n\n\n\n

Eight modules, math through generative AI and MLOps, in the order that makes sense rather than the order a YouTube algorithm thinks will keep you watching. We’ll be honest about how much math you genuinely need (less than you fear, more than zero), where deep learning actually earns its keep, and why generative AI isn’t optional anymore in a 2026 curriculum, whether you like that or not.<\/p>\n\n\n\n

If you’d rather have a structured version of this with mentorship and real project feedback instead of assembling it from forty open browser tabs, Scaler’s AI & Machine Learning Course <\/a>covers most of what’s below end to end. We’ll point to more specific options as the relevant modules come up.<\/p>\n\n\n\n

<\/span>What Do You Learn in an AI & ML Course?<\/strong><\/span><\/h2>\n\n\n\n

First, the terminology untangling that should honestly happen before anyone signs up for anything. Artificial Intelligence is the broad umbrella, any system that mimics intelligent behavior. Machine Learning is a subset of AI, specifically systems that learn patterns from data rather than following hardcoded rules. Deep Learning is a subset of ML, using layered neural networks for that learning. Generative AI is a further subset, models that don’t just classify or predict but actually generate new content, text, images, code. Nested dolls, basically, each one smaller and more specific than the one containing it.<\/p>\n\n\n\n

A complete 2026 syllabus needs to cover all four layers, not just the classical ML middle that used to be \u201cthe whole curriculum\u201d a few years back. That means math foundations, programming, classical ML, deep learning, NLP and computer vision, generative AI and LLMs, and increasingly MLOps, because a model that never makes it to production isn’t actually useful to anyone except the person who built it for a grade.<\/p>\n\n\n\n

For a longer dedicated breakdown of artificial intelligence as its own subject before ML narrows the focus, the Scaler Artificial Intelligence Syllabus<\/a> is a good companion read.<\/p>\n\n\n\n

<\/span>AI ML Syllabus 2026 at a Glance <\/strong><\/span><\/h2>\n\n\n\n

Here’s the whole thing up front, because nobody enjoys scrolling six sections deep just to find out where generative AI actually shows up.<\/p>\n\n\n\n

Module<\/strong><\/td>Core Topics<\/strong><\/td>Tools<\/strong><\/td>Outcome<\/strong><\/td><\/tr>
1. Math & Statistics<\/td>Linear algebra, calculus, probability, statistics<\/td>NumPy, basic math libraries<\/td>Understand what’s happening inside an ML algorithm, not just call .fit()<\/td><\/tr>
2. Python & Data Handling<\/td>Python, NumPy, pandas, matplotlib, data cleaning<\/td>Python, Jupyter, pandas, matplotlib<\/td>Load, clean, and explore real datasets confidently<\/td><\/tr>
3. Classical Machine Learning<\/td>Supervised\/unsupervised learning, regression, classification, clustering<\/td>scikit-learn<\/td>Build and evaluate working ML models on tabular data<\/td><\/tr>
4. Deep Learning & Neural Networks<\/td>Neural nets, CNNs, RNNs, backpropagation<\/td>TensorFlow, PyTorch<\/td>Train models on image and sequence data<\/td><\/tr>
5. NLP & Computer Vision<\/td>Text processing, embeddings, transformers, image tasks<\/td>Hugging Face, OpenCV<\/td>Build applications that understand text or images<\/td><\/tr>
6. Generative AI & LLMs<\/td>LLMs, prompting, RAG, fine-tuning basics<\/td>OpenAI API, LangChain, Hugging Face<\/td>Build LLM-powered applications, not just call an API blindly<\/td><\/tr>
7. MLOps & Deployment<\/td>Model deployment, monitoring, pipelines, reproducibility<\/td>Docker, MLflow, cloud platforms<\/td>Ship a model into production and keep it working<\/td><\/tr>
8. Projects & Portfolio<\/td>ML model, deep-learning app, LLM-powered project<\/td>Everything above, combined<\/td>Proof you can actually do the job, not just pass a quiz on it<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

For a structured walkthrough of this same ground with mentorship attached, Scaler’s Academy programs<\/a> are worth a look, and the dedicated Machine Learning Syllabus<\/a> goes deeper on modules 2 and 3 specifically if that’s where you want to start.<\/p>\n\n\n\n

<\/span>Module 1: Math & Statistics Foundations<\/strong><\/span><\/h2>\n\n\n\n

Deep breaths. You do not need to relearn calculus from a textbook cover to cover, and anyone who tells you otherwise is gatekeeping for sport. You do need a working, applied understanding of a handful of specific concepts, because they show up constantly in how ML actually behaves.<\/p>\n\n\n\n

What’s actually worth learning here<\/strong><\/h3>\n\n\n\n

\u2022        Linear algebra: vectors, matrices, matrix multiplication, dot products. This is the language neural networks are literally written in<\/p>\n\n\n\n

\u2022        Calculus (just enough): derivatives and gradients, specifically because gradient descent, the thing that trains most ML models, is calculus wearing a disguise<\/p>\n\n\n\n

\u2022        Probability: conditional probability, Bayes’ theorem, distributions. Half of classical ML is dressed-up probability theory<\/p>\n\n\n\n

\u2022        Statistics: mean, variance, standard deviation, hypothesis testing, correlation versus causation (please internalize that last one before you accidentally make a very confident, very wrong claim in an interview)<\/p>\n\n\n\n

You don’t need to derive backpropagation by hand to use it well, the same way you don’t need to understand internal combustion to drive a car competently. But knowing roughly what’s happening under the hood means you’ll actually notice when something’s gone wrong, instead of just staring at a loss curve that refuses to go down and hoping it fixes itself.<\/p>\n\n\n\n

Scaler’s free Mathematics for Machine Learning<\/a> course covers exactly this applied slice, not a full undergraduate math degree’s worth of content. The Scaler Machine Learning hub is a good reference to keep open alongside it.<\/p>\n\n\n\n

<\/span>Module 2: Python & Data Handling<\/strong><\/span><\/h2>\n\n\n\n

Python runs this entire field, and that’s not really up for debate anymore. Every major ML library, scikit-learn, TensorFlow, PyTorch, Hugging Face, assumes Python as the default interface. Learning R or Julia first isn’t wrong exactly, it’s just adding detour signs to a road that already has a perfectly good direct route.<\/p>\n\n\n\n

Topics<\/strong><\/h3>\n\n\n\n

\u2022        Python fundamentals: variables, control flow, functions, basic OOP, enough to read and write real ML code comfortably<\/p>\n\n\n\n

\u2022        NumPy: array operations, the actual computational backbone underneath most ML libraries, whether you see it directly or not<\/p>\n\n\n\n

\u2022        pandas: loading, cleaning, filtering, and reshaping tabular data, which is most of what you’ll actually spend time doing before any model gets near the data<\/p>\n\n\n\n

\u2022        matplotlib \/ seaborn: visualizing data and results, because nobody trusts a model’s output they can’t actually see plotted out<\/p>\n\n\n\n

\u2022        Data cleaning: handling missing values, outliers, and inconsistent formatting. Unglamorous, constant, and frankly where most of the real work in ML lives, despite what the job title implies<\/p>\n\n\n\n

The official Python documentation (python.org)<\/a> is genuinely good and underrated as a learning resource once you’re past the absolute basics. Scaler’s Python for Data Science course<\/a> and the broader Scaler Python Tutorial<\/a> cover this module with the data-science-specific framing that a generic \u201clearn Python\u201d course often skips.<\/p>\n\n\n\n

<\/span>Module 3: Classical Machine Learning<\/strong><\/span><\/h2>\n\n\n\n

This is where it stops being math and code in isolation and starts being actual machine learning. Classical ML, the stuff that doesn’t involve neural networks, still powers a surprising amount of production systems, mostly because it’s faster, more interpretable, and frequently just as accurate for tabular, structured data.<\/p>\n\n\n\n

Topics<\/strong><\/h3>\n\n\n\n

\u2022        Supervised learning: training on labeled data, the bulk of real-world ML work, split into regression (predicting a number) and classification (predicting a category)<\/p>\n\n\n\n

\u2022        Unsupervised learning: finding structure in unlabeled data, clustering customers into segments, for instance, without anyone telling the model what the segments should be ahead of time<\/p>\n\n\n\n

\u2022        Core algorithms: linear and logistic regression, decision trees, random forests, k-means clustering, support vector machines. Know what each does and, more importantly, when each one is actually the right tool<\/p>\n\n\n\n

\u2022        Model evaluation: accuracy, precision, recall, F1 score, confusion matrices. A model that’s 95% accurate sounds great until you learn the dataset was 95% one class to begin with<\/p>\n\n\n\n

\u2022        Overfitting and underfitting: recognizing when a model has memorized the training data instead of actually learning the pattern, which is the single most common rookie mistake in this entire field<\/p>\n\n\n\n

<\/span>Module 4: Deep Learning & Neural Networks<\/strong><\/span><\/h2>\n\n\n\n

Here’s an honest, slightly unpopular take: you do not need deep learning for most ML problems. If your data fits in a spreadsheet and a random forest gets you 90% accuracy, training a neural network is mostly just burning compute to feel sophisticated. Deep learning earns its reputation specifically with unstructured data: images, audio, raw text, video, where classical ML genuinely struggles.<\/p>\n\n\n\n

Topics<\/strong><\/h3>\n\n\n\n

\u2022        Neural network fundamentals: neurons, layers, activation functions, forward and backward propagation<\/p>\n\n\n\n

\u2022        CNNs (Convolutional Neural Networks): the architecture behind most image-related tasks, built around the idea of scanning small patches of an image for patterns rather than treating every pixel independently<\/p>\n\n\n\n

\u2022        RNNs (Recurrent Neural Networks) and their successors: built for sequence data like text or time series, where order genuinely matters (the foundational idea behind everything that eventually leads to transformers in Module 5)<\/p>\n\n\n\n

\u2022        Training mechanics: loss functions, optimizers, learning rate, batch size, and the very real patience required when a model trains for six hours and then produces garbage<\/p>\n\n\n\n

\u2022        Frameworks: TensorFlow and PyTorch, the two dominant libraries, both genuinely capable, with PyTorch having pulled ahead in research and increasingly in industry adoption too<\/p>\n\n\n\n

Per PyTorch’s own documentation, the framework is built to provide tensor computation with strong GPU acceleration and deep neural networks built on a tape-based autograd system, which is a fairly accurate description of why researchers gravitated toward it. TensorFlow, per its own documentation, positions itself as an end-to-end platform for machine learning with a particularly strong production deployment ecosystem, which is part of why it still shows up heavily in enterprise settings even as PyTorch wins mindshare elsewhere.<\/p>\n\n\n\n

Scaler’s free Deep Learning course<\/a> is a solid no-cost entry point, and the Deep Learning Roadmap<\/a> maps out the deeper progression if this module is where you want to specialize.<\/p>\n\n\n\n

<\/span>Module 5: NLP & Computer Vision<\/strong><\/span><\/h2>\n\n\n\n

Two application domains, both built on top of the deep learning foundation from Module 4, and both genuinely transformed by the same underlying breakthrough: the transformer architecture.<\/p>\n\n\n\n

NLP topics<\/strong><\/h3>\n\n\n\n

\u2022        Text preprocessing: tokenization, stemming, lemmatization, much of which overlaps directly with what a sentiment analysis or chatbot pipeline needs<\/p>\n\n\n\n

\u2022        Word embeddings: representing words as dense numerical vectors that capture semantic meaning, the bridge between raw text and something a model can actually compute on<\/p>\n\n\n\n

\u2022        Transformers: the architecture underneath essentially every modern NLP breakthrough, including the LLMs that show up in Module 6. Worth understanding conceptually even before you touch an LLM directly<\/p>\n\n\n\n

Computer vision topics<\/strong><\/h3>\n\n\n\n

\u2022        Image classification: assigning a label to an entire image, the most basic CV task and the one most tutorials start with<\/p>\n\n\n\n

\u2022        Object detection: finding and localizing multiple objects within a single image, considerably harder than plain classification<\/p>\n\n\n\n

\u2022        Image segmentation: classifying every individual pixel, used heavily in medical imaging and autonomous vehicle perception<\/p>\n\n\n\n

Example applications worth knowing: a spam filter is NLP text classification, a facial recognition unlock is computer vision, a chatbot is NLP plus, increasingly, an LLM doing most of the heavy lifting now. The Scaler AI & Machine Learning Course covers both NLP and CV with hands-on projects rather than just architecture diagrams to memorize.<\/p>\n\n\n\n

<\/span>Module 6: Generative AI & LLMs<\/strong><\/span><\/h2>\n\n\n\n

This is the module that didn’t exist in most syllabi five years ago and is now, frankly, the entire reason a lot of people are looking at this curriculum in the first place. Skipping it in 2026 isn’t an option if you want to be taken seriously in an interview, no matter how solid your classical ML fundamentals are.<\/p>\n\n\n\n

Topics<\/strong><\/h3>\n\n\n\n

\u2022        Large Language Models (LLMs): what they actually are (very large transformer models trained on massive text corpora), and crucially, what they’re not (not reasoning the way a human does, however convincing the output sounds)<\/p>\n\n\n\n

\u2022        Prompt engineering: structuring inputs to reliably get useful outputs, a genuinely real and underrated skill, not the punchline it sometimes gets treated as<\/p>\n\n\n\n

\u2022        RAG (Retrieval-Augmented Generation): combining an LLM with an external knowledge source, so it can answer questions about your specific documents instead of just whatever it memorized during training, which it will otherwise sometimes confidently make up<\/p>\n\n\n\n

\u2022        Fine-tuning basics: adapting a pretrained model to a narrower task or domain, versus just prompting a general-purpose model and hoping it behaves<\/p>\n\n\n\n

\u2022        Working with APIs: calling OpenAI, Anthropic, or open-source models through frameworks like LangChain to actually build something, rather than just chatting with a model in a browser tab<\/p>\n\n\n\n

If you’re serious about specializing here specifically, the Scaler Generative AI Roadmap<\/a>. For a more intensive, advanced program built specifically around this, the IIT Roorkee Advanced AI Engineering Course<\/a> is worth evaluating if generative AI and LLMs are where you want to end up specializing rather than just dabbling.<\/p>\n\n\n\n

<\/span>Module 7: MLOps & Deployment<\/strong><\/span><\/h2>\n\n\n\n

A model sitting in a Jupyter notebook on your laptop, however accurate, is not a product. It’s a science fair project. MLOps is the unglamorous, genuinely necessary discipline of getting a model from \u201cit works on my machine\u201d to \u201cit works reliably for real users, at 3 AM, without you babysitting it.\u201d<\/p>\n\n\n\n

Topics<\/strong><\/h3>\n\n\n\n

\u2022        Model deployment: packaging a trained model behind an API so other systems can actually call it<\/p>\n\n\n\n

\u2022        Containerization: using Docker to make sure a model behaves identically across your laptop, a colleague’s laptop, and the production server, instead of the classic \u201cworks on my machine\u201d standoff<\/p>\n\n\n\n

\u2022        Monitoring: tracking a deployed model’s performance over time, since real-world data drifts and a model that was accurate at launch can quietly degrade without anyone noticing for months<\/p>\n\n\n\n

\u2022        Pipelines and reproducibility: automating the retraining and redeployment process so updates don’t require manually re-running fifteen notebook cells in the right order and praying<\/p>\n\n\n\n

\u2022        Experiment tracking: tools like MLflow that log what was tried, what worked, and what didn’t, so six months from now someone (often you) can actually answer \u201cwait, why did we pick this model again?\u201d<\/p>\n\n\n\n

This module is increasingly what separates someone who can build a model from someone who can ship one, and that distinction matters a lot more to hiring managers than most self-taught learners initially expect. The Scaler MLOps Roadmap<\/a> covers this ground in more depth, and Scaler’s Academy programs build deployment-focused project work directly into the curriculum rather than treating it as an optional afterthought.<\/p>\n\n\n\n

<\/span>Module 8: AI\/ML Projects for Your Portfolio<\/strong><\/span><\/h2>\n\n\n\n

Nobody gets hired because they finished a syllabus. They get hired because there’s proof, in the form of something that actually runs, that they can do the job. Build these roughly in this order, each one stacking a new skill onto the last.<\/p>\n\n\n\n

Project 1: Classical ML model<\/strong><\/h3>\n\n\n\n

Predict house prices or customer churn using a tabular dataset, scikit-learn, and proper train\/test evaluation. Boring on the surface, but it proves Modules 1 through 3 in one clean package.<\/p>\n\n\n\n

Project 2: Deep learning application<\/strong><\/h3>\n\n\n\n

An image classifier (cats vs dogs is a clich\u00e9 for a reason, it’s genuinely a clean teaching dataset) or a basic sentiment classifier on text using a neural network. This proves Module 4 and a slice of Module 5.<\/p>\n\n\n\n

Project 3: LLM-powered project<\/strong><\/h3>\n\n\n\n

Build a RAG-based question-answering system over a small set of your own documents, or a chatbot with a genuinely specific, narrow purpose (not \u201cI made a general chatbot,\u201d which proves nothing an interviewer hasn’t seen forty times already). This proves Module 6, and it’s increasingly the project people actually ask about in interviews now.<\/p>\n\n\n\n

Project 4: Deployed, end-to-end system<\/strong><\/h3>\n\n\n\n

Take any of the above and actually deploy it: a working API, containerized, with basic monitoring. This proves Module 7 and is, frankly, the project that separates a portfolio that gets callbacks from one that doesn’t.<\/p>\n\n\n\n

Push everything to GitHub with a README explaining your decisions, not just your code; interviewers genuinely read these more than people expect. The Scaler Machine Learning Roadmap has more project ideas at each stage if this ladder needs more rungs, and the AI & Machine Learning Course builds mentored versions of exactly this project progression directly into its curriculum.<\/p>\n\n\n\n

<\/span>AI\/ML Career Path, Roles & Salary in India<\/strong><\/span><\/h2>\n\n\n\n

This is, by most credible measures, one of the better times in recent memory to be building these skills, even accounting for the usual hype-cycle noise around AI.<\/p>\n\n\n\n

Role<\/strong><\/td>Typical Annual Salary (India)<\/strong><\/td>Focus<\/strong><\/td><\/tr>
ML Engineer (entry, 0\u20132 yrs)<\/td>\u20b96\u201312 LPA<\/td>Building and deploying models, heavy on Modules 2\u20134<\/td><\/tr>
Data Scientist (entry, 0\u20132 yrs)<\/td>\u20b96\u201314 LPA<\/td>Analysis, modeling, and communicating insights, heavy on Modules 1\u20133<\/td><\/tr>
AI Engineer \/ LLM Engineer (mid, 3\u20136 yrs)<\/td>\u20b915\u201330 LPA<\/td>Generative AI applications, RAG systems, fine-tuning, heavy on Module 6<\/td><\/tr>
Senior ML\/AI Engineer (7+ yrs)<\/td>\u20b930\u201360+ LPA<\/td>Architecture decisions, MLOps ownership, mentoring, all modules combined<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Per the World Economic Forum’s Future of Jobs Report 2025, AI and Machine Learning Specialists rank among the fastest-growing job roles globally in percentage terms, alongside Big Data Specialists and Fintech Engineers, and the report notes that 86% of surveyed employers expect AI and information processing technologies to transform their business by 2030. That’s not a niche prediction buried in a footnote, that’s the headline finding from over a thousand global employers.<\/p>\n\n\n\n

The same report flags that roughly 39% of workers’ core skills are expected to change by 2030, which is the polite, corporate-report way of saying: the skills you have right now have a shelf life, and continuously updating them isn’t optional anymore, regardless of which specific role you’re aiming for within this field.<\/p>\n\n\n\n

A typical progression looks like: Junior ML Engineer or Data Scientist \u2192 ML Engineer or AI Engineer (specializing toward either classical ML\/deployment or generative AI\/LLMs) \u2192 Senior Engineer (architecture, mentoring) \u2192 Staff Engineer, ML Architect, or AI Research Lead. Plenty of people also branch sideways into MLOps Engineer or Applied Scientist roles depending on whether they lean more toward infrastructure or research.<\/p>\n\n\n\n

If you want the fuller career map with more detail on how this plays out by company type and specialization, the Scaler Artificial Intelligence Syllabus goes deeper than salary tables alone reasonably can. For learners aiming specifically at the advanced, research-adjacent end of this spectrum, the IIT Roorkee Advanced AI Engineering Course<\/a> is built around exactly that altitude.<\/p>\n\n\n\n

<\/span>FAQs<\/strong><\/span><\/h2>\n\n\n\n

How long does it take to complete an AI ML syllabus?<\/strong><\/h3>\n\n\n\n

For a full professional curriculum covering Modules 1 through 8 properly, 8 to 14 months of consistent study is realistic for most learners, faster if you’re already comfortable with programming and basic math, slower if you’re starting from genuinely zero on both. Generative AI specifically can be picked up faster once the earlier foundations are solid, since it builds directly on concepts from Modules 4 and 5.<\/p>\n\n\n\n

Do I need strong math for AI and ML?<\/strong><\/h3>\n\n\n\n

You need working, applied math, not research-level math. Linear algebra, basic calculus (derivatives and gradients specifically), probability, and statistics cover the vast majority of what shows up in practice. You don’t need to prove theorems or derive every algorithm from first principles; you need to understand what’s happening well enough to debug a model that’s misbehaving, which is a meaningfully lower bar than a math degree.<\/p>\n\n\n\n

What is the difference between AI, ML and deep learning?<\/strong><\/h3>\n\n\n\n

AI is the broadest umbrella, any system designed to mimic intelligent behavior. Machine Learning is a subset of AI where systems learn patterns from data rather than following explicit hardcoded rules. Deep Learning is a subset of ML that specifically uses layered neural networks, particularly effective on unstructured data like images, audio, and text. Generative AI sits inside deep learning, referring specifically to models that generate new content rather than just classify or predict.<\/p>\n\n\n\n

Should I learn generative AI in 2026?<\/strong><\/h3>\n\n\n\n

Yes, without much room for debate at this point. LLMs and generative AI tooling have moved from \u201cinteresting research direction\u201d to \u201ccore expectation in most AI\/ML job descriptions\u201d in a remarkably short window. Skipping Module 6 in a 2026 curriculum leaves a gap that will show up directly in interviews, regardless of how strong your classical ML fundamentals are.<\/p>\n\n\n\n

Can I learn AI\/ML without a CS degree?<\/strong><\/h3>\n\n\n\n

Yes! With the honest caveat that you’ll need to prove your skills harder than someone with a degree on paper would. A portfolio of real projects (Module 8), solid applied math fundamentals, and the ability to clearly explain your decisions in an interview matter more to most product companies than the credential itself. Consistency over months matters considerably more than any single course or certificate.<\/p>\n\n\n\n

Which is better: a B.Tech AI\/ML syllabus or a professional course?<\/strong><\/h3>\n\n\n\n

Depends entirely on your goal, not on which one is objectively \u201cbetter.\u201d A B.Tech syllabus gives broader academic depth, more theory, more time, and a recognized degree, but moves slower and isn’t always aligned with what’s currently in demand industry-side. A professional, job-focused course moves faster, stays closer to current tools and generative AI developments, and is built around employability rather than academic breadth. If you already have a degree in something else and want a focused career switch, a professional program is usually the more efficient path; if you’re choosing your undergraduate degree itself, breadth has its own long-term value worth weighing.<\/p>\n","protected":false},"excerpt":{"rendered":"

Every AI\/ML syllabus floating around the internet seems to either drown you in math you’ll never directly use, or skip straight to \u201cbuild a chatbot in 10 minutes\u201d and call it a curriculum. Neither actually gets you job-ready. Somewhere between \u201cprove this theorem\u201d and \u201ccopy-paste this API call\u201d is the syllabus that actually works, and […]<\/p>\n","protected":false},"author":201,"featured_media":13150,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[320,37,316],"tags":[272,303],"class_list":["post-13031","post","type-post","status-publish","format-standard","has-post-thumbnail","category-syllabus","category-artificial-intelligence-machine-learning","category-artificial-intelligence","tag-artificial-intelligence","tag-machine-learning"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.scaler.com\/blog\/wp-json\/wp\/v2\/posts\/13031","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.scaler.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.scaler.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.scaler.com\/blog\/wp-json\/wp\/v2\/users\/201"}],"replies":[{"embeddable":true,"href":"https:\/\/www.scaler.com\/blog\/wp-json\/wp\/v2\/comments?post=13031"}],"version-history":[{"count":2,"href":"https:\/\/www.scaler.com\/blog\/wp-json\/wp\/v2\/posts\/13031\/revisions"}],"predecessor-version":[{"id":13151,"href":"https:\/\/www.scaler.com\/blog\/wp-json\/wp\/v2\/posts\/13031\/revisions\/13151"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.scaler.com\/blog\/wp-json\/wp\/v2\/media\/13150"}],"wp:attachment":[{"href":"https:\/\/www.scaler.com\/blog\/wp-json\/wp\/v2\/media?parent=13031"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.scaler.com\/blog\/wp-json\/wp\/v2\/categories?post=13031"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.scaler.com\/blog\/wp-json\/wp\/v2\/tags?post=13031"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}