AI Engineer
A structured Day 1 to Day 72 topic roadmap for a 12-week AI Engineer Bootcamp, featuring one short conceptual video per learning day. Each 5–8 minute video explains what, why, and when, with intuition, a practical example, a common misconception, and interview relevance. The curriculum is concept-focused, not live-coding, following the same five-point learning template every day. Topics span Python, data handling, statistics, classical ML, deep learning, MLOps/deployment, capstone, and interview preparation.
₹1,999
One payment, tax included. No mentor and no live classes — the full list is below.
Where this course leaves you
- What You’ll Learn
- Python & Data Foundations — Build strong programming, data handling, and statistical fundamentals.
- Machine Learning & Deep Learning — Understand core ML algorithms, neural networks, and model development.
- Deployment & MLOps — Learn how to productionize, deploy, monitor, and maintain AI models.
- Real-World AI Engineering — Apply your skills through a capstone project and prepare for AI Engineer interviews.
At a glance
- Format
- Self-paced, online
- Weeks
- 12
- Lessons
- 75
- Price
- ₹1,999, tax included
- Track
- Tech Track
And what you do not get
A self-paced course is the material and the practice. Anything that needs another person's time is not part of it, and we would rather you knew that before you paid than after.
Included
- Every lesson, written outThe full text of each lesson below, in order, to read at whatever pace suits you.
- The videos that go with themWhere a lesson has a recording or a walkthrough, it plays inside the lesson.
- Coding practice in the browserRun the examples and the exercises as you read them. There is nothing to install.
- Your progress, rememberedStop in the middle of a week and pick it up on another device a month later.
Not included
- No mentorNobody is assigned to you, and no one reviews what you write.
- No live classes, no office hoursThere is no timetable and no session to attend.
- No assignments to hand inNothing is submitted, and nothing is marked.
- No messagingYou cannot write to a teacher from inside the course.
Those four are what the mentored programmes are for. See the programmes.
All 12 weeks, in order
Everything in the course is listed here. There is nothing behind the price you cannot see first.
- Week 1
Days 1-6: Foundations, Python & Tooling
AI engineer role, Python basics, control flow, functions, collections, and debugging/Git — plus orientation and a hands-on environment setup guide.
- Day 0 - Orientation: Prerequisites, Tools & Where This Course Takes You
- Day 0.5 - Environment & Tooling Setup: Get Your Machine Ready
- Day 1 - AI Engineer role, tool setup + baseline: What Does an AI/ML Engineer Actually Ship?
- Day 2 - Python values, types + operators: Python Types Are Part of Your Data Contract
- Day 3 - Control flow + iteration: Control Flow Turns Requirements Into Code
- Day 4 - Functions, scope + modules: Why Functions Matter in ML Projects
- Day 5 - Python collections + complexity intuition: Choose the Right Python Data Structure
- Day 6 - Files, exceptions, debugging + Git: Debugging Is a Reproducible Process
- Week 2
Days 7-12: Data Handling & Preparation (inferred grouping)
NumPy, Pandas, data cleaning, EDA, SQL, and an integrated data preparation case.
- Day 7 - NumPy arrays + vectorization: Why NumPy Is Fast Enough for ML Data Work
- Day 8 - Pandas DataFrames + I/O: A DataFrame Is a Table With Behavior
- Day 9 - Cleaning missing values, duplicates + types: Cleaning Choices Change the Model
- Day 10 - EDA + visualization for modeling: EDA Is Risk Detection Before Modeling
- Day 11 - SQL essentials for ML data: SQL Builds the Training Table
- Day 12 - Integrated data preparation case: A Modeling Dataset Needs an Audit Trail
- Week 3
Days 13-18: Statistics, Math & ML Workflow (inferred grouping)
This week deepens the intuition behind machine learning: how the shape of a distribution changes modelling choices, why base rates and conditional probability matter, how sampling and hypothesis tests express uncertainty, the linear-algebra and calculus foundations behind ML and deep learning, and the discipline of train/validate/test splits, baselines, and avoiding data leakage.
- Day 13 - Descriptive statistics + distributions: Distribution Shape Changes Model Decisions
- Day 14 - Probability + conditional probability: Base Rates Matter in AI Decisions
- Day 15 - Sampling, confidence + hypothesis intuition: Uncertainty Is Part of the Result
- Day 16 - Linear algebra for ML: ML Sees Data as Vectors
- Day 16b - Math bridge: Derivatives, Gradients & Vector/Matrix Operations
- Day 17 - ML workflow, targets + splits: Train, Validate, Test Have Different Jobs
- Day 18 - Leakage, baselines + preprocessing discipline: Data Leakage Can Make a Bad Model Look Brilliant
- Week 4
Days 19-24: Regression (inferred grouping)
Regression baseline/linear regression, metrics, encoding/scaling, regularization, cross-validation, and a mini-project with assessment.
- Day 19 - Regression baseline + linear regression: A Baseline Tells You Whether ML Adds Value
- Day 20 - Regression metrics: Metrics Encode What Errors You Care About
- Day 21 - Encoding + scaling: Preprocessing Is Part of the Model
- Day 22 - Regularization - Ridge + Lasso: Regularization Trades Flexibility for Stability
- Day 23 - Cross-validation + residual analysis: Cross-Validation Tests Stability, Not the Future
- Day 24 - Regression mini-project + assessment: A Good ML Project Explains Its Errors
- Week 5
Days 25-30: Classification (inferred grouping)
Logistic regression, classification metrics, decision trees, ensembles, class imbalance/thresholds, and a classification case with assessment.
- Day 25 - Logistic regression + probabilities: Classification Starts With a Probability and a Decision
- Day 26 - Classification metrics: Accuracy Can Hide the Failure You Care About
- Day 27 - Decision trees: Decision Trees Learn Rules From Data
- Day 28 - Random forests + gradient boosting: Ensembles Reduce Different Kinds of Error
- Day 29 - Class imbalance + thresholds: Imbalance Is a Decision Problem, Not Just a Data Problem
- Day 30 - Classification case + assessment: A Classifier Is Only Useful at an Operating Point
- Week 6
Days 31-36: Feature Engineering, Pipelines & Model Practice (inferred grouping)
Feature engineering, pipelines/ColumnTransformer, hyperparameter tuning, model selection, explainability, and reproducibility.
- Day 31 - Feature engineering: Features Translate Domain Knowledge Into Signals
- Day 32 - Pipelines + ColumnTransformer: A Pipeline Is an Executable Modeling Contract
- Day 33 - Hyperparameter tuning: Tuning Is Search Under a Budget
- Day 34 - Model selection + leakage-safe CV: Fair Model Comparison Requires the Same Rules
- Day 35 - Explainability + error analysis: Explainability Is Evidence About Model Behavior, Not Truth
- Day 36 - Reproducibility + experiment tracking: Reproducibility Means Someone Can Recreate the Result
- Week 7
Days 37-42: Unsupervised Learning & Integrated Mini-Project (inferred grouping)
K-means, PCA, anomaly detection awareness, evaluating unsupervised work, an integrated ML mini-project build, and presentation with cumulative assessment.
- Day 37 - K-means clustering: Clustering Finds Structure You Still Have to Interpret
- Day 38 - PCA: PCA Rotates Information Into New Axes
- Day 39 - Anomaly detection awareness: Anomaly Detection Produces Candidates, Not Verdicts
- Day 40 - Evaluating unsupervised work: Unsupervised Models Need External Validation
- Day 41 - Integrated ML mini-project build: Use Unsupervised Learning Only Where It Helps
- Day 42 - Mini-project presentation + cumulative assessment: A Portfolio Project Is a Chain of Defensible Decisions
- Week 8
Days 43-48: Neural Networks & Deep Learning (inferred grouping)
Neural network intuition, loss/gradient descent/backprop concept, PyTorch tensors/datasets, training loop, overfitting/regularization, and a DL mini-case vs classical comparison.
- Day 43 - Neural-network intuition: A Neural Network Is a Stack of Learned Transformations
- Day 44 - Loss, gradient descent + backprop concept: Training Means Repeatedly Reducing a Defined Error
- Day 45 - PyTorch tensors + datasets: Batches Turn Data Into a Training Stream
- Day 46 - PyTorch training loop: The Training Loop Has a Small Number of Essential Steps
- Day 47 - Overfitting + regularization in DL: Validation Curves Tell You When Learning Stops Generalizing
- Day 48 - DL mini-case + classical comparison: Deep Learning Must Earn Its Complexity
- Week 9
Days 49-54: ML Engineering & Deployment (inferred grouping)
This week moves from building models to shipping them. You'll learn how to structure an ML project with clear boundaries, save models with the context needed to reuse them, expose a model as a REST API with FastAPI, protect it with tests and input validation, package the runtime with Docker, and treat deployment as an ongoing lifecycle.
- Day 49 - ML project structure + practical OOP: A Deployable ML Project Needs Boundaries
- Day 50 - Serialization + inference contract: A Model Artifact Needs Context
- Day 51 - REST + JSON + FastAPI: An API Turns a Model Into a Service Contract
- Day 52 - Testing + input validation: Tests Protect the Contract Around the Model
- Day 53 - Docker: Docker Packages the Runtime, Not the Model Logic
- Day 54 - Cloud/deployment + lifecycle awareness: Deployment Is a Lifecycle, Not an Upload Button
- Week 10
Days 55-60: Capstone Phase 1 (inferred grouping)
Capstone problem/success criteria, data profiling/leakage review, EDA/split strategy, baselines, feature pipeline, and Phase 1 checkpoint.
- Day 55 - Capstone problem + success criteria: A Prediction Problem Needs an Actionable Decision
- Day 56 - Capstone data profiling + leakage review: Leakage Often Looks Like a Convenient Feature
- Day 57 - Capstone EDA + split strategy: Your Split Defines the Claim Your Model Can Make
- Day 58 - Capstone baselines: Baselines Protect You From Self-Deception
- Day 59 - Capstone feature pipeline: A Feature Is Valid Only If It Exists at Inference Time
- Day 60 - Capstone Phase 1 checkpoint: A Strong Checkpoint Finds Problems Before Tuning
- Week 11
Days 61-66: Capstone Build & Defense (inferred grouping)
Model comparison/tuning, thresholds/calibration/error analysis, FastAPI serving, tests/Docker, documentation/model card, and demo/defense.
- Day 61 - Capstone model comparison + tuning: Model Selection Is a Multi-Criteria Decision
- Day 62 - Thresholds, calibration + error analysis: The Model Score Still Needs an Operating Policy
- Day 63 - Capstone FastAPI serving: Serving Must Reuse the Same Preprocessing Pipeline
- Day 64 - Capstone tests + Docker: A Passing Notebook Is Not a Passing Service
- Day 65 - Documentation, model card + limitations: Limitations Are Part of Engineering Quality
- Day 66 - Capstone demo + defense: Project Defense Tests Ownership, Not Memorization
- Week 12
Days 67-72: Interview Preparation & Job Readiness (inferred grouping)
Timed Python coding screen, ML/statistics screening, SQL/data-prep practical, system/deployment case, capstone presentation/behavioral, and full mock hiring process with gap plan.
- Day 67 - Timed Python coding screen: Coding Interviews Reward Correct Reasoning Before Cleverness
- Day 68 - ML/statistics screening: A Good ML Answer Connects Concept to Failure Mode
- Day 69 - SQL + data-preparation practical: Data Rounds Test Whether the Model Has Trustworthy Inputs
- Day 70 - System/deployment case: ML Engineering Continues After model.fit()
- Day 71 - Capstone presentation + behavioral: Your Project Story Should Show Decisions and Evidence
- Day 72 - Full mock hiring process + gap plan: Job Ready Means Strong Core Skills Plus a Clear Learning Boundary
Buy AI Engineer
₹1,999
One payment. Tax is inside this figure, not added to it.
- You buy the course and start. There is no call and no application.
- No mentor, no live classes, no marked work, no messaging.
- Worth reading first: our refund policy.
Wanted a mentor instead?
Then this is not the thing to buy. The mentored programmes at our centre are months of full-time work with a named mentor, live classes and placement support. They are priced per batch rather than printed on a page, and you get the number on a free demo call.
Not sure this is the right course?
This one has no mentor attached to it. If that is what you actually want, book a free demo call and we will walk you through the mentored programme instead — honestly, including what it costs.
Placement assistance — not a guarantee — and we'll always be upfront about that.