Course

AI, ML & Soft Skills — Complete Career Accelerator

A complete AI/ML Engineer flagship program covering Python and statistical foundations, classical machine learning with scikit-learn, XGBoost and LightGBM, deep learning with PyTorch, the full LLM/GenAI engineering stack (Hugging Face, LangChain, LlamaIndex, fine-tuning, end-to-end RAG pipeline design), vector databases (Pinecone, Weaviate, Chroma, Milvus), MLOps (FastAPI, Docker, Kubernetes, MLflow, Airflow, ONNX), AI agent frameworks (LangGraph, AutoGen, CrewAI, MCP) and responsible AI — folded together with professional communication, behavioral interview mastery and resume/portfolio building, so technical depth and interview readiness are built in one program instead of two.

⏱ 16 Weeks 🖥 Online 💻 7 Projects ⭐ 4.9/5

🎓 Book Free Demo 📞 +91 75502 51906

INCLUDED AT NO COST

Everything below is included at no extra cost

🚀 FREE!

Live Project(s)

💼 FREE!

Job Reference(s)

🎓 FREE!

Career Guidance

♾️ FREE!

Life Time Support

PACKAGES

We Offer Responsive Packages With An Unexplored Syllabus

ML foundations

⏱ 6 Weeks

★ 4.7/5

Classical ML Specialist Track

Python, statistics and classical machine learning with scikit-learn, XGBoost and LightGBM — the models that power most real ML jobs.

Python for ML/AI, SQL & Statistical Foundations
Classical Machine Learning
Professional Communication & Technical Writing
Resume & Portfolio Building for ML Roles
Comprehensive Interview Prep & Career Launch

Python, NumPy, pandas, scikit-learn, XGBoost, LightGBM, SQL

View Curriculum →

Most enrolled

⏱ 8 Weeks

★ 4.9/5

Deep Learning & LLM Engineering Track

PyTorch, transformers and the full LLM/GenAI engineering stack — LangChain, LlamaIndex and end-to-end RAG pipeline design.

Python for ML/AI, SQL & Statistical Foundations
Deep Learning with PyTorch
LLM & GenAI Engineering
Embeddings & Vector Databases
Comprehensive Interview Prep & Career Launch

PyTorch, Hugging Face, LangChain, LlamaIndex, Pinecone, Weaviate, Chroma

View Curriculum →

Systems-first

⏱ 7.5 Weeks

★ 4.8/5

MLOps & Deployment Track

Serve, track and deploy models properly — FastAPI, Docker, Kubernetes basics, MLflow, Airflow and ONNX.

Python for ML/AI, SQL & Statistical Foundations
Classical Machine Learning
MLOps — Serving, Tracking & Deploying Models
AI Ethics, Responsible AI & Governance
Comprehensive Interview Prep & Career Launch

FastAPI, Docker, Kubernetes, MLflow, Airflow, ONNX

View Curriculum →

AI-specialized

⏱ 6.5 Weeks

★ 4.9/5

AI Agents & Applied GenAI Track

Build agentic systems with LangGraph, AutoGen, CrewAI and MCP, grounded in real retrieval and vector search.

LLM & GenAI Engineering
Embeddings & Vector Databases
AI Agent Frameworks & Tool Integration
AI Ethics, Responsible AI & Governance
Comprehensive Interview Prep & Career Launch

LangGraph, AutoGen, CrewAI, MCP, LangChain, Vector Databases

View Curriculum →

Flagship

⏱ 16 Weeks

★ 4.9/5

Complete Flagship — AI, ML & Soft Skills Career Accelerator

Every module — classical ML through LLMs, agents and MLOps — combined with the full communication, behavioral-interview and portfolio track in one program.

All 12 modules, start to finish
Classical ML, Deep Learning, LLM/GenAI Engineering & AI Agents
MLOps deployment (FastAPI, Docker, Kubernetes, MLflow, Airflow, ONNX)
Professional communication, behavioral interviews & portfolio building
Comprehensive Interview Prep & Career Launch

Python, NumPy, pandas, scikit-learn, XGBoost, LightGBM, PyTorch, Hugging Face, LangChain, LlamaIndex, Pinecone, Weaviate, Chroma, Milvus, FastAPI, Docker, Kubernetes, MLflow, Airflow, ONNX, LangGraph, AutoGen, CrewAI, MCP

View Curriculum →

Customized

⏱ Custom

★ 4.8/5

Build Your Own (Customized)

Pick exactly the modules you need from the shared AI/ML module bank, priced and scheduled to match.

Choose any modules from the shared bank
Custom duration & pricing
Same trainers, same mock-interview access
Comprehensive Interview Prep & Career Launch included

Selected per module

View Curriculum →

LEARNING OUTCOMES

What you will be able to do

Concrete skills, not topic names — each one is something you can show an interviewer.

🎯

Design test cases that find real defects

Boundary values, equivalence classes and edge cases — not just happy-path coverage.

🧪

Automate with a real framework

Page Object Model, reusable helpers and a suite you can actually maintain.

📊

Read a defect report the way a developer will

Reproduction steps, severity and evidence — reports that get fixed, not ignored.

🔧

Build and maintain a test suite

Structure tests so they survive the product changing under them.

🔗

Test APIs, not just interfaces

Postman and REST Assured, including auth flows most courses skip.

📈

Run suites in a CI pipeline

Jenkins and GitHub Actions — tests that run on every commit automatically.

TOOLS & SKILLS

Tools and skills covered

Java

Selenium

TestNG

Postman

Git

Jenkins

JMeter

SQL

🏆

Certificate of Completion

Awarded once every module and project is complete — recognised by our hiring partners.

IS THIS FOR YOU?

Who this course is for

If none of these describe you, tell us — we will point you at a better fit rather than sell you this one.

🎓

Freshers from any engineering branch

🔁

Manual testers hitting the automation ceiling

💻

Support engineers moving into QA

WHY IN FOURCUP?

Software Testing Program

Practical exposure to actual work environment

Work on live, under-construction projects with the exposure of an actual work environment, alongside a freelancer.

Dynamic syllabus structure

Built with multiple industry experts, so students become genuinely fluent in the subject rather than exam-ready.

Expert teachers who are active practitioners too

Mentors bring real industry experience and share their ups, downs and delivery skills, not just slides.

GET STARTED

Get Started Today

Download our Syllabus and review it thoroughly.

Request Call Back / Apply the Enquiry Form.

Join Our Free Demo Class.

Understand the Opportunity.

Join the Training session and become an expert!

KEEP EXPLORING

Get Your Related Courses

View All Courses

AI, ML & Soft Skills

Soft Skills & Interview Readiness Add-On

A focused Soft Skills & Interview Readiness program covering professional communication for technical roles, group discussion and aptitude fundamentals, the STAR method for behavioral interviews, body language and virtual-interview presence, salary negotiation and offer evaluation, workplace professionalism, and resume/LinkedIn optimization — anchored by a mock interview tailored to your own track (developer, tester or cloud/DevOps), not a generic HR-round rehearsal. Designed to work equally well as a standalone short course or as an add-on graduates of other Fourcup tracks bolt onto their main course.

Zoom/Google MeetSlackLinkedInResume templates

22% OFF Standalone or add-on

AI, ML & Soft Skills

AI & GenAI Add-On — Practical AI Skills for Tech Professionals

Works two ways: as a standalone short course in practical AI/GenAI literacy, or as an add-on for graduates of Fourcup's testing, development or cloud/DevOps tracks who want to layer AI skills onto their main course. Covers what LLMs are and aren't, prompt engineering, evaluating AI output critically, using AI coding/testing/ops assistants day to day, RAG (embeddings, vector databases), calling LLM APIs with streaming and tool-calling, a beginner-level intro to AI agents (LangGraph, AutoGen, CrewAI, MCP), and responsible AI use — scoped for practical fluency, not full ML engineering depth.

ChatGPTClaudeGeminiGitHub CopilotClaude Code+10 more

20% OFF Add-on ready

Software Development

Front-End & UI/UX Development — React, Next.js & Design Systems

A complete, current front-end and UI/UX program covering modern JavaScript/TypeScript, React 19, Next.js (App Router, SSR/SSG/ISR, Server Components), Tailwind CSS and shadcn/ui, UI/UX fundamentals (usability heuristics, Figma-to-code, design systems, responsive design), accessibility (WCAG 2.2) and Core Web Vitals as first-class engineering skills, component and end-to-end testing with Playwright and Storybook, and an AI-assisted front-end workflow (Claude Code, GitHub Copilot, Cursor, v0) with the same "review AI output like an editor" discipline throughout — so you graduate able to design, build, test and ship real production interfaces.

FigmaReactTypeScriptNext.jsTailwind CSS+12 more

15% OFF High-demand skill set

CURRICULUM

Module by module

Open any module to see exactly what it covers. Nothing is hidden behind a "download the syllabus" form.

6 Weeks

DURATION

MODULES

REAL-TIME PROJECTS

MOCK INTERVIEWS

ML foundations

Classical ML Specialist Track

⏱ 6 Weeks ★ 4.7

5 modules

Most enrolled

Deep Learning & LLM Engineering Track

⏱ 8 Weeks ★ 4.9

5 modules

Systems-first

MLOps & Deployment Track

⏱ 7.5 Weeks ★ 4.8

5 modules

AI-specialized

AI Agents & Applied GenAI Track

⏱ 6.5 Weeks ★ 4.9

5 modules

Flagship

Complete Flagship — AI, ML & Soft Skills Career Accelerator

⏱ 16 Weeks ★ 4.9

12 modules

Customized

Build Your Own (Customized)

⏱ Custom ★ 4.8

0 modules

1 Python for ML/AI, SQL & Statistical Foundations 1.5 wks ▾

Topic	What You'll Learn
1. Python for Data & ML	NumPy, pandas, data manipulation and cleaning, plus async patterns for calling APIs and services without blocking.
2. Statistics & Probability for ML	Distributions, hypothesis testing, correlation vs. causation, and the statistical reasoning every ML claim rests on.
3. Linear Algebra & Calculus, Just Enough	Vectors, matrices and gradients explained the way you actually need them for ML, not a full university course.
4. SQL for ML Practitioners	Writing the joins and aggregations that pull training data out of a real production database.
5. Data Visualization	Matplotlib, Seaborn and exploratory data analysis (EDA) that actually surfaces what's wrong with a dataset before you model it.
5.1) Real-world starting point	Most ML tutorials start with a clean CSV. This module is deliberately paired with a messy, real-world dataset, because that's what every ML job actually hands you first.

Topic	What You'll Learn
1. Python for Data & ML	NumPy, pandas, data manipulation and cleaning, plus async patterns for calling APIs and services without blocking.
2. Statistics & Probability for ML	Distributions, hypothesis testing, correlation vs. causation, and the statistical reasoning every ML claim rests on.
3. Linear Algebra & Calculus, Just Enough	Vectors, matrices and gradients explained the way you actually need them for ML, not a full university course.
4. SQL for ML Practitioners	Writing the joins and aggregations that pull training data out of a real production database.
5. Data Visualization	Matplotlib, Seaborn and exploratory data analysis (EDA) that actually surfaces what's wrong with a dataset before you model it.
5.1) Real-world starting point	Most ML tutorials start with a clean CSV. This module is deliberately paired with a messy, real-world dataset, because that's what every ML job actually hands you first.

2 Classical Machine Learning 2 wks ▾

Topic	What You'll Learn
1. Supervised Learning	Regression and classification with scikit-learn: linear/logistic regression, decision trees, SVMs, and when each one is the right tool.
2. Gradient-Boosted Trees	XGBoost and LightGBM in depth, the models that still win most tabular-data problems in production.
3. Feature Engineering	Encoding, scaling, handling missing data and building features that actually move a model's performance.
4. Model Evaluation	Precision/recall, ROC-AUC, cross-validation, and reading a confusion matrix like an engineer, not just printing accuracy.
5. Unsupervised Learning	Clustering (k-means, hierarchical) and dimensionality reduction (PCA) for when there's no label to predict.
5.1) Why this module exists	Job postings that say "machine learning" overwhelmingly mean XGBoost on tabular data, not a neural network. Skipping straight to deep learning is the single most common gap in self-taught ML candidates.

Topic	What You'll Learn
1. Supervised Learning	Regression and classification with scikit-learn: linear/logistic regression, decision trees, SVMs, and when each one is the right tool.
2. Gradient-Boosted Trees	XGBoost and LightGBM in depth, the models that still win most tabular-data problems in production.
3. Feature Engineering	Encoding, scaling, handling missing data and building features that actually move a model's performance.
4. Model Evaluation	Precision/recall, ROC-AUC, cross-validation, and reading a confusion matrix like an engineer, not just printing accuracy.
5. Unsupervised Learning	Clustering (k-means, hierarchical) and dimensionality reduction (PCA) for when there's no label to predict.
5.1) Why this module exists	Job postings that say "machine learning" overwhelmingly mean XGBoost on tabular data, not a neural network. Skipping straight to deep learning is the single most common gap in self-taught ML candidates.

3 Professional Communication & Technical Writing 0.5 wk ▾

Topic	What You'll Learn
1. Communicating with Non-Technical Stakeholders	Explaining a model's results and limitations to a product or business audience without jargon.
2. Technical Writing for ML/Data Teams	Writing a model card, a design doc and a clear README that a teammate can actually follow.
3. Documenting Experiments & Decisions	Recording why a modeling choice was made, not just what the final number was.
4. Presenting Findings	Structuring a results presentation so a non-ML audience walks away with the right conclusion.
4.1) Why this module exists	ML engineers spend a surprising share of their week explaining models to people who didn't build them. This is graded as a real skill here, not treated as an afterthought.

4 Resume & Portfolio Building for ML Roles 0.5 wk ▾

Topic	What You'll Learn
1. Framing ML Projects on a Resume	Describing a model's impact and methodology the way a hiring manager actually scans it, not a tool list.
2. Building a GitHub Portfolio	Structuring project repos with clear READMEs, reproducible code and results a recruiter can verify in two minutes.
3. Kaggle & Published Projects	Using competitions and public projects to build a credible, checkable track record.
4. LinkedIn for ML/AI Roles	Positioning yourself for the specific ML/AI roles you're targeting, not a generic tech-job profile.

5 Comprehensive Interview Preparation & Career Launch 1.5 wks ▾

Topic	What You'll Learn
1. ML System Design Interviews	Working through real prompts ("design a recommendation system," "design a fraud-detection pipeline") from requirements to a defensible architecture.
2. Take-Home Assignment Strategy	Managing scope, time and presentation on the take-home assignments most ML interview loops now include.
3. ML/AI Interview Question Bank	Classical ML, deep learning, LLM/GenAI and MLOps questions asked in real interviews, reviewed and explained.
4. Salary Negotiation & Offer Evaluation	Reading an ML/AI offer letter and negotiating from an informed position.
5. Placement Assistance	Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.
6. 🚀 Final Mock Interview	A full-length, role-realistic ML/AI Engineer interview, end to end, covering technical and behavioral rounds.

Topic	What You'll Learn
1. ML System Design Interviews	Working through real prompts ("design a recommendation system," "design a fraud-detection pipeline") from requirements to a defensible architecture.
2. Take-Home Assignment Strategy	Managing scope, time and presentation on the take-home assignments most ML interview loops now include.
3. ML/AI Interview Question Bank	Classical ML, deep learning, LLM/GenAI and MLOps questions asked in real interviews, reviewed and explained.
4. Salary Negotiation & Offer Evaluation	Reading an ML/AI offer letter and negotiating from an informed position.
5. Placement Assistance	Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.
6. 🚀 Final Mock Interview	A full-length, role-realistic ML/AI Engineer interview, end to end, covering technical and behavioral rounds.

1 Python for ML/AI, SQL & Statistical Foundations 1.5 wks ▾

Topic	What You'll Learn
1. Python for Data & ML	NumPy, pandas, data manipulation and cleaning, plus async patterns for calling APIs and services without blocking.
2. Statistics & Probability for ML	Distributions, hypothesis testing, correlation vs. causation, and the statistical reasoning every ML claim rests on.
3. Linear Algebra & Calculus, Just Enough	Vectors, matrices and gradients explained the way you actually need them for ML, not a full university course.
4. SQL for ML Practitioners	Writing the joins and aggregations that pull training data out of a real production database.
5. Data Visualization	Matplotlib, Seaborn and exploratory data analysis (EDA) that actually surfaces what's wrong with a dataset before you model it.
5.1) Real-world starting point	Most ML tutorials start with a clean CSV. This module is deliberately paired with a messy, real-world dataset, because that's what every ML job actually hands you first.

Topic	What You'll Learn
1. Python for Data & ML	NumPy, pandas, data manipulation and cleaning, plus async patterns for calling APIs and services without blocking.
2. Statistics & Probability for ML	Distributions, hypothesis testing, correlation vs. causation, and the statistical reasoning every ML claim rests on.
3. Linear Algebra & Calculus, Just Enough	Vectors, matrices and gradients explained the way you actually need them for ML, not a full university course.
4. SQL for ML Practitioners	Writing the joins and aggregations that pull training data out of a real production database.
5. Data Visualization	Matplotlib, Seaborn and exploratory data analysis (EDA) that actually surfaces what's wrong with a dataset before you model it.
5.1) Real-world starting point	Most ML tutorials start with a clean CSV. This module is deliberately paired with a messy, real-world dataset, because that's what every ML job actually hands you first.

2 Deep Learning with PyTorch 2 wks ▾

Topic	What You'll Learn
1. Neural Network Fundamentals	Forward/backward propagation, activation functions, loss functions and optimizers, built from first principles.
2. PyTorch in Depth	Tensors, autograd, the training loop, and why PyTorch is the framework current ML hiring favors over TensorFlow.
3. Convolutional Neural Networks (CNNs)	Image classification and the intuition behind convolution, pooling and feature maps.
4. Recurrent Networks & Transformers, Conceptually	RNNs/LSTMs for sequences, and the transformer architecture (attention, encoder/decoder) that underlies every modern LLM.
5. Transfer Learning	Fine-tuning a pretrained model instead of training from scratch, the way real teams actually work.
5.1) Where this shows up in real jobs	Interviewers now routinely ask you to explain attention and why transformers replaced RNNs for most sequence tasks. Conceptual clarity here matters as much as code.

Topic	What You'll Learn
1. Neural Network Fundamentals	Forward/backward propagation, activation functions, loss functions and optimizers, built from first principles.
2. PyTorch in Depth	Tensors, autograd, the training loop, and why PyTorch is the framework current ML hiring favors over TensorFlow.
3. Convolutional Neural Networks (CNNs)	Image classification and the intuition behind convolution, pooling and feature maps.
4. Recurrent Networks & Transformers, Conceptually	RNNs/LSTMs for sequences, and the transformer architecture (attention, encoder/decoder) that underlies every modern LLM.
5. Transfer Learning	Fine-tuning a pretrained model instead of training from scratch, the way real teams actually work.
5.1) Where this shows up in real jobs	Interviewers now routinely ask you to explain attention and why transformers replaced RNNs for most sequence tasks. Conceptual clarity here matters as much as code.

3 LLM & GenAI Engineering 2 wks ▾

Topic	What You'll Learn
1. Hugging Face Ecosystem	Transformers, datasets and model hubs: loading, running and adapting pretrained language models.
2. Prompt Engineering	Zero-shot, few-shot, chain-of-thought and structured-output prompting, with the discipline of testing prompts like code.
3. Fine-Tuning Basics	LoRA/QLoRA and instruction fine-tuning, and knowing when fine-tuning is worth it versus a well-built prompt.
4. Building with LangChain & LlamaIndex	Chains, agents, document loaders and indexes — the two most-used orchestration frameworks in real GenAI codebases.
5. End-to-End RAG Pipeline Design	Chunking, embedding, retrieval and generation wired together as a production pipeline, not a notebook demo.
5.1) Why this module exists	"GenAI engineer" job descriptions in 2026 assume you can design a full RAG pipeline end to end, not just call a chat completion API.

Topic	What You'll Learn
1. Hugging Face Ecosystem	Transformers, datasets and model hubs: loading, running and adapting pretrained language models.
2. Prompt Engineering	Zero-shot, few-shot, chain-of-thought and structured-output prompting, with the discipline of testing prompts like code.
3. Fine-Tuning Basics	LoRA/QLoRA and instruction fine-tuning, and knowing when fine-tuning is worth it versus a well-built prompt.
4. Building with LangChain & LlamaIndex	Chains, agents, document loaders and indexes — the two most-used orchestration frameworks in real GenAI codebases.
5. End-to-End RAG Pipeline Design	Chunking, embedding, retrieval and generation wired together as a production pipeline, not a notebook demo.
5.1) Why this module exists	"GenAI engineer" job descriptions in 2026 assume you can design a full RAG pipeline end to end, not just call a chat completion API.

4 Embeddings & Vector Databases 1 wk ▾

Topic	What You'll Learn
1. Embeddings in Depth	How text (and images) become vectors, embedding models, and measuring similarity correctly.
2. Semantic Search	Building search that matches meaning, not just keywords, and evaluating retrieval quality honestly.
3. Pinecone & Weaviate	Managed vector database options: indexing, querying and metadata filtering in production-style setups.
4. Chroma & Milvus	Open-source and self-hosted vector store alternatives, and the tradeoffs against a managed service.
5. Scaling Retrieval	Indexing strategies (HNSW, IVF) and the tradeoffs between recall, latency and cost at real data volumes.

5 Comprehensive Interview Preparation & Career Launch 1.5 wks ▾

Topic	What You'll Learn
1. ML System Design Interviews	Working through real prompts ("design a recommendation system," "design a fraud-detection pipeline") from requirements to a defensible architecture.
2. Take-Home Assignment Strategy	Managing scope, time and presentation on the take-home assignments most ML interview loops now include.
3. ML/AI Interview Question Bank	Classical ML, deep learning, LLM/GenAI and MLOps questions asked in real interviews, reviewed and explained.
4. Salary Negotiation & Offer Evaluation	Reading an ML/AI offer letter and negotiating from an informed position.
5. Placement Assistance	Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.
6. 🚀 Final Mock Interview	A full-length, role-realistic ML/AI Engineer interview, end to end, covering technical and behavioral rounds.

Topic	What You'll Learn
1. ML System Design Interviews	Working through real prompts ("design a recommendation system," "design a fraud-detection pipeline") from requirements to a defensible architecture.
2. Take-Home Assignment Strategy	Managing scope, time and presentation on the take-home assignments most ML interview loops now include.
3. ML/AI Interview Question Bank	Classical ML, deep learning, LLM/GenAI and MLOps questions asked in real interviews, reviewed and explained.
4. Salary Negotiation & Offer Evaluation	Reading an ML/AI offer letter and negotiating from an informed position.
5. Placement Assistance	Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.
6. 🚀 Final Mock Interview	A full-length, role-realistic ML/AI Engineer interview, end to end, covering technical and behavioral rounds.

1 Python for ML/AI, SQL & Statistical Foundations 1.5 wks ▾

Topic	What You'll Learn
1. Python for Data & ML	NumPy, pandas, data manipulation and cleaning, plus async patterns for calling APIs and services without blocking.
2. Statistics & Probability for ML	Distributions, hypothesis testing, correlation vs. causation, and the statistical reasoning every ML claim rests on.
3. Linear Algebra & Calculus, Just Enough	Vectors, matrices and gradients explained the way you actually need them for ML, not a full university course.
4. SQL for ML Practitioners	Writing the joins and aggregations that pull training data out of a real production database.
5. Data Visualization	Matplotlib, Seaborn and exploratory data analysis (EDA) that actually surfaces what's wrong with a dataset before you model it.
5.1) Real-world starting point	Most ML tutorials start with a clean CSV. This module is deliberately paired with a messy, real-world dataset, because that's what every ML job actually hands you first.

Topic	What You'll Learn
1. Python for Data & ML	NumPy, pandas, data manipulation and cleaning, plus async patterns for calling APIs and services without blocking.
2. Statistics & Probability for ML	Distributions, hypothesis testing, correlation vs. causation, and the statistical reasoning every ML claim rests on.
3. Linear Algebra & Calculus, Just Enough	Vectors, matrices and gradients explained the way you actually need them for ML, not a full university course.
4. SQL for ML Practitioners	Writing the joins and aggregations that pull training data out of a real production database.
5. Data Visualization	Matplotlib, Seaborn and exploratory data analysis (EDA) that actually surfaces what's wrong with a dataset before you model it.
5.1) Real-world starting point	Most ML tutorials start with a clean CSV. This module is deliberately paired with a messy, real-world dataset, because that's what every ML job actually hands you first.

2 Classical Machine Learning 2 wks ▾

Topic	What You'll Learn
1. Supervised Learning	Regression and classification with scikit-learn: linear/logistic regression, decision trees, SVMs, and when each one is the right tool.
2. Gradient-Boosted Trees	XGBoost and LightGBM in depth, the models that still win most tabular-data problems in production.
3. Feature Engineering	Encoding, scaling, handling missing data and building features that actually move a model's performance.
4. Model Evaluation	Precision/recall, ROC-AUC, cross-validation, and reading a confusion matrix like an engineer, not just printing accuracy.
5. Unsupervised Learning	Clustering (k-means, hierarchical) and dimensionality reduction (PCA) for when there's no label to predict.
5.1) Why this module exists	Job postings that say "machine learning" overwhelmingly mean XGBoost on tabular data, not a neural network. Skipping straight to deep learning is the single most common gap in self-taught ML candidates.

Topic	What You'll Learn
1. Supervised Learning	Regression and classification with scikit-learn: linear/logistic regression, decision trees, SVMs, and when each one is the right tool.
2. Gradient-Boosted Trees	XGBoost and LightGBM in depth, the models that still win most tabular-data problems in production.
3. Feature Engineering	Encoding, scaling, handling missing data and building features that actually move a model's performance.
4. Model Evaluation	Precision/recall, ROC-AUC, cross-validation, and reading a confusion matrix like an engineer, not just printing accuracy.
5. Unsupervised Learning	Clustering (k-means, hierarchical) and dimensionality reduction (PCA) for when there's no label to predict.
5.1) Why this module exists	Job postings that say "machine learning" overwhelmingly mean XGBoost on tabular data, not a neural network. Skipping straight to deep learning is the single most common gap in self-taught ML candidates.

3 MLOps — Serving, Tracking & Deploying Models 2 wks ▾

Topic	What You'll Learn
1. Serving Models with FastAPI	Wrapping a trained model behind a real, documented API endpoint others can call.
2. Containerization with Docker	Packaging a model and its dependencies so "works on my laptop" stops being an excuse.
3. Kubernetes Basics for ML	Deploying and scaling containerized model services, at the level an ML engineer (not a dedicated SRE) is expected to know.
4. Experiment Tracking with MLflow	Logging runs, comparing models and managing the model registry instead of a spreadsheet of results.
5. Pipeline Orchestration with Airflow	Scheduling and monitoring the data and training pipelines that keep a model current.
6. Model Portability with ONNX	Exporting a model to run efficiently across frameworks and hardware.
6.1) Why this module exists	A model that only runs in a notebook isn't shippable. This module is deliberately systems-first: every model built earlier in the course gets served, containerized and tracked here, not left as a one-off Jupyter cell.

Topic	What You'll Learn
1. Serving Models with FastAPI	Wrapping a trained model behind a real, documented API endpoint others can call.
2. Containerization with Docker	Packaging a model and its dependencies so "works on my laptop" stops being an excuse.
3. Kubernetes Basics for ML	Deploying and scaling containerized model services, at the level an ML engineer (not a dedicated SRE) is expected to know.
4. Experiment Tracking with MLflow	Logging runs, comparing models and managing the model registry instead of a spreadsheet of results.
5. Pipeline Orchestration with Airflow	Scheduling and monitoring the data and training pipelines that keep a model current.
6. Model Portability with ONNX	Exporting a model to run efficiently across frameworks and hardware.
6.1) Why this module exists	A model that only runs in a notebook isn't shippable. This module is deliberately systems-first: every model built earlier in the course gets served, containerized and tracked here, not left as a one-off Jupyter cell.

4 AI Ethics, Responsible AI & Governance 0.5 wk ▾

Topic	What You'll Learn
1. Bias & Fairness in ML	Where bias enters a pipeline (data, labeling, features) and how to test for it, not just assert it away.
2. Model Transparency & Explainability	SHAP/LIME-style techniques for explaining a model's predictions to a non-technical stakeholder.
3. Data Privacy & Responsible AI Practice	Handling training data responsibly, and the governance expectations real organizations now hold ML teams to.
4. AI Regulation, Practically	What emerging AI governance frameworks mean for a working ML engineer's day-to-day decisions, at a working-knowledge level.

5 Comprehensive Interview Preparation & Career Launch 1.5 wks ▾

Topic	What You'll Learn
1. ML System Design Interviews	Working through real prompts ("design a recommendation system," "design a fraud-detection pipeline") from requirements to a defensible architecture.
2. Take-Home Assignment Strategy	Managing scope, time and presentation on the take-home assignments most ML interview loops now include.
3. ML/AI Interview Question Bank	Classical ML, deep learning, LLM/GenAI and MLOps questions asked in real interviews, reviewed and explained.
4. Salary Negotiation & Offer Evaluation	Reading an ML/AI offer letter and negotiating from an informed position.
5. Placement Assistance	Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.
6. 🚀 Final Mock Interview	A full-length, role-realistic ML/AI Engineer interview, end to end, covering technical and behavioral rounds.

Topic	What You'll Learn
1. ML System Design Interviews	Working through real prompts ("design a recommendation system," "design a fraud-detection pipeline") from requirements to a defensible architecture.
2. Take-Home Assignment Strategy	Managing scope, time and presentation on the take-home assignments most ML interview loops now include.
3. ML/AI Interview Question Bank	Classical ML, deep learning, LLM/GenAI and MLOps questions asked in real interviews, reviewed and explained.
4. Salary Negotiation & Offer Evaluation	Reading an ML/AI offer letter and negotiating from an informed position.
5. Placement Assistance	Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.
6. 🚀 Final Mock Interview	A full-length, role-realistic ML/AI Engineer interview, end to end, covering technical and behavioral rounds.

1 LLM & GenAI Engineering 2 wks ▾

Topic	What You'll Learn
1. Hugging Face Ecosystem	Transformers, datasets and model hubs: loading, running and adapting pretrained language models.
2. Prompt Engineering	Zero-shot, few-shot, chain-of-thought and structured-output prompting, with the discipline of testing prompts like code.
3. Fine-Tuning Basics	LoRA/QLoRA and instruction fine-tuning, and knowing when fine-tuning is worth it versus a well-built prompt.
4. Building with LangChain & LlamaIndex	Chains, agents, document loaders and indexes — the two most-used orchestration frameworks in real GenAI codebases.
5. End-to-End RAG Pipeline Design	Chunking, embedding, retrieval and generation wired together as a production pipeline, not a notebook demo.
5.1) Why this module exists	"GenAI engineer" job descriptions in 2026 assume you can design a full RAG pipeline end to end, not just call a chat completion API.

Topic	What You'll Learn
1. Hugging Face Ecosystem	Transformers, datasets and model hubs: loading, running and adapting pretrained language models.
2. Prompt Engineering	Zero-shot, few-shot, chain-of-thought and structured-output prompting, with the discipline of testing prompts like code.
3. Fine-Tuning Basics	LoRA/QLoRA and instruction fine-tuning, and knowing when fine-tuning is worth it versus a well-built prompt.
4. Building with LangChain & LlamaIndex	Chains, agents, document loaders and indexes — the two most-used orchestration frameworks in real GenAI codebases.
5. End-to-End RAG Pipeline Design	Chunking, embedding, retrieval and generation wired together as a production pipeline, not a notebook demo.
5.1) Why this module exists	"GenAI engineer" job descriptions in 2026 assume you can design a full RAG pipeline end to end, not just call a chat completion API.

2 Embeddings & Vector Databases 1 wk ▾

Topic	What You'll Learn
1. Embeddings in Depth	How text (and images) become vectors, embedding models, and measuring similarity correctly.
2. Semantic Search	Building search that matches meaning, not just keywords, and evaluating retrieval quality honestly.
3. Pinecone & Weaviate	Managed vector database options: indexing, querying and metadata filtering in production-style setups.
4. Chroma & Milvus	Open-source and self-hosted vector store alternatives, and the tradeoffs against a managed service.
5. Scaling Retrieval	Indexing strategies (HNSW, IVF) and the tradeoffs between recall, latency and cost at real data volumes.

3 AI Agent Frameworks & Tool Integration 1.5 wks ▾

Topic	What You'll Learn
1. Agent Fundamentals	What makes a system an "agent" rather than a single LLM call: planning, memory and tool use.
2. LangGraph	Building stateful, multi-step agent workflows as an explicit graph instead of an opaque chain.
3. AutoGen & CrewAI	Multi-agent orchestration frameworks for coordinating several specialized agents on one task.
4. Model Context Protocol (MCP)	The emerging standard for connecting agents and models to external tools and data sources, and why teams are converging on it.
5. Agent Evaluation & Guardrails	Testing agent behavior, containing failure modes, and knowing when an agent is the wrong solution to a problem.
5.1) Where this shows up in real jobs	"Agentic AI" is now a standing line item in GenAI job postings. Employers increasingly expect familiarity with MCP specifically as the connective layer between agents and tools.

Topic	What You'll Learn
1. Agent Fundamentals	What makes a system an "agent" rather than a single LLM call: planning, memory and tool use.
2. LangGraph	Building stateful, multi-step agent workflows as an explicit graph instead of an opaque chain.
3. AutoGen & CrewAI	Multi-agent orchestration frameworks for coordinating several specialized agents on one task.
4. Model Context Protocol (MCP)	The emerging standard for connecting agents and models to external tools and data sources, and why teams are converging on it.
5. Agent Evaluation & Guardrails	Testing agent behavior, containing failure modes, and knowing when an agent is the wrong solution to a problem.
5.1) Where this shows up in real jobs	"Agentic AI" is now a standing line item in GenAI job postings. Employers increasingly expect familiarity with MCP specifically as the connective layer between agents and tools.

4 AI Ethics, Responsible AI & Governance 0.5 wk ▾

Topic	What You'll Learn
1. Bias & Fairness in ML	Where bias enters a pipeline (data, labeling, features) and how to test for it, not just assert it away.
2. Model Transparency & Explainability	SHAP/LIME-style techniques for explaining a model's predictions to a non-technical stakeholder.
3. Data Privacy & Responsible AI Practice	Handling training data responsibly, and the governance expectations real organizations now hold ML teams to.
4. AI Regulation, Practically	What emerging AI governance frameworks mean for a working ML engineer's day-to-day decisions, at a working-knowledge level.

5 Comprehensive Interview Preparation & Career Launch 1.5 wks ▾

Topic	What You'll Learn
1. ML System Design Interviews	Working through real prompts ("design a recommendation system," "design a fraud-detection pipeline") from requirements to a defensible architecture.
2. Take-Home Assignment Strategy	Managing scope, time and presentation on the take-home assignments most ML interview loops now include.
3. ML/AI Interview Question Bank	Classical ML, deep learning, LLM/GenAI and MLOps questions asked in real interviews, reviewed and explained.
4. Salary Negotiation & Offer Evaluation	Reading an ML/AI offer letter and negotiating from an informed position.
5. Placement Assistance	Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.
6. 🚀 Final Mock Interview	A full-length, role-realistic ML/AI Engineer interview, end to end, covering technical and behavioral rounds.

Topic	What You'll Learn
1. ML System Design Interviews	Working through real prompts ("design a recommendation system," "design a fraud-detection pipeline") from requirements to a defensible architecture.
2. Take-Home Assignment Strategy	Managing scope, time and presentation on the take-home assignments most ML interview loops now include.
3. ML/AI Interview Question Bank	Classical ML, deep learning, LLM/GenAI and MLOps questions asked in real interviews, reviewed and explained.
4. Salary Negotiation & Offer Evaluation	Reading an ML/AI offer letter and negotiating from an informed position.
5. Placement Assistance	Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.
6. 🚀 Final Mock Interview	A full-length, role-realistic ML/AI Engineer interview, end to end, covering technical and behavioral rounds.

1 Python for ML/AI, SQL & Statistical Foundations 1.5 wks ▾

Topic	What You'll Learn
1. Python for Data & ML	NumPy, pandas, data manipulation and cleaning, plus async patterns for calling APIs and services without blocking.
2. Statistics & Probability for ML	Distributions, hypothesis testing, correlation vs. causation, and the statistical reasoning every ML claim rests on.
3. Linear Algebra & Calculus, Just Enough	Vectors, matrices and gradients explained the way you actually need them for ML, not a full university course.
4. SQL for ML Practitioners	Writing the joins and aggregations that pull training data out of a real production database.
5. Data Visualization	Matplotlib, Seaborn and exploratory data analysis (EDA) that actually surfaces what's wrong with a dataset before you model it.
5.1) Real-world starting point	Most ML tutorials start with a clean CSV. This module is deliberately paired with a messy, real-world dataset, because that's what every ML job actually hands you first.

Topic	What You'll Learn
1. Python for Data & ML	NumPy, pandas, data manipulation and cleaning, plus async patterns for calling APIs and services without blocking.
2. Statistics & Probability for ML	Distributions, hypothesis testing, correlation vs. causation, and the statistical reasoning every ML claim rests on.
3. Linear Algebra & Calculus, Just Enough	Vectors, matrices and gradients explained the way you actually need them for ML, not a full university course.
4. SQL for ML Practitioners	Writing the joins and aggregations that pull training data out of a real production database.
5. Data Visualization	Matplotlib, Seaborn and exploratory data analysis (EDA) that actually surfaces what's wrong with a dataset before you model it.
5.1) Real-world starting point	Most ML tutorials start with a clean CSV. This module is deliberately paired with a messy, real-world dataset, because that's what every ML job actually hands you first.

2 Classical Machine Learning 2 wks ▾

Topic	What You'll Learn
1. Supervised Learning	Regression and classification with scikit-learn: linear/logistic regression, decision trees, SVMs, and when each one is the right tool.
2. Gradient-Boosted Trees	XGBoost and LightGBM in depth, the models that still win most tabular-data problems in production.
3. Feature Engineering	Encoding, scaling, handling missing data and building features that actually move a model's performance.
4. Model Evaluation	Precision/recall, ROC-AUC, cross-validation, and reading a confusion matrix like an engineer, not just printing accuracy.
5. Unsupervised Learning	Clustering (k-means, hierarchical) and dimensionality reduction (PCA) for when there's no label to predict.
5.1) Why this module exists	Job postings that say "machine learning" overwhelmingly mean XGBoost on tabular data, not a neural network. Skipping straight to deep learning is the single most common gap in self-taught ML candidates.

Topic	What You'll Learn
1. Supervised Learning	Regression and classification with scikit-learn: linear/logistic regression, decision trees, SVMs, and when each one is the right tool.
2. Gradient-Boosted Trees	XGBoost and LightGBM in depth, the models that still win most tabular-data problems in production.
3. Feature Engineering	Encoding, scaling, handling missing data and building features that actually move a model's performance.
4. Model Evaluation	Precision/recall, ROC-AUC, cross-validation, and reading a confusion matrix like an engineer, not just printing accuracy.
5. Unsupervised Learning	Clustering (k-means, hierarchical) and dimensionality reduction (PCA) for when there's no label to predict.
5.1) Why this module exists	Job postings that say "machine learning" overwhelmingly mean XGBoost on tabular data, not a neural network. Skipping straight to deep learning is the single most common gap in self-taught ML candidates.

3 Deep Learning with PyTorch 2 wks ▾

Topic	What You'll Learn
1. Neural Network Fundamentals	Forward/backward propagation, activation functions, loss functions and optimizers, built from first principles.
2. PyTorch in Depth	Tensors, autograd, the training loop, and why PyTorch is the framework current ML hiring favors over TensorFlow.
3. Convolutional Neural Networks (CNNs)	Image classification and the intuition behind convolution, pooling and feature maps.
4. Recurrent Networks & Transformers, Conceptually	RNNs/LSTMs for sequences, and the transformer architecture (attention, encoder/decoder) that underlies every modern LLM.
5. Transfer Learning	Fine-tuning a pretrained model instead of training from scratch, the way real teams actually work.
5.1) Where this shows up in real jobs	Interviewers now routinely ask you to explain attention and why transformers replaced RNNs for most sequence tasks. Conceptual clarity here matters as much as code.

Topic	What You'll Learn
1. Neural Network Fundamentals	Forward/backward propagation, activation functions, loss functions and optimizers, built from first principles.
2. PyTorch in Depth	Tensors, autograd, the training loop, and why PyTorch is the framework current ML hiring favors over TensorFlow.
3. Convolutional Neural Networks (CNNs)	Image classification and the intuition behind convolution, pooling and feature maps.
4. Recurrent Networks & Transformers, Conceptually	RNNs/LSTMs for sequences, and the transformer architecture (attention, encoder/decoder) that underlies every modern LLM.
5. Transfer Learning	Fine-tuning a pretrained model instead of training from scratch, the way real teams actually work.
5.1) Where this shows up in real jobs	Interviewers now routinely ask you to explain attention and why transformers replaced RNNs for most sequence tasks. Conceptual clarity here matters as much as code.

4 LLM & GenAI Engineering 2 wks ▾

Topic	What You'll Learn
1. Hugging Face Ecosystem	Transformers, datasets and model hubs: loading, running and adapting pretrained language models.
2. Prompt Engineering	Zero-shot, few-shot, chain-of-thought and structured-output prompting, with the discipline of testing prompts like code.
3. Fine-Tuning Basics	LoRA/QLoRA and instruction fine-tuning, and knowing when fine-tuning is worth it versus a well-built prompt.
4. Building with LangChain & LlamaIndex	Chains, agents, document loaders and indexes — the two most-used orchestration frameworks in real GenAI codebases.
5. End-to-End RAG Pipeline Design	Chunking, embedding, retrieval and generation wired together as a production pipeline, not a notebook demo.
5.1) Why this module exists	"GenAI engineer" job descriptions in 2026 assume you can design a full RAG pipeline end to end, not just call a chat completion API.

Topic	What You'll Learn
1. Hugging Face Ecosystem	Transformers, datasets and model hubs: loading, running and adapting pretrained language models.
2. Prompt Engineering	Zero-shot, few-shot, chain-of-thought and structured-output prompting, with the discipline of testing prompts like code.
3. Fine-Tuning Basics	LoRA/QLoRA and instruction fine-tuning, and knowing when fine-tuning is worth it versus a well-built prompt.
4. Building with LangChain & LlamaIndex	Chains, agents, document loaders and indexes — the two most-used orchestration frameworks in real GenAI codebases.
5. End-to-End RAG Pipeline Design	Chunking, embedding, retrieval and generation wired together as a production pipeline, not a notebook demo.
5.1) Why this module exists	"GenAI engineer" job descriptions in 2026 assume you can design a full RAG pipeline end to end, not just call a chat completion API.

5 Embeddings & Vector Databases 1 wk ▾

Topic	What You'll Learn
1. Embeddings in Depth	How text (and images) become vectors, embedding models, and measuring similarity correctly.
2. Semantic Search	Building search that matches meaning, not just keywords, and evaluating retrieval quality honestly.
3. Pinecone & Weaviate	Managed vector database options: indexing, querying and metadata filtering in production-style setups.
4. Chroma & Milvus	Open-source and self-hosted vector store alternatives, and the tradeoffs against a managed service.
5. Scaling Retrieval	Indexing strategies (HNSW, IVF) and the tradeoffs between recall, latency and cost at real data volumes.

6 MLOps — Serving, Tracking & Deploying Models 2 wks ▾

Topic	What You'll Learn
1. Serving Models with FastAPI	Wrapping a trained model behind a real, documented API endpoint others can call.
2. Containerization with Docker	Packaging a model and its dependencies so "works on my laptop" stops being an excuse.
3. Kubernetes Basics for ML	Deploying and scaling containerized model services, at the level an ML engineer (not a dedicated SRE) is expected to know.
4. Experiment Tracking with MLflow	Logging runs, comparing models and managing the model registry instead of a spreadsheet of results.
5. Pipeline Orchestration with Airflow	Scheduling and monitoring the data and training pipelines that keep a model current.
6. Model Portability with ONNX	Exporting a model to run efficiently across frameworks and hardware.
6.1) Why this module exists	A model that only runs in a notebook isn't shippable. This module is deliberately systems-first: every model built earlier in the course gets served, containerized and tracked here, not left as a one-off Jupyter cell.

Topic	What You'll Learn
1. Serving Models with FastAPI	Wrapping a trained model behind a real, documented API endpoint others can call.
2. Containerization with Docker	Packaging a model and its dependencies so "works on my laptop" stops being an excuse.
3. Kubernetes Basics for ML	Deploying and scaling containerized model services, at the level an ML engineer (not a dedicated SRE) is expected to know.
4. Experiment Tracking with MLflow	Logging runs, comparing models and managing the model registry instead of a spreadsheet of results.
5. Pipeline Orchestration with Airflow	Scheduling and monitoring the data and training pipelines that keep a model current.
6. Model Portability with ONNX	Exporting a model to run efficiently across frameworks and hardware.
6.1) Why this module exists	A model that only runs in a notebook isn't shippable. This module is deliberately systems-first: every model built earlier in the course gets served, containerized and tracked here, not left as a one-off Jupyter cell.

7 AI Agent Frameworks & Tool Integration 1.5 wks ▾

Topic	What You'll Learn
1. Agent Fundamentals	What makes a system an "agent" rather than a single LLM call: planning, memory and tool use.
2. LangGraph	Building stateful, multi-step agent workflows as an explicit graph instead of an opaque chain.
3. AutoGen & CrewAI	Multi-agent orchestration frameworks for coordinating several specialized agents on one task.
4. Model Context Protocol (MCP)	The emerging standard for connecting agents and models to external tools and data sources, and why teams are converging on it.
5. Agent Evaluation & Guardrails	Testing agent behavior, containing failure modes, and knowing when an agent is the wrong solution to a problem.
5.1) Where this shows up in real jobs	"Agentic AI" is now a standing line item in GenAI job postings. Employers increasingly expect familiarity with MCP specifically as the connective layer between agents and tools.

Topic	What You'll Learn
1. Agent Fundamentals	What makes a system an "agent" rather than a single LLM call: planning, memory and tool use.
2. LangGraph	Building stateful, multi-step agent workflows as an explicit graph instead of an opaque chain.
3. AutoGen & CrewAI	Multi-agent orchestration frameworks for coordinating several specialized agents on one task.
4. Model Context Protocol (MCP)	The emerging standard for connecting agents and models to external tools and data sources, and why teams are converging on it.
5. Agent Evaluation & Guardrails	Testing agent behavior, containing failure modes, and knowing when an agent is the wrong solution to a problem.
5.1) Where this shows up in real jobs	"Agentic AI" is now a standing line item in GenAI job postings. Employers increasingly expect familiarity with MCP specifically as the connective layer between agents and tools.

8 AI Ethics, Responsible AI & Governance 0.5 wk ▾

Topic	What You'll Learn
1. Bias & Fairness in ML	Where bias enters a pipeline (data, labeling, features) and how to test for it, not just assert it away.
2. Model Transparency & Explainability	SHAP/LIME-style techniques for explaining a model's predictions to a non-technical stakeholder.
3. Data Privacy & Responsible AI Practice	Handling training data responsibly, and the governance expectations real organizations now hold ML teams to.
4. AI Regulation, Practically	What emerging AI governance frameworks mean for a working ML engineer's day-to-day decisions, at a working-knowledge level.

9 Professional Communication & Technical Writing 0.5 wk ▾

Topic	What You'll Learn
1. Communicating with Non-Technical Stakeholders	Explaining a model's results and limitations to a product or business audience without jargon.
2. Technical Writing for ML/Data Teams	Writing a model card, a design doc and a clear README that a teammate can actually follow.
3. Documenting Experiments & Decisions	Recording why a modeling choice was made, not just what the final number was.
4. Presenting Findings	Structuring a results presentation so a non-ML audience walks away with the right conclusion.
4.1) Why this module exists	ML engineers spend a surprising share of their week explaining models to people who didn't build them. This is graded as a real skill here, not treated as an afterthought.

10 Behavioral Interview Mastery 1 wk ▾

Topic	What You'll Learn
1. The STAR Method	Structuring a behavioral answer (Situation, Task, Action, Result) so it lands instead of rambling.
2. Common Behavioral Themes	Conflict with a teammate, a missed deadline, a project that failed — building a bank of real, specific stories in advance.
3. Track-Specific Mock Interviews for ML/AI Engineer Roles	Practising the behavioral round the way it's actually run for ML/AI positions specifically, not a generic template.
4. Recorded Practice with Feedback	Reviewing your own recorded answers for filler words, pacing and clarity, and iterating.

11 Resume & Portfolio Building for ML Roles 0.5 wk ▾

Topic	What You'll Learn
1. Framing ML Projects on a Resume	Describing a model's impact and methodology the way a hiring manager actually scans it, not a tool list.
2. Building a GitHub Portfolio	Structuring project repos with clear READMEs, reproducible code and results a recruiter can verify in two minutes.
3. Kaggle & Published Projects	Using competitions and public projects to build a credible, checkable track record.
4. LinkedIn for ML/AI Roles	Positioning yourself for the specific ML/AI roles you're targeting, not a generic tech-job profile.

12 Comprehensive Interview Preparation & Career Launch 1.5 wks ▾

Topic	What You'll Learn
1. ML System Design Interviews	Working through real prompts ("design a recommendation system," "design a fraud-detection pipeline") from requirements to a defensible architecture.
2. Take-Home Assignment Strategy	Managing scope, time and presentation on the take-home assignments most ML interview loops now include.
3. ML/AI Interview Question Bank	Classical ML, deep learning, LLM/GenAI and MLOps questions asked in real interviews, reviewed and explained.
4. Salary Negotiation & Offer Evaluation	Reading an ML/AI offer letter and negotiating from an informed position.
5. Placement Assistance	Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.
6. 🚀 Final Mock Interview	A full-length, role-realistic ML/AI Engineer interview, end to end, covering technical and behavioral rounds.

Topic	What You'll Learn
1. ML System Design Interviews	Working through real prompts ("design a recommendation system," "design a fraud-detection pipeline") from requirements to a defensible architecture.
2. Take-Home Assignment Strategy	Managing scope, time and presentation on the take-home assignments most ML interview loops now include.
3. ML/AI Interview Question Bank	Classical ML, deep learning, LLM/GenAI and MLOps questions asked in real interviews, reviewed and explained.
4. Salary Negotiation & Offer Evaluation	Reading an ML/AI offer letter and negotiating from an informed position.
5. Placement Assistance	Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.
6. 🚀 Final Mock Interview	A full-length, role-realistic ML/AI Engineer interview, end to end, covering technical and behavioral rounds.

The curriculum for this package has not been added yet.

WHAT YOU GET

This Course Includes

💻

Online

🎯

16 Weeks Duration (Flexible)

🖥

7 Real Industry Projects

🏆

Certificate on Completion

♾️

Lifetime Access to Recordings

💼

Job References to 50+ Companies

🎯

5 Mock Interview Rounds

🗣

Soft Skills & Communication

📱

WhatsApp Batch Support

📥

Brochure & Syllabus Download

🛠 Tools Covered:

PythonNumPypandasscikit-learnXGBoostLightGBMPyTorchHugging FaceLangChainLlamaIndexPineconeWeaviateChromaMilvusFastAPIDockerKubernetesMLflowAirflowONNXLangGraphAutoGenCrewAIMCP

⬇️ Download Syllabus 💬 Enquire About This Course

KEEP EXPLORING

Get Your Related Courses

View All Courses

AI, ML & Soft Skills

Soft Skills & Interview Readiness Add-On

Zoom/Google MeetSlackLinkedInResume templates

22% OFF Standalone or add-on

AI, ML & Soft Skills

AI & GenAI Add-On — Practical AI Skills for Tech Professionals

ChatGPTClaudeGeminiGitHub CopilotClaude Code+10 more

20% OFF Add-on ready

Software Development

Front-End & UI/UX Development — React, Next.js & Design Systems

FigmaReactTypeScriptNext.jsTailwind CSS+12 more

15% OFF High-demand skill set

YOUR MENTOR

Who teaches this course

Sessions are taught by someone who does this work, not by a full-time trainer reading slides.

👨‍🏫

Fourcup AI/ML Faculty

Real Industry Trainers, Not Just Instructors

Every Fourcup trainer has a minimum of 8+ years of total IT industry experience and 5+ years of specific, hands-on experience in the exact tool or domain they teach. They are currently working professionals — the frameworks, edge cases and interview questions taught are what's actually used in live projects today. Mock-interview mentors are the same trainers who conduct real technical interviews at their day jobs.

8+ years experience Active practitioner

KEEP EXPLORING

Get Your Related Courses

View All Courses

AI, ML & Soft Skills

Soft Skills & Interview Readiness Add-On

Zoom/Google MeetSlackLinkedInResume templates

22% OFF Standalone or add-on

AI, ML & Soft Skills

AI & GenAI Add-On — Practical AI Skills for Tech Professionals

ChatGPTClaudeGeminiGitHub CopilotClaude Code+10 more

20% OFF Add-on ready

Software Development

Front-End & UI/UX Development — React, Next.js & Design Systems

FigmaReactTypeScriptNext.jsTailwind CSS+12 more

15% OFF High-demand skill set

BATCHES

When you can start

Weekday, weekend and fast-track options. Seats are limited so sessions stay small enough to ask questions in.

Batch dates for this course have not been added yet.

KEEP EXPLORING

Get Your Related Courses

View All Courses

AI, ML & Soft Skills

Soft Skills & Interview Readiness Add-On

Zoom/Google MeetSlackLinkedInResume templates

22% OFF Standalone or add-on

AI, ML & Soft Skills

AI & GenAI Add-On — Practical AI Skills for Tech Professionals

ChatGPTClaudeGeminiGitHub CopilotClaude Code+10 more

20% OFF Add-on ready

Software Development

Front-End & UI/UX Development — React, Next.js & Design Systems

FigmaReactTypeScriptNext.jsTailwind CSS+12 more

15% OFF High-demand skill set

FAQ

Questions people actually ask

If yours is not here, call us — a working engineer will answer, not a call centre.

No questions have been added for this course yet.

KEEP EXPLORING

Get Your Related Courses

View All Courses

AI, ML & Soft Skills

Soft Skills & Interview Readiness Add-On

Zoom/Google MeetSlackLinkedInResume templates

22% OFF Standalone or add-on

AI, ML & Soft Skills

AI & GenAI Add-On — Practical AI Skills for Tech Professionals

ChatGPTClaudeGeminiGitHub CopilotClaude Code+10 more

20% OFF Add-on ready

Software Development

Front-End & UI/UX Development — React, Next.js & Design Systems

FigmaReactTypeScriptNext.jsTailwind CSS+12 more

15% OFF High-demand skill set