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Enterprise AI/ML Track 2026

AI/ML Engineering with GenAI
End-to-End Machine Learning, Deep Neural Networks & Production GenAI.
Get Placed in Top MNCs.

Comprehensive AI/ML Engineering with GenAI training covering Scikit-Learn, PyTorch, Transformers, LLM Fine-Tuning (LoRA), High-Throughput Model Serving & 100% placement support.

4.9/5 Rating (1.8k+ Reviews)
120+ Hours Live Hands-on
20+ ML & GenAI Tools Modern Stack
Production Capstone Labs
100% Placement Support
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AI/ML Engineering with GenAI Certification Course Student Working on AI Projects
Python
Python
PyTorch
PyTorch
Hugging Face
Hugging Face
Scikit-Learn
Scikit-Learn
TensorFlow
TensorFlow
vLLM
vLLM
MLflow
MLflow
Docker
Docker
๐Ÿ–ฅ๏ธLive Interactive Coding
๐Ÿ‘คAI Principal Mentors
๐Ÿ“‹Enterprise Capstones
๐Ÿ’ผPlacement Assurance

Start Your Career in AI/ML Engineering with GenAI

Get personalized curriculum & free 1-on-1 counseling.

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Average Career Growth
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◆ Enterprise-Aligned Curriculum

Complete AI/ML Engineering with GenAI Curriculum — Foundations to Enterprise Scale

Engineered in collaboration with principal engineers from leading product and Fortune 500 AI teams.

01

Mathematical Foundations & High-Performance Python for ML

⏱ In-Depth Module
โšก
  • Vectorized Array Operations & Matrix Decompositions (SVD, PCA)
  • Multivariate Calculus: Gradients, Jacobians, Hessians & Chain Rule
  • Probability Distributions, Bayesian Inference & Maximum Likelihood Estimation
→
02

Supervised & Unsupervised Machine Learning Algorithms

⏱ In-Depth Module
โšก
  • Linear & Logistic Regression with L1/L2 Regularization (Lasso/Ridge/ElasticNet)
  • Tree Algorithms: Decision Trees, Random Forests, Gradient Boosted Trees
  • State-of-the-Art Gradient Boosters: XGBoost, LightGBM, CatBoost
→
03

Deep Learning & Neural Networks with PyTorch

⏱ In-Depth Module
โšก
  • PyTorch Tensors, Autograd & Building Custom nn.Module Architectures
  • Loss Functions, Optimizers (AdamW, SGD with Momentum) & Schedulers
  • Preventing Overfitting: Dropout, Weight Decay, LayerNorm & Batch Normalization
→
04

LLM Fine-Tuning: PEFT, LoRA & QLoRA

⏱ In-Depth Module
โšก
  • Full Fine-Tuning vs Parameter-Efficient Fine-Tuning (PEFT)
  • Low-Rank Adaptation (LoRA) & Quantized LoRA (QLoRA) Mathematics
  • Data Preparation for Instruction Fine-Tuning (Alpaca & ShareGPT formats)
→

Detailed Module-by-Module Breakdown

Click each module below to explore technical topics, coding labs, and tools covered.

Module 01

Mathematical Foundations & High-Performance Python for ML

18 Hours

Solidify linear algebra, calculus, probability, and high-performance vector computation with NumPy and Pandas.

Core Topics & Competencies:

  • โœ” Vectorized Array Operations & Matrix Decompositions (SVD, PCA)
  • โœ” Multivariate Calculus: Gradients, Jacobians, Hessians & Chain Rule
  • โœ” Probability Distributions, Bayesian Inference & Maximum Likelihood Estimation
  • โœ” Feature Engineering: Outlier Treatment, Normalization & Target Encoding
  • โœ” Handling Imbalanced Datasets with SMOTE, Tomek Links & Class Weights
๐Ÿ’ป Hands-on Capstone Lab:

High-Throughput Feature Pipeline for Millions of Financial Transactions.

Python 3.11NumPyPandasSciPyJupyterLab
Module 02

Supervised & Unsupervised Machine Learning Algorithms

22 Hours

Master classical algorithms, ensemble methods, and hyperparameter tuning with Scikit-Learn and XGBoost.

Core Topics & Competencies:

  • โœ” Linear & Logistic Regression with L1/L2 Regularization (Lasso/Ridge/ElasticNet)
  • โœ” Tree Algorithms: Decision Trees, Random Forests, Gradient Boosted Trees
  • โœ” State-of-the-Art Gradient Boosters: XGBoost, LightGBM, CatBoost
  • โœ” Unsupervised Learning: K-Means++, DBSCAN, Hierarchical Clustering
  • โœ” Dimensionality Reduction: PCA, t-SNE & UMAP
  • โœ” Cross-Validation Strategies & Rigorous Model Evaluation (ROC-AUC, PR-AUC, F1)
๐Ÿ’ป Hands-on Capstone Lab:

Enterprise Customer Attrition & Lifetime Value Prediction Engine with XGBoost & Optuna Tuning.

Scikit-LearnXGBoostLightGBMOptunaSeaborn
Module 03

Deep Learning & Neural Networks with PyTorch

24 Hours

Build deep multi-layer perceptrons, convolutional networks, and recurrent sequence models using PyTorch.

Core Topics & Competencies:

  • โœ” PyTorch Tensors, Autograd & Building Custom nn.Module Architectures
  • โœ” Loss Functions, Optimizers (AdamW, SGD with Momentum) & Schedulers
  • โœ” Preventing Overfitting: Dropout, Weight Decay, LayerNorm & Batch Normalization
  • โœ” Convolutional Neural Networks (CNNs): ResNet, EfficientNet & Transfer Learning
  • โœ” Sequence Modeling: RNNs, LSTMs & Introduction to Self-Attention
๐Ÿ’ป Hands-on Capstone Lab:

Deep Computer Vision Defect Detection System with PyTorch and Transfer Learning.

PyTorchTorchvisionCUDATensorBoard
Module 04

Transformers, Hugging Face & Natural Language Processing

20 Hours

Master transformer architectures, tokenizers, BERT, GPT, and modern NLP pipelines.

Core Topics & Competencies:

  • โœ” Attention Is All You Need: Scaled Dot-Product Attention & Positional Encoding
  • โœ” Hugging Face Transformers Ecosystem: Pipelines, Datasets, AutoModel & AutoTokenizer
  • โœ” Encoder Models for Text Classification & Named Entity Recognition (NER)
  • โœ” Decoder Models for Autoregressive Text Generation
  • โœ” Embedding Generation & Semantic Similarity Search
๐Ÿ’ป Hands-on Capstone Lab:

Multi-Class Financial Legal Contract Classification and Entity Extraction with Hugging Face.

Hugging FacePyTorchTransformersDatasets
Module 05

LLM Fine-Tuning: PEFT, LoRA & QLoRA

20 Hours

Customize state-of-the-art open models (Llama, Mistral, Qwen) on proprietary enterprise datasets.

Core Topics & Competencies:

  • โœ” Full Fine-Tuning vs Parameter-Efficient Fine-Tuning (PEFT)
  • โœ” Low-Rank Adaptation (LoRA) & Quantized LoRA (QLoRA) Mathematics
  • โœ” Data Preparation for Instruction Fine-Tuning (Alpaca & ShareGPT formats)
  • โœ” Accelerating Training with Unsloth, Axolotl and FlashAttention-2
  • โœ” Supervised Fine-Tuning (SFT) Trainer & Direct Preference Optimization (DPO)
๐Ÿ’ป Hands-on Capstone Lab:

Fine-Tuning a 7B Parameter Medical Consultation LLM using QLoRA, Unsloth, and Custom Clinical Data.

UnslothPEFTLoRABitsAndBytesHugging Face TRL
Module 06

High-Throughput Model Serving, MLOps & Production Inference

16 Hours

Deploy low-latency AI models at enterprise scale with vLLM, Triton, and Docker microservices.

Core Topics & Competencies:

  • โœ” LLM Inference Optimization: PagedAttention, KV-Cache & Continuous Batching
  • โœ” High-Throughput Serving with vLLM & TensorRT-LLM
  • โœ” Packaging Models into REST APIs using FastAPI & Docker Containers
  • โœ” Experiment Tracking and Model Registry with MLflow
  • โœ” Model Monitoring for Data Drift & Performance Degradation
๐Ÿ’ป Hands-on Capstone Lab:

Sub-50ms Low-Latency Model Serving Cluster with vLLM, FastAPI, Docker & Prometheus Metrics.

vLLMFastAPIDockerMLflowPrometheus

Tools & Frameworks You Will Master

Gain hands-on proficiency in the exact modern tech stack used across Fortune 500 tech teams.

Python
Python
PyTorch
PyTorch
Hugging Face
Hugging Face
Scikit-Learn
Scikit-Learn
TensorFlow
TensorFlow
vLLM
vLLM
MLflow
MLflow
Docker
Docker
LAUNCHPAD PRO

Build Experience That Gets You Interview-Ready

Real project work. Agile exposure. Mentor feedback. A portfolio you can talk about in interviews.

Live Project Work
Agile + Jira Workflow
Team Collaboration
Mentor Code &
Project Reviews
Resume + Interview
Prep
Completion Certificate
Explore LaunchPad Pro → See What You'll Work On →
LaunchPad Pro Student Experience
Hands-on • Mentor-led

Already trained. Now build proof of your skills.

Turn learning into practical experience you can discuss with confidence.

  • Work on live enterprise projects
  • Build a real-world portfolio
  • Use Agile workflows & Jira
  • Get mentor feedback
  • Practice with mock interviews
Designed for job-focused learners
48,500+
Successful Learners
500+
Hiring Partners
โ‚น12.5 LPA
Highest Package
140%
Average Career Growth
100%
Interview Guarantee

Career & Salary Calculator

Explore verified 2026 compensation benchmarks and market demand curves across India's top tech hubs.

๐Ÿ’ฐ Estimated Salary Range
Market Data 2026
₹ 8.5 LPA – 16.5 LPA
Frontier AI Specialist | 1-3 Years | Delhi NCR
๐Ÿ“ˆEntry Level
₹ 7.5 – 9.5 LPA
๐Ÿ’ผMid Level
₹ 11.0 – 16.5 LPA
โญLead Architect
₹ 25.0+ LPA

Based on verified 2026 hiring data from Fortune 500 and Top MNC tech recruiters.

Salary Curve by Experience
High-Paying Frontier Track
7.5L
Fresher
12.5L
1 - 3 Yrs
18.5L
3 - 5 Yrs
28.0L
5 - 8 Yrs
42.0L+
8+ Yrs

Frequently Asked Questions

Everything you need to know about the AI/ML Engineering with GenAI Certification Course, batches, prerequisites & placement assurance.

How does this course combine Traditional ML with Generative AI?

Modern enterprise AI engineers need both! Traditional ML powers predictive analytics, risk scoring, and tabular algorithms (XGBoost, Scikit-Learn), while GenAI handles language and vision tasks. This course bridges both so you can engineer complete AI systems from predictive modeling to custom LLM fine-tuning.

Will I learn how to fine-tune open-source LLMs on custom datasets?

Yes! You will get hands-on experience fine-tuning modern models (Llama 3, Mistral, Qwen) using PEFT, LoRA, and QLoRA with high-performance tools like Unsloth and Hugging Face TRL.

What hardware is required for deep learning and fine-tuning labs?

You do not need an expensive local GPU! We guide you on using cloud GPU workstations (Google Colab Pro, RunPod, AWS EC2) and lightweight quantized frameworks so you can train models smoothly.