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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 & 1-on-1 Mock Interview SessionsDedicated Support.

4.9/5 Rating (1.8k+ Reviews)
120+ Hours Live Hands-on
20+ ML & GenAI Tools Modern Stack
Production Capstone Labs
1-on-1 Mock Interview SessionsDedicated Support Support
Global Certification
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& Real Data
100% Career Guidance & Interview Prep
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
🖥️Live Interactive Coding
👤AI Principal Mentors
📋Enterprise Capstones
💼Career Mentorship Assurance

Start Your Career in AI/ML Engineering with GenAI

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

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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
⚡
  • 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
⚡
  • 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
⚡
  • 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
⚡
  • 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:

  • ✔ Vectorized Array Operations & Matrix Decompositions (SVD, PCA)
  • ✔ Multivariate Calculus: Gradients, Jacobians, Hessians & Chain Rule
  • ✔ Probability Distributions, Bayesian Inference & Maximum Likelihood Estimation
  • ✔ Feature Engineering: Outlier Treatment, Normalization & Target Encoding
  • ✔ Handling Imbalanced Datasets with SMOTE, Tomek Links & Class Weights
💻 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:

  • ✔ 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
  • ✔ Unsupervised Learning: K-Means++, DBSCAN, Hierarchical Clustering
  • ✔ Dimensionality Reduction: PCA, t-SNE & UMAP
  • ✔ Cross-Validation Strategies & Rigorous Model Evaluation (ROC-AUC, PR-AUC, F1)
💻 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:

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

  • ✔ Attention Is All You Need: Scaled Dot-Product Attention & Positional Encoding
  • ✔ Hugging Face Transformers Ecosystem: Pipelines, Datasets, AutoModel & AutoTokenizer
  • ✔ Encoder Models for Text Classification & Named Entity Recognition (NER)
  • ✔ Decoder Models for Autoregressive Text Generation
  • ✔ Embedding Generation & Semantic Similarity Search
💻 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:

  • ✔ 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)
  • ✔ Accelerating Training with Unsloth, Axolotl and FlashAttention-2
  • ✔ Supervised Fine-Tuning (SFT) Trainer & Direct Preference Optimization (DPO)
💻 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:

  • ✔ LLM Inference Optimization: PagedAttention, KV-Cache & Continuous Batching
  • ✔ High-Throughput Serving with vLLM & TensorRT-LLM
  • ✔ Packaging Models into REST APIs using FastAPI & Docker Containers
  • ✔ Experiment Tracking and Model Registry with MLflow
  • ✔ Model Monitoring for Data Drift & Performance Degradation
💻 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+
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₹12.5 LPA
Highest Package
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Average Career Growth
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Interview Guarantee
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Find out the average salary for different IT roles based on experience, location and skills.
Estimated Salary Range Based on Market Data
₹ 14.3 LPA – 26.4 LPA
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Salary varies based on skills, company, and industry demand. Talk to our experts to know how you can reach the higher range.
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Frequently Asked Questions

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

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