Best AI/ML Engineering with GenAI in
Varanasi
Accelerate your engineering career with 100% placement assurance, high-tech offline laboratory training, 120+ Hours+ hours of intensive live projects, and 1-on-1 mentorship from top architects.
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4Achievers Varanasi Tech Campus
Experience focused, mentor-led offline learning equipped with state-of-the-art workstations, high-speed internet, dedicated faculty doubt counters, and air-conditioned classrooms.
AI/ML Engineering with GenAI Syllabus in Varanasi
Curriculum co-designed with engineering leads from tier-1 MNCs. Click each module below to view detailed topics, live coding projects, and industry tools.
01
Mathematical Foundations & High-Performance Python for ML
18 Hours
Mathematical Foundations & High-Performance Python for ML
Solidify linear algebra, calculus, probability, and high-performance vector computation with NumPy and Pandas.
Core Technical Topics & Competencies Covered:
- โVectorized Array Operations & Matrix Decompositions (SVD, PCA)
- โMultivariate Calculus: Gradients, Jacobians, Hessians & Chain Rule
- โProbability Distributions, Bayesian Inference & Maximum Likelihood Estimation
- โFeature Engineering: Outlier Treatment, Normalization & Target Encoding
- โHandling Imbalanced Datasets with SMOTE, Tomek Links & Class Weights
02
Supervised & Unsupervised Machine Learning Algorithms
22 Hours
Supervised & Unsupervised Machine Learning Algorithms
Master classical algorithms, ensemble methods, and hyperparameter tuning with Scikit-Learn and XGBoost.
Core Technical Topics & Competencies Covered:
- โLinear & Logistic Regression with L1/L2 Regularization (Lasso/Ridge/ElasticNet)
- โTree Algorithms: Decision Trees, Random Forests, Gradient Boosted Trees
- โState-of-the-Art Gradient Boosters: XGBoost, LightGBM, CatBoost
- โUnsupervised Learning: K-Means++, DBSCAN, Hierarchical Clustering
- โDimensionality Reduction: PCA, t-SNE & UMAP
- โCross-Validation Strategies & Rigorous Model Evaluation (ROC-AUC, PR-AUC, F1)
03
Deep Learning & Neural Networks with PyTorch
24 Hours
Deep Learning & Neural Networks with PyTorch
Build deep multi-layer perceptrons, convolutional networks, and recurrent sequence models using PyTorch.
Core Technical Topics & Competencies Covered:
- โPyTorch Tensors, Autograd & Building Custom nn.Module Architectures
- โLoss Functions, Optimizers (AdamW, SGD with Momentum) & Schedulers
- โPreventing Overfitting: Dropout, Weight Decay, LayerNorm & Batch Normalization
- โConvolutional Neural Networks (CNNs): ResNet, EfficientNet & Transfer Learning
- โSequence Modeling: RNNs, LSTMs & Introduction to Self-Attention
04
Transformers, Hugging Face & Natural Language Processing
20 Hours
Transformers, Hugging Face & Natural Language Processing
Master transformer architectures, tokenizers, BERT, GPT, and modern NLP pipelines.
Core Technical Topics & Competencies Covered:
- โAttention Is All You Need: Scaled Dot-Product Attention & Positional Encoding
- โHugging Face Transformers Ecosystem: Pipelines, Datasets, AutoModel & AutoTokenizer
- โEncoder Models for Text Classification & Named Entity Recognition (NER)
- โDecoder Models for Autoregressive Text Generation
- โEmbedding Generation & Semantic Similarity Search
05
LLM Fine-Tuning: PEFT, LoRA & QLoRA
20 Hours
LLM Fine-Tuning: PEFT, LoRA & QLoRA
Customize state-of-the-art open models (Llama, Mistral, Qwen) on proprietary enterprise datasets.
Core Technical Topics & Competencies Covered:
- โFull Fine-Tuning vs Parameter-Efficient Fine-Tuning (PEFT)
- โLow-Rank Adaptation (LoRA) & Quantized LoRA (QLoRA) Mathematics
- โData Preparation for Instruction Fine-Tuning (Alpaca & ShareGPT formats)
- โAccelerating Training with Unsloth, Axolotl and FlashAttention-2
- โSupervised Fine-Tuning (SFT) Trainer & Direct Preference Optimization (DPO)
06
High-Throughput Model Serving, MLOps & Production Inference
16 Hours
High-Throughput Model Serving, MLOps & Production Inference
Deploy low-latency AI models at enterprise scale with vLLM, Triton, and Docker microservices.
Core Technical Topics & Competencies Covered:
- โLLM Inference Optimization: PagedAttention, KV-Cache & Continuous Batching
- โHigh-Throughput Serving with vLLM & TensorRT-LLM
- โPackaging Models into REST APIs using FastAPI & Docker Containers
- โExperiment Tracking and Model Registry with MLflow
- โModel Monitoring for Data Drift & Performance Degradation
Where Our Varanasi Students Get Hired
Over 500+ global enterprises and innovative unicorns hire directly from 4Achievers programs.
Frequently Asked Questions (Varanasi)
Everything you need to know about fees, batch timings, placement support, and prerequisites.
Why choose 4Achievers for AI/ML Engineering with GenAI in Varanasi?
4Achievers provides 100% placement assurance, dedicated offline labs with high-speed development machines, real-world live projects, and personal 1-on-1 mentor guidance from working senior engineers.
What is the course duration and batch schedule?
The program spans 16 Weeks with both weekday regular batches (Mon-Fri) and weekend tracks (Sat-Sun) specifically designed for working professionals and university students.
Is there 100% placement support provided in Varanasi?
Yes! Our dedicated Corporate Placement Cell coordinates guaranteed interview drives, resume redesign workshops, and mock technical grilling until you secure your desired job offer.