Best AI/ML Engineering with GenAI in
Noida
Accelerate your engineering career with 1-on-1 Mock Interview SessionsDedicated Support, 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 Noida 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 Noida
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:
- ✔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
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:
- ✔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)
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:
- ✔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
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:
- ✔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
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:
- ✔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)
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:
- ✔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
Where Our Noida Students Get Hired
Over 500+ global enterprises and innovative unicorns hire directly from 4Achievers programs.
Frequently Asked Questions (Noida)
Everything you need to know about fees, batch timings, Career Guidance & Interview Prep, and prerequisites.
Why choose 4Achievers for AI/ML Engineering with GenAI in Noida?
4Achievers provides 1-on-1 Mock Interview SessionsDedicated Support, 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 1-on-1 Mock Interview SessionsDedicated Support provided in Noida?
Yes! Our dedicated Corporate Career Mentorship Desk coordinates guaranteed interview drives, resume redesign workshops, and mock technical grilling until you secure your desired job offer.