Professional Certification Path

AI + ML Masters Program
Mastery Program.

A rigorous, 16-week masters-level engineering program designed for data scientists, ML engineers, and AI architects. Deeply covers mathematical foundations, classical machine learning, PyTorch deep learning, Transformer architectures, Parameter-Efficient Fine-Tuning (LoRA/QLoRA), Enterprise RAG, Autonomous Agents, and production MLOps deployment.

Level: Masters / Advanced Professional Track
Job Ready
Self-Paced
AI + ML Masters Program
Interactive Audio-Visual Masterclass & Sandbox

AI & Deep Learning Architecture Sandbox

Simulate backprop loss curves (MLP, ResNet, CNN, Transformer), compute cosine similarity matrices, calculate exact LoRA/QLoRA VRAM footprints, and benchmark MLOps throughput. Includes AI Neural voiceovers for each lesson so you can master concepts visually and audibly without boring video delays.

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AI & Deep Learning Architecture Sandbox

Course Curriculum & Hands-on Labs

16 Modules • 32 In-Depth Lessons • 16 Practical Lab Assignments • 32 Quizzes

Phase 1: Deep Learning Foundations
Weekly Engineering Competency Outcome: Master matrix decomposition (SVD, Eigenvalues), high-dimensional geometry, tensor rank approximations, and GPU memory layouts.
Structured Lessons & Topics
Matrix Decompositions: SVD, Eigenvalues & Tensor Geometries on GPU
Duration: 50 mins Quiz Evaluation Included
Text
High-Dimensional Semantic Geometry, Dot Products & Cosine Metric Spaces
Duration: 45 mins Quiz Evaluation Included
Lab
Weekly Hands-On Project Assignment
Graded Lab

Lab Assignment 1: SVD Weight Compression Engine for Linear Layers

Objective: Build a PyTorch module that factorizes a large linear layer (W: 4096 x 4096) into low-rank matrices A and B using truncated SVD, and benchmark accuracy vs parameter savings.

Task Instructions: 1. Initialize a linear layer with random Gaussian weights representing a pre-trained layer. 2. Compute full SVD. 3. Truncate to rank r = [16, 32, 64, 128]. 4. Measure parameter reduction ratio, reconstruction error, and FLOP savings per forward pass.
Deliverables: A single Python script `svd_compression_lab.py` containing the `LowRankLinear` module and benchmark graphs.
Grading Rubric: 40% functional correctness, 30% numerical accuracy against baseline SVD, 30% benchmarks.
Weekly Engineering Competency Outcome: Eliminate Python interpreter bottlenecks through SIMD vectorization, memory-aligned strides, contiguous buffers, and high-throughput batching.
Structured Lessons & Topics
Vectorized SIMD Broadcasting, Memory Strides & Cache Alignment
Duration: 45 mins Quiz Evaluation Included
Text
Memory-Mapped Arrays (mmap) & Zero-Copy Batch Data Ingestion
Duration: 50 mins Quiz Evaluation Included
Lab
Weekly Hands-On Project Assignment
Graded Lab

Lab Assignment 2: Zero-Copy Vectorized Batch Ingestion Engine

Objective: Build a high-performance Python data loader using `np.memmap` and vectorized NumPy broadcasting that yields batches of 1024 token sequences with under 2 milliseconds latency per batch.

Task Instructions: 1. Generate a binary token file of 500,000 sequences (sequence length 128). 2. Implement an asynchronous memory-mapped batch generator. 3. Apply dynamic sequence padding and attention mask generation purely through vectorized broadcasting without loops.
Deliverables: `data_ingest_engine.py` with benchmark script verifying batch latency under 2ms.
Grading Rubric: 50% throughput/latency, 30% zero-copy compliance, 20% documentation.
Weekly Engineering Competency Outcome: Derive backpropagation via the multivariable chain rule, implement reverse topological sorting, and build an autograd engine from scratch.
Structured Lessons & Topics
Multivariable Chain Rule, Vector-Jacobian Products & Computational Graphs
Duration: 55 mins Quiz Evaluation Included
Text
Building MicroGrad from Scratch: Value Nodes, Adjoints & Topological Sort
Duration: 60 mins Quiz Evaluation Included
Lab
Weekly Hands-On Project Assignment
Graded Lab

Lab Assignment 3: Extend Scalar Autograd Engine to 2-Layer Neural Net

Objective: Extend the `Value` class with `relu()`, `tanh()`, and `pow()` methods, build a Multi-Layer Perceptron (MLP) consisting of `Neuron` and `Layer` classes, and train it to fit a non-linear XOR boundary.

Task Instructions: 1. Implement `relu()` with backward derivative $(data > 0)$. 2. Build `MLP(nin=2, nouts=[16, 16, 1])`. 3. Run 100 epochs of gradient descent with learning rate 0.05 on XOR dataset. 4. Verify training loss drops below 0.01.
Deliverables: Self-contained Python script `custom_autograd_xor.py` demonstrating convergence.
Grading Rubric: 40% mathematical correctness of derivatives, 40% XOR convergence, 20% clean OOP structure.
Weekly Engineering Competency Outcome: Derive Cross-Entropy from Maximum Likelihood Estimation, implement custom loss functions, and master L1/L2 weight decay and label smoothing.
Structured Lessons & Topics
Maximum Likelihood Estimation (MLE), NLL & Cross-Entropy Derivation
Duration: 45 mins Quiz Evaluation Included
Text
L1/L2 Weight Decay, Dropout Mechanics & Label Smoothing
Duration: 50 mins Quiz Evaluation Included
Lab
Weekly Hands-On Project Assignment
Graded Lab

Lab Assignment 4: Custom Focal Loss & Label Smoothing Implementation

Objective: Implement Lin et al.'s Focal Loss $(\text{FL}(p_t) = -\alpha_t (1 - p_t)^\gamma \log(p_t))$ in PyTorch with LogSumExp numerical stability, and train a classifier on an imbalanced dataset (95% class 0, 5% class 1).

Task Instructions: 1. Write `FocalLoss(nn.Module)` with parameters $\gamma=2.0$ and $\alpha=0.25$. 2. Compare against standard CrossEntropyLoss on the imbalanced dataset. 3. Graph precision-recall curves showing minority class recovery.
Deliverables: Python file `focal_loss_benchmark.py` and saved PR-curve plot.
Grading Rubric: 40% numerical stability, 30% minority class PR-AUC improvement, 30% code elegance.
Phase 2: Sequence & Attention Mechanics
Weekly Engineering Competency Outcome: Understand recurrent hidden state propagation, vanishing/exploding gradients, gating mechanisms (LSTM & GRU), and bidirectional representations.
Structured Lessons & Topics
Vanishing & Exploding Gradients in Recurrent State Transitions
Duration: 50 mins Quiz Evaluation Included
Text
LSTM & GRU Gating Mechanics: Cell State, Forget, Input & Output Gates
Duration: 55 mins Quiz Evaluation Included
Lab
Weekly Hands-On Project Assignment
Graded Lab

Lab Assignment 5: Character-Level Language Model with Custom LSTM & Gradient Clipping

Objective: Build a character-level sequence predictor from scratch using your `CustomLSTMCell`, implement global norm gradient clipping (`torch.nn.utils.clip_grad_norm_`), and generate Shakespearean text snippets.

Task Instructions: 1. Tokenize a text file into character vocab. 2. Implement truncated BPTT with sequence length 64. 3. Train for 20 epochs with Adam. 4. Implement temperature-scaled sampling for inference.
Deliverables: Python file `char_lstm_model.py` and sample text generation outputs at temperatures T=[0.2, 0.7, 1.2].
Grading Rubric: 40% loss convergence, 30% temperature sampling correctness, 30% clean fused gate implementation.
Weekly Engineering Competency Outcome: Derive Scaled Dot-Product Attention, prove the variance scaling factor $\sqrt{d_k}$, and implement Multi-Head Attention from scratch.
Structured Lessons & Topics
Scaled Dot-Product Attention: Mathematical Derivation & The Sqrt(d_k) Factor
Duration: 55 mins Quiz Evaluation Included
Text
Multi-Head Attention (MHA) Implementation & Quadratic Complexity Analysis
Duration: 60 mins Quiz Evaluation Included
Lab
Weekly Hands-On Project Assignment
Graded Lab

Lab Assignment 6: Multi-Head Causal Self-Attention with KV-Caching

Objective: Build a modular `CausalSelfAttention` class supporting both full training parallel forward pass and single-token auto-regressive generation with an incremental Key-Value (KV) cache.

Task Instructions: 1. Implement triangular causal lower mask. 2. Implement incremental KV-cache in PyTorch that stores past $K$ and $V$ tensors and concatenates the new token vector. 3. Benchmark generation speed: with KV-cache vs without KV-cache for 128 generated tokens.
Deliverables: Python script `causal_attention_kv_cache.py` with benchmark graphs.
Grading Rubric: 40% KV-cache speedup verification, 40% causal masking correctness, 20% clean modular structure.
Weekly Engineering Competency Outcome: Master state-of-the-art open-source LLM architectures: RMSNorm, SwiGLU activations, Rotary Position Embeddings (RoPE), and Grouped-Query Attention (GQA).
Structured Lessons & Topics
Rotary Position Embedding (RoPE) Mathematical Derivation
Duration: 55 mins Quiz Evaluation Included
Text
RMSNorm, SwiGLU MLP & Grouped-Query Attention (GQA)
Duration: 55 mins Quiz Evaluation Included
Lab
Weekly Hands-On Project Assignment
Graded Lab

Lab Assignment 7: Assemble a 125M LLaMA-3 Style Transformer Decoder

Objective: Build and initialize a complete modern decoder architecture integrating RMSNorm, RoPE, SwiGLU, and Grouped-Query Attention (GQA) in PyTorch.

Task Instructions: 1. Implement `RMSNorm` with learnable scale parameter $g$. 2. Implement `RotaryEmbedding`. 3. Implement `SwiGLU` MLP. 4. Connect 6 transformer blocks with residual connections and verify parameter count.
Deliverables: Python file `llama3_decoder_from_scratch.py` with forward pass unit test.
Grading Rubric: 40% architectural accuracy, 30% RoPE relative invariance test, 30% clean code.
Weekly Engineering Competency Outcome: Master contrastive learning (InfoNCE loss), Sentence Transformers, Approximate Nearest Neighbors (HNSW, IVF-PQ), and Hybrid BM25 search.
Structured Lessons & Topics
Contrastive Learning, InfoNCE Loss & Bi-Encoder Fine-Tuning
Duration: 50 mins Quiz Evaluation Included
Text
Approximate Nearest Neighbors (HNSW Graphs, IVF-PQ) & Hybrid Search
Duration: 55 mins Quiz Evaluation Included
Lab
Weekly Hands-On Project Assignment
Graded Lab

Lab Assignment 8: Enterprise Hybrid RAG Retriever with HNSW & BM25

Objective: Build a hybrid retrieval engine in Python combining BM25 keyword matching and FAISS/Chroma HNSW vector search, fused with Reciprocal Rank Fusion (RRF).

Task Instructions: 1. Index 5,000 technical documentation chunks. 2. Implement BM25 with `rank_bm25`. 3. Index embeddings with FAISS IndexHNSWFlat. 4. Implement `reciprocal_rank_fusion(dense_ranks, bm25_ranks, k=60)`. 5. Evaluate Recall@5 on queries containing exact technical acronyms.
Deliverables: Python file `hybrid_rrf_retriever.py` with evaluation report.
Grading Rubric: 40% RRF implementation correctness, 30% hybrid recall gains over pure vector search, 30% query latency.
Phase 3: LLM Fine-Tuning & Multi-Modal
Weekly Engineering Competency Outcome: Fine-tune 70B parameter LLMs on a single GPU using Low-Rank Adaptation (LoRA), QLoRA 4-bit NormalFloat (NF4), and Hugging Face PEFT.
Structured Lessons & Topics
LoRA Theory, Rank Selection (r) & Scaling Alpha Factor (alpha/r)
Duration: 55 mins Quiz Evaluation Included
Text
QLoRA: NormalFloat4 (NF4), Double Quantization & Paged Optimizers
Duration: 60 mins Quiz Evaluation Included
Lab
Weekly Hands-On Project Assignment
Graded Lab

Lab Assignment 9: Fine-Tune LLaMA-3 8B with QLoRA NF4 on Domain Instructions

Objective: Configure and execute a QLoRA fine-tuning run on `meta-llama/Meta-Llama-3-8B` using Hugging Face `peft`, `bitsandbytes`, and `trl` SFTTrainer, targeting all linear projections (`q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`).

Task Instructions: 1. Set 4-bit NF4 config with `BitsAndBytesConfig`. 2. Configure `LoraConfig(r=16, lora_alpha=32, lora_dropout=0.05)`. 3. Verify trainable parameter count is < 0.2% of total model. 4. Save and merge LoRA adapter with base weights.
Deliverables: Python fine-tuning script `qlora_llama3_training.py` and evaluation inference script.
Grading Rubric: 40% training script execution without OOM, 30% parameter efficiency verification, 30% clean merge logic.
Weekly Engineering Competency Outcome: Understand human preference alignment, Reinforcement Learning from Human Feedback (PPO, KL-penalties), and Direct Preference Optimization (DPO).
Structured Lessons & Topics
RLHF Foundations: Reward Modeling, PPO & KL Divergence Penalties
Duration: 55 mins Quiz Evaluation Included
Text
Direct Preference Optimization (DPO): Closed-Form Loss & Training
Duration: 55 mins Quiz Evaluation Included
Lab
Weekly Hands-On Project Assignment
Graded Lab

Lab Assignment 10: Align an LLM with Direct Preference Optimization (DPO)

Objective: Fine-tune an SFT model using the DPO loss on a dataset of chosen vs rejected technical responses with Hugging Face TRL `DPOTrainer`.

Task Instructions: 1. Format dataset with `prompt`, `chosen`, and `rejected` keys. 2. Set DPO hyperparameter $\beta=0.1$. 3. Train for 3 epochs. 4. Track log-odds margin $(\log \pi(y_w) - \log \pi(y_l))$ across steps, verifying it increases over time.
Deliverables: Python script `dpo_alignment_trainer.py` and evaluation comparison table.
Grading Rubric: 40% loss convergence, 30% margin growth verification, 30% clean data formatting.
Weekly Engineering Competency Outcome: Master multimodal representation learning: CLIP contrastive pretraining, Vision Transformers (ViT), and projection adapters (LLaVA architecture).
Structured Lessons & Topics
Contrastive Language-Image Pretraining (CLIP) & Dual-Encoder Embeddings
Duration: 50 mins Quiz Evaluation Included
Text
Vision-Language Architecture: Vision Encoder, Projector & LLM Decoder (LLaVA)
Duration: 55 mins Quiz Evaluation Included
Lab
Weekly Hands-On Project Assignment
Graded Lab

Lab Assignment 11: Zero-Shot Multimodal Document Classification & Visual Q&A

Objective: Build a zero-shot multimodal document classifier and visual Q&A engine using Hugging Face `transformers` and a lightweight VLM (e.g. `HuggingFaceM4/idefics2-8b` or `microsoft/git-base`).

Task Instructions: 1. Ingest image invoices/receipts. 2. Formulate zero-shot prompt templates for key information extraction (Vendor, Total, Date). 3. Compare latency and structured JSON output accuracy.
Deliverables: Python script `vlm_doc_extractor.py` processing 5 test document images.
Grading Rubric: 40% extraction accuracy, 30% structured JSON compliance, 30% latency profiling.
Weekly Engineering Competency Outcome: Master ReAct autonomous loops, strict schema tool use, dynamic plan correction, and enterprise Model Context Protocol (MCP) server development.
Structured Lessons & Topics
ReAct Loops: Reasoning + Acting, Tool Calling & Context Pruning
Duration: 55 mins Quiz Evaluation Included
Text
Enterprise Model Context Protocol (MCP): Architecture & Implementation
Duration: 60 mins Quiz Evaluation Included
Lab
Weekly Hands-On Project Assignment
Graded Lab

Lab Assignment 12: Build an MCP Server with SQLite Database Inspection & Safe Execution

Objective: Build a production MCP server in Python over stdio transport that exposes database querying, table introspection, and Python scratchpad sandboxing.

Task Instructions: 1. Implement JSON-RPC 2.0 protocol handler. 2. Implement `list_tools` and `call_tool`. 3. Add safety guards (block destructive `DROP/DELETE` statements). 4. Test with a client agent loop.
Deliverables: Python file `mcp_sqlite_server.py` with integration test script.
Grading Rubric: 40% JSON-RPC compliance, 30% SQL injection/mutation safety guards, 30% integration test.
Phase 4: Production MLOps & Capstone
Weekly Engineering Competency Outcome: Achieve 4x-10x higher serving throughput using PagedAttention (vLLM), continuous iteration-level batching, and TensorRT-LLM.
Structured Lessons & Topics
KV-Cache Memory Fragmentation & The PagedAttention Algorithm
Duration: 55 mins Quiz Evaluation Included
Text
Continuous Iteration-Level Batching vs. Static Batching
Duration: 55 mins Quiz Evaluation Included
Lab
Weekly Hands-On Project Assignment
Graded Lab

Lab Assignment 13: Deploy & Benchmark vLLM Server with Continuous Batching

Objective: Deploy a local vLLM serving container hosting an 8B model, configure PagedAttention block size, and execute a locust load benchmark testing P50, P95, and P99 latency under 50 concurrent virtual users.

Task Instructions: 1. Launch vLLM server: `python -m vllm.entrypoints.openai.api_server`. 2. Run concurrency benchmark script sending Poisson-distributed requests. 3. Graph throughput (tokens/sec) vs concurrency.
Deliverables: Configuration file `vllm_config.yaml` and benchmark report `vllm_benchmark_results.json`.
Grading Rubric: 40% throughput optimization, 30% latency benchmark graphs, 30% clean configuration.
Weekly Engineering Competency Outcome: Compress LLMs for production deployment using Activation-Aware Weight Quantization (AWQ), GPTQ, and compile for edge inference via GGUF and llama.cpp.
Structured Lessons & Topics
Post-Training Quantization: AWQ vs GPTQ Mathematical Comparison
Duration: 55 mins Quiz Evaluation Included
Text
GGUF Architecture & Edge Inference with llama.cpp (AVX-512 / ARM NEON)
Duration: 55 mins Quiz Evaluation Included
Lab
Weekly Hands-On Project Assignment
Graded Lab

Lab Assignment 14: Quantize an 8B Model with AutoAWQ & Benchmark PPL

Objective: Quantize a Hugging Face model using AutoAWQ to 4-bit precision, evaluate WikiText-2 perplexity degradation against FP16 baseline, and export to GGUF format for edge inference.

Task Instructions: 1. Run AutoAWQ quantization with calibration dataset. 2. Verify model size reduces from ~16GB to ~4.5GB. 3. Measure tokens/sec speedup on GPU.
Deliverables: Quantization script `run_awq_quant.py` and perplexity benchmark log.
Grading Rubric: 40% size reduction verification, 30% perplexity preservation, 30% clean export.
Weekly Engineering Competency Outcome: Secure production AI pipelines against prompt injection, jailbreaking, data poisoning, and deploy programmable semantic guardrails (Llama Guard, NeMo).
Structured Lessons & Topics
Adversarial Attack Vectors: Prompt Injections, Jailbreaking & Data Poisoning
Duration: 55 mins Quiz Evaluation Included
Text
Multi-Layered AI Defense: Semantic Guardrails, Output Filtering & Llama Guard
Duration: 55 mins Quiz Evaluation Included
Lab
Weekly Hands-On Project Assignment
Graded Lab

Lab Assignment 15: Implement a Multi-Layered AI Security & Guardrail Gateway

Objective: Build an asynchronous FastAPI security gateway that intercepts incoming prompts, evaluates them against safety categories, redacts PII with Presidio, and blocks indirect injections.

Task Instructions: 1. Implement regex and semantic injection filter. 2. Implement automated PII anonymization. 3. Integrate simulated Llama Guard classification. 4. Return standard HTTP 400 with structured violation metadata upon breach.
Deliverables: Python file `ai_guardrail_gateway.py` with 10 attack vector test cases.
Grading Rubric: 40% injection block rate, 30% zero false-positive on valid technical queries, 30% clean PII masking.
Weekly Engineering Competency Outcome: Architect and deploy an enterprise-grade autonomous AI microservice with vLLM serving, asynchronous FastAPI endpoints, Docker containerization, and Prometheus observability.
Structured Lessons & Topics
Enterprise AI Architecture: Microservices, Asynchronous Workers & Telemetry
Duration: 60 mins Quiz Evaluation Included
Text
Master Capstone Defense: End-to-End Pipeline Deployment & SLA Validation
Duration: 65 mins Quiz Evaluation Included
Lab
Weekly Hands-On Project Assignment
Graded Lab

Master Capstone Project: End-to-End Autonomous AI System with Docker & Prometheus

Objective: Build, containerize, and defend a complete enterprise AI application featuring a fine-tuned LLM, Hybrid RAG retriever, multi-step agentic tool loop, and Prometheus monitoring dashboard.

Task Instructions: 1. Write `Dockerfile` with multi-stage build. 2. Create `docker-compose.yml` orchestrating API, Redis, and Vector DB. 3. Implement `/health`, `/metrics`, and `/predict` endpoints. 4. Write automated integration test suite with >90% coverage.
Deliverables: Git repository link with `Dockerfile`, `docker-compose.yml`, source code, and benchmark report demonstrating <500ms TTFT under 20 concurrent users.
Grading Rubric: 40% end-to-end functionality, 30% Docker containerization & latency SLAs, 30% safety & telemetry.

Certification Readiness

This course is specifically aligned with the global certification requirements for AI + ML Masters Program. We provide regular exam simulations and high-fidelity mock labs to ensure you are 100% ready for the actual exam.

10+
Mock Exams
100%
Pass Rate Focus

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