CyberGuardX — AI & Data Science Diploma

What You Will Learn

Start with the big picture: what AI, ML and Data Science are, common use-cases, the AI lifecycle and how companies use models to drive decisions.

Key Topics:

AI vs ML vs DL vs Data Science
Mathematics essentials: Linear algebra, calculus, probability, statistics
Tools & environments: Python, Jupyter, Conda

Outcome: Clear roadmap and mental model to proceed into hands-on work.

Python fundamentals tailored for AI: data structures, functions, object oriented programming and writing maintainable code.

Key Topics:

NumPy, Pandas
Data ingestion & cleaning
API usage & data scraping

Outcome: Build reproducible data pipelines and scripts for analysis.

Learn exploratory data analysis, statistical inference and visual storytelling using Matplotlib, Seaborn, Plotly and BI tools.

Key Topics:

EDA workflows & hypothesis testing
Feature engineering
Dashboards with Power BI / Tableau

Outcome: Deliver insights and interactive dashboards for stakeholders.

Core ML algorithms, evaluation metrics and model selection techniques using scikit-learn and practical case studies.

Key Topics:

Linear/Logistic Regression, Decision Trees, Ensembles
Clustering, Dimensionality Reduction
Model validation & hyperparameter tuning

Outcome: Build reliable ML models and production-ready pipelines.

Hands-on with neural networks using TensorFlow/Keras and PyTorch. Cover CNNs, RNNs, Transformers and transfer learning.

Key Topics:

Model architecture, backpropagation, optimizers
Computer Vision & Sequence models
Transfer learning and practical tips for training

Outcome: Train, evaluate and tune deep learning models for real use-cases.

From classic NLP pipelines to transformer-based language models and practical fine-tuning of LLMs for applications like chatbots and summarization.

Key Topics:

Tokenization, embeddings, sequence models
Transformers, BERT, GPT-style models
Fine-tuning and evaluation for downstream tasks

Outcome: Build production-ready NLP solutions and LLM-based assistants.

Image processing, detection and segmentation workflows using OpenCV and state-of-the-art detection models (e.g. YOLO families).

Key Topics:

Image pre-processing & augmentation
Object detection, image segmentation
Deploying vision models

Outcome: Deliver CV applications like detection, OCR, and classification.

Practical MLOps covering containerization, model versioning, CI/CD for ML, serving with FastAPI/Streamlit and model monitoring.

Key Topics:

Docker, MLflow, model registries
API serving and web apps
Monitoring and drift detection

Outcome: Ship models safely and monitor them in production.

Apply ML and AI techniques to security problems: anomaly detection, log analysis, malware classification and threat intelligence automation.

Key Topics:

Time-series anomaly detection
ML-assisted threat hunting
Integrating models with SIEM/ELK pipelines

Outcome: Build AI tools that boost SOC capabilities.

Capstone projects covering real-world scenarios. Each learner builds a portfolio of deployed applications and research-style reports.

Deliverables:

Deployed API or web app
Notebook and reproducible pipeline
Project defense and feedback

Outcome: A job-ready portfolio to share with employers.

Aligned with Industry Standards

The diploma prepares learners for industry-recognized roles and certifications by teaching concepts and skills aligned with popular certification bodies and major cloud providers.

Data Science Fundamentals

Core concepts that map to data science certification objectives.

ML & Deep Learning Practices

Hands-on practices to prepare for ML & deep learning assessments.

Cloud AI & MLOps

Deployment and cloud fundamentals aligned with major cloud AI services.

Please note

This program provides training aligned to industry standards. Official certification exams (from cloud providers or certification bodies) are administered by those providers and may require separate registration and fees.

Projects Roadmap

Data Cleaning & EDA

Real-world dataset cleaning, feature engineering and exploratory analysis.

Project 1

Predictive Modeling

Regression & classification use-cases with end-to-end pipelines.

Project 2

Deep Learning Application

Image or sequence model trained and evaluated with best practices.

Project 3

NLP Assistant

Build a summarizer or question-answering chatbot using transformer models.

Project 4

MLOps Pipeline

Model packaging, versioning and deployment to a cloud endpoint.

Project 5

AI for Cybersecurity

Anomaly detection or malware classification integrated with logs.

Project 6

Each project is reviewed by mentors and results in a portfolio-ready deliverable.

Labs & Assessment Roadmap

Labs

Kaggle Practice Notebooks
Hands-on GPU Training Sessions
Cloud Labs (AWS / Azure)
MLOps CI/CD Workshops

Practical labs that mirror production environments and common hiring tests.

Quizzes & Assessments

Regular checks to validate learning progress and readiness for projects.

Quiz 1: Python & Data Fundamentals

Core programming, data manipulation and EDA basics.

Quiz 2: Machine Learning

Algorithms, evaluation metrics and model selection.

Quiz 3: Deep Learning & NLP

Neural networks, transformers and sequence models.

Final Assessment: Project Defense

Capstone defense with mentor feedback and industry panel review.

Personalized feedback is provided after each assessment to help learners improve.

AI & Data Science Diploma

What You Will Learn