Artificial Intelligence and Applied Gen AI Certification in Oman

Python syntax, variables, data types

Conditional statements and loops

Functions and lambda functions

Lists, tuples, dictionaries, sets

Practice: Automate report filtering

Jupyter/Colab usage

NumPy arrays, indexing, reshaping

Vectorized operations

Use Case: Matrix operations for logistics

Load, clean, and merge data

Handling missing values

Filtering, slicing, groupby operations

Project: Merge Excel reports across departments

Mean, median, variance, std deviation

Skewness and kurtosis

Probability rules and distributions (normal, binomial)

Domain examples: Retail and HR

Central Limit Theorem

Confidence intervals

t-tests, chi-square, ANOVA

Correlation vs causation

Use Case: A/B testing for ecommerce

Histograms, barplots, boxplots

Heatmaps and correlation plots

Seaborn styling

Mini Project: Visualize retail sales

Feature engineering: scaling, encoding

Binning, outlier treatment

Practice: EDA on sales/finance dataset

Supervised vs unsupervised ML

ML workflow: split, train, evaluate

Metrics: accuracy, precision, recall, F1, ROC

Use Case: HR attrition model

Salary prediction using linear regression

Attrition classification with logistic regression

Confusion matrix, coefficients, ROC-AUC

Decision tree construction

Random forest ensemble

K-Nearest Neighbors (KNN)

GridSearchCV tuning

Use Case: Loan eligibility prediction

K-means clustering

Hierarchical clustering

PCA for feature reduction

Use Case: Customer segmentation

Connect CSV, Excel, and Python outputs

Power Query transformations

Visuals: cards, charts, slicers

Case: HR Dashboard (attrition analysis)

Data modeling

DAX calculated columns and measures

Multi-page dashboards

Case: Finance dashboard with forecasting

Export ML results to Power BI

Import predictions

Build ML-powered dashboards

Use Case: Telecom churn dashboard

Data ingestion and cleaning with Pandas

Statistical summary and EDA

Visual story using Matplotlib/Seaborn

ML models: Linear/Logistic/Tree/KNN

Dashboard in Power BI integrating Python model outputs

Sales performance predictor

HR attrition analyzer

Why deep learning?

ANN structure, activation functions

Feedforward and backpropagation

Build ANN using Keras

TensorFlow 2.x and Keras API

Loss functions and optimizers

Overfitting and underfitting

Train a churn model on tabular data

NLP use cases: chatbots, sentiment, classification

Preprocessing: tokenization, stop words, lemmatization

Vectorization: Bag of Words, TF-IDF

Hands-on: Sentiment analysis

Logistic regression and Naive Bayes

Word embeddings introduction

Build: spam or feedback classifier

CNN layers: filters, pooling, convolution

Applications: image classification, document scan

Hands-on: Use pre-built CNN (transfer learning)

Save/load models (Pickle, Joblib, TF)

Create Flask/FastAPI REST APIs

Deploy a model (e.g., churn or sentiment)

Agent, environment, state, action, reward

Exploration vs exploitation

Q-learning (tabular)

Hands-on: Grid-world agent simulation

Self-attention and multi-head attention

BERT vs GPT

Positional encoding

Hands-on: Text generation with transformer

Generator vs Discriminator

DCGAN, CycleGAN, StyleGAN

Hands-on: Synthetic image generation

Denoising Diffusion Probabilistic Models (DDPM)

Diffusion vs GANs

Tools: Stable Diffusion, DALL·E

Hands-on: Generate text-to-image outputs

GPT, Claude, Falcon, Mistral overview

Prompt engineering

Fine-tuning domain-specific LLMs

Hands-on: Legal/Medical chatbot

LangChain architecture

Vector databases (FAISS, Pinecone)

Build RAG pipeline

Hands-on: Doc-aware Q&A chatbot

Vision-Language Models: GPT-4V, CLIP, BLIP

Text-to-image/video/audio generation

Hands-on: Generate media from prompts

Content generation pipelines

Brand asset generation

Tools: Sora, Runway, DALL·E

Deep Q-Networks (DQN)

Policy Gradients

PPO (Proximal Policy Optimization)

Hands-on: Train an autonomous agent

Multi-agent collaboration

LangChain dynamic decision agents

Build a simple autonomous assistant

Task decomposition & planning

Reflex vs learning agents

CrewAI orchestration

Hands-on: Multi-agent research + writing task

LangChain tools: Google Search, Wolfram, Zapier

Agent memory: vector, long-term, summary

Context retention strategies

Hands-on: Web-aware document chatbot

OpenAI, Claude function calling

ReAct prompting strategy

Toolformer concept

Hands-on: Reasoning assistant with tools & APIs

Orchestrating prompt → tools → output

Guardrails, observability

Evaluation metrics: latency, task completion

Hands-on: AI assistant to summarize meetings, update CRM, draft reports

Ethics, safety, bias, hallucinations

Gradio, Streamlit, Hugging Face Spaces

Hands-on: Host your GenAI app publicly

Data preprocessing + supervised ML model

LLM-based Q&A or text generation module

Image/audio generation (optional enhancement)

LangChain-based agentic workflow

Deployment via Flask + Gradio/Streamlit

Evaluation report: accuracy, latency, ethical handling

AI Assistant for HR Analytics

Legal or Medical Document Analyzer

Brand Content Generator for Marketing

Autonomous CRM Update & Reporting Agent