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Kgisl Skill Development

  Learn Artificial Intelligence  

Online Courses & Training in Dubai

Professional Certification in Artificial Intelligence

Expert-led AI training

Industry-recognized certification

Hands-on projects and real-world case studies.

Flexible online learning

Internship

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Upcoming Class Schedule

Feb 15th 2025

6PM - 9PM

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Graduates & Early to Mid-career Professionals

Duration

120-Hours

Mode

Online

Format

Hands-On Live Training

Suited For

Graduates & Early to Mid-career Professionals

Each weeks

4 weeks

Each Session

6 Session

Instructor

Sagar Maria

Sagar Maria

Ex- Data Analytics & Machine Learning Expert

Know More
The Importance of Real-World Projects in AI Certification Programs
 Why is Artificial Intelligence in demand in Dubai?
AI Certification: Shaping UAE's Workforce in 2025
 Top 5 AI Skills For 2025

Why Enroll In an Artificial Intelligence Course in Dubai?

The demand for professionals skilled in Artificial Intelligence (AI) and Machine Learning (ML) is soaring across finance, healthcare, and IT sectors. The UAE market is leading the AI transformation, offering lucrative job opportunities for certified AI professionals.KGiSL Skill Development Institute’s Artificial Intelligence course provides comprehensive online training, starting with: AI and ML are transforming sectors like finance, healthcare, and IT, creating high demand. The UAE offers exciting opportunities. KGiSL Skill Development Institute’s AI course starts with practical online training.

Python programming

Python programming

Learn data structures, data cleaning, EDA (Exploratory Data Analysis), visualization and more.

Deep Learning

Deep Learning

Master the fundamentals of Deep learning to build neural networks and launch or excel in the AI career.

Generative AI

Generative AI

Explore advanced technologies like transformers, attention mechanisms, and Large Language Models (LLM) to develop AI-powered solutions.

Why Enroll In an Artificial Intelligence Course in Dubai?

200+ Hours of Live Sessions

180+ Hours of Project

180+ Hours of Self-Practice

25+ Aptitude Tests

EPIC Learning

Adaptive LMS

Soft Skills Training

Ongoing Career Support

Flexible EMI Plans

Artificial Intelligence Certification Course Overview

Designed for beginners and early to mid-level professionals, this online AI training program covers Python programming, Deep Learning, and Generative AI. Learn to build AI solutions using neural networks, transformers, and Large Language Models (LLMs). Our AI coding course teaches EDA, advanced machine learning, and practical skills for careers in finance, healthcare, and tech.

Artificial Intelligence Certification Course Curriculum

Learn AI with a syllabus designed by ex-data scientists and machine learning experts with 25+ years of combined industry experience.

Module 1

Introduction to Python

AI Python for Beginners is a great way to start learning Python, a versatile and beginner-friendly programming language. Python is widely used in web development, data analysis, artificial intelligence, and machine learning. This module will introduce you to the fundamental concepts of Python programming.

By the end of this module, you’ll have a solid foundation in Python, empowering you to write efficient code, analyze data, and create meaningful visualizations. This will lay the foundation for exploring more advanced topics in AI programming with Python and applying them to real-world scenarios.

Python Basics

Understand syntax, variables, and control structures.

Control Statements

Understand conditional and looping structures for decision-making and iteration.

Data Structures

Explore lists, tuples, dictionaries, sets, and other core data structures.

Functions

Dive into function creation, parameters, and return values for organizing your code.

Modules and Packages

Discover how to organize your code into reusable modules and packages.

File Handling

Learn to work with files to store and retrieve data in Python.

Error Handling

Master handling exceptions to make your programs more robust.

Advanced Concepts in Python

As this artificial intelligence course progresses, more advanced concepts will be introduced to enhance your Python skills.

Python Libraries

Explore powerful libraries like NumPy for numerical computations and pandas for data manipulation.

Data Cleaning

Learn techniques for handling missing data, outliers, and transforming raw datasets into usable formats.

Exploratory Data Analysis (EDA)

Understand how to summarize and visualize data to uncover patterns and insights.

Data Visualization

Use tools like Matplotlib and Seaborn to create insightful visualizations of your data.

As this artificial intelligence course progresses, more advanced concepts will be introduced to enhance your Python skills.

Module 2

Deep Learning

Deep Learning, a key part of Artificial Intelligence (AI), focuses on creating and training Neural Networks to solve complex problems. It's behind innovations like image classification, natural language processing (NLP), and predictive analytics.

This best artificial intelligence course will teach you how to use Deep Learning to build AI solutions for real-world challenges.

This module emphasizes practical applications, combining hands-on experience with theoretical insights to equip you with advanced AI capabilities.

By the end of this module, you'll understand foundational architectures like ANN, CNN, and RNN, along with advanced methods for optimizing and deploying Deep Learning models to implement advanced AI solutions

Core Concepts in Deep Learning

Neural Networks (NN)

Grasp the fundamentals of neural networks, including layers, weights, biases, and activation functions.

Artificial Neural Networks (ANN)

Build fully connected networks to handle tasks like regression and classification.

Convolutional Neural Networks (CNN)

Explore how CNNs are designed for feature extraction in image processing.

Recurrent Neural Networks (RNN)

Delve into sequence modeling for applications such as language translation and time-series forecasting.

Advanced Techniques in Deep Learning

LSTM (Long Short-Term Memory)

Manage long-range dependencies in sequential data with LSTM networks.

GRU (Gated Recurrent Unit)

Use GRUs for efficient sequence modeling and reduced computational overhead.

Other RNN Variants

Investigate alternative RNN architectures for specialized tasks.

GAN (Generative Adversarial Networks)

Master the creation of synthetic data using GANs for applications in data augmentation and generative tasks.

Module 3

Generative AI

Generative AI Certification Courses enable machines to create new content using technologies like Transformers, Attention Mechanisms, and VAEs. This module covers core concepts and tools like Vector DB and LangChain for building dynamic generative AI applications.

By the end of this module, you'll master Generative AI technologies, including transformers, fine-tuning large models, and creating AI solutions for content generation across various domains.

Core Concepts in Generative AI Certification Courses

Transformers

Key to modern NLP and generative models.

Attention Mechanism

Improves language generation and image synthesis.

VAEs:

Generates new data points for unsupervised learning.

Vector DB

Optimizes data retrieval for generative models.

LangChain

Integrates dynamic workflows for content generation.

Generative AI Mastery

Advanced Transformer Architectures

Work with models like GPT-4 and BERT.

LLMs and Fine-Tuning

Tailor large language models for tasks like summarization and chatbots.

RAG Models

Combine retrieval and generation for better accuracy.

Flask API

Deploy generative models using Flask for web applications.

Skills You’ll Gain

Hub IT allows your business and technology computers to store, transmit, analyze, and manipulate big data.

AI Fundamentals

Generative AI

Python Programming

Data Visualization

Data Training

Machine Learning

Deep Learning

NLP

Industry Tools

allows your business and technology computers to store, transmit, analyze, and manipulate big data.

Pandas

NumPy

matplotlib

seaborn

plotly

scikit-learn

Jupyter Notebooks

TensorFlow

Flask

Keras

OpenCV

HuggingFace

Langchain

FastAPI

OpenAIGPT-3/4

Master AI & MLwithReal-World Projects

From analyzing real data sets to developing AI models, solidify your understanding and master practical skills to ace AI job interviews.

Time Series Prediction with LSTM

Time Series Prediction with LSTM

Create an LSTM-based model to predict time series data, such as stock prices or weather patterns, and evaluate its performance.

Text Generation with Transformers

Text Generation with Transformers

Develop a text generation model using transformers and fine-tune it for specific tasks like chatbot development or content creation

Image Classification with CNNs

Image Classification with CNNs

Implement a convolutional neural network (CNN) to classify images and optimize the model using advanced techniques like data augmentation.

Generative AI API with Flask

Generative AI API with Flask

Build a Flask API for a generative AI application that utilizes large language models (LLMs) and integrates retrieval-augmented generation (RAG) techniques.

Python-based Data Cleaning & Visualization Tool

Python-based Data Cleaning & Visualization Tool

Build a data cleaning and analysis tool that processes raw data, applies EDA techniques, and visualizes insights using Python libraries.

Artificial Intelligence Certification Course Curriculum

Learn AI with a syllabus designed by ex-data scientists and machine learning experts with 25+ years of combined industry experience.

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AI Machine Learning Course- Outcomes

Stay Industry-relevant

Stay Industry-relevant

Artificial intelligence is constantly evolving, so having AI skills can help you stay industry-relevant and stand out in the job market.

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Build a Strong AI Portfolio

Build a Strong AI Portfolio

Work onpractical projects that showcase your ability to develop, deploy, and optimize AI models, building a portfolio that demonstrates your skills.

Intern with the Industry

Intern with the Industry

Eligible learners get a 1-month internship with companies after course completion and work on exciting AI projects

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Career advancement

Career advancement

Open doors to top AI careers like Data Scientist, AI Engineer, Machine Learning Engineer, and Natural Language Processing (NLP) Engineer

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Earn Your Artificial Intelligence Certification

Showcase your knowledge with an industry-recognized Professional Certification in Artificial Intelligence. Share it on LinkedIn or with your network to unlock new career opportunities in GenAI, Machine Learning or Big Data.

Ready to Get Started?

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Frequently Asked Questions?

KGiSL Skill Development Training Institute's Artificial intelligence course is for graduates and
professionals looking to upskill themselves and start their career in AI or ML. A solid foundation in
computer science, mathematics, statistics or basic Python skills is a plus

Our Generative AI Certification Courses offer flexible schedules, including evening weekend classes for
both beginners working professionals.

Python is a fundamental skill for data scientists and AI professionals. Learning python opens up job
opportunities ranging from entry-level AI jobs to advanced big data roles.

KGiSL Skill Development Training Institute offers one of the best artificial intelligence courses in Dubai
with career support and internship opportunities.

Research analyst, junior data analyst, associate data scientist, AI/ML Engineer, and AI research assistant
are some of the commonentry level AI jobs

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GAYTHIRI-T
Subject Matter Expert - Data Analytics and Data Science
Educational Qualification:

Master of Science in Machine Learning & Artificial Intelligence | Liverpool John Moores University, UK

Executive PG Diploma: ML & AI | International Institute of Information Technology, Bangalore
Bachelor of Engineering in Computer Science | Thiagarajar College of Engineering, Madurai

Professional Summary

  • Over 5+ years of industrial and training experience including TCS
  • Implemented an automated customer complaint classification system, utilizing NMF for topic modelling on clients' complaints dataset and designing a supervised model to predict relevant topics for incoming complaints.
  • Implemented a Random Forest predictive maintenance model, chosen for robust performance and interpretability, in the manufacturing equipment's control system, utilizing sensor data to proactively identify issues and achieve a 28.7% reduction in equipment downtime.
  • Established an end-to-end Machine Learning Operations (MLOps) solution using Jarvislabs to lower the Customer Acquisition Cost (CAC) for EdTech firm 'CodePro' by computing lead scores to forecast course enrolment likelihood.
  • Developed a capstone project focusing on credit card fraud detection.
  • Assessed the loan dataset of a lending finance firm to determine the likelihood of a client defaulting or repaying a loan based on foundational client information.
  • Developed and led the implementation of a time series forecasting model, incorporating diverse models, including XGBoost and LSTM, resulting in a 16.3% reduction in energy load prediction errors.
  • Implemented a real-time fraud detection system in a cosmetic E-commerce platform using XGBoost, reducing fraudulent transactions by 18.8%, enhancing security and customer trust.
  • Innovated a gesture recognition feature for smart TVs, implementing a system adept at accurately identifying five user gestures from input videos using three architectures: 3D Convolutional network, CNN + RNN, and Transfer learning with GRU (Gated Recurrent Units), enabling seamless TV operation without a remote.
  • Implemented a robust quality control system utilizing sensor details and product images, integrating a finely tuned CNN model, resulting in a significant 21.4% reduction in product defects.
  • Established customer segmentation through K-Means clustering, crafting personalized marketing strategies, product recommendations, and tailored communication channels for each distinct customer group based on in-depth analysis of common characteristics, behaviors, and preferences.
  • Statistics essentials, Inferential statistics, Hypothesis testing
  • Exploratory Data Analysis, Predictive Analysis, Regression & Classification
  • Linear and Logistic Regression, Naive Bayes Classifier, Decision Trees, Random Forests, Bagging and Boosting Techniques, XGBoost, Clustering – K Means and Hierarchical clustering, Principal Component Analysis (PCA)
  • Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Transfer Learning, LSTM (Long Short-Term Memory networks), GRU (Gated recurrent units)
  • NLP (Natural Language Processing), MLOPs
  • Tools Excel, VSCode, Jupyter, Jarvislabs, GIT
  • Packages Python, Pandas, Numpy, Scikit Learn, Matplotlib, Seaborn
  • Data Exploration & Analysis, Model Building & Evaluation
  • Feature Engineering, Data Storytelling & Visualization
  • Machine Learning Model Selection, Predictive Modeling
  • Computer vision, Metaheuristic Optimization algorithms
  • Deep Learning
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