Driving Perfomance

 IT Technology

Courses

1. ARTIFICIAL INTELLIGENCE (AI)

Course Overview

The Artificial Intelligence (AI) Course is designed to teach   how machines think, learn, and make decisions. This program covers the foundations of AI, Python programming, neural networks, deep learning, computer vision, and natural language processing.   work on real-time AI projects and learn how to deploy AI models used in industries like healthcare, finance, e-commerce, automation, and robotics.

Course Syllabus
  1. Introduction to AI
  2. Python for AI
  3. Neural Networks & Deep Learning
  4. AI Tools & Libraries
  5. Computer Vision Basics
  6. NLP Basics
  7. Real-Time AI Projects
  8. Deployment of AI Models
Outcomes

After completing this course,   will be able to:
✔ Build ML and AI models
✔ Work with Python, TensorFlow, Keras
✔ Build CV & NLP applications
✔ Deploy AI models online
✔ Create a portfolio for job readiness

2. MACHINE LEARNING (ML)

Course Overview

The Machine Learning (ML) Course prepares   to build intelligent systems that can learn from data and make predictions. This program covers Python foundations, data preprocessing, regression/classification models, clustering, model evaluation, predictive modelling, ML pipelines, and a complete end-to-end ML project.

Course Syllabus
  1. Python Refresher
  2. Data Preprocessing
  3. Supervised Learning
  4. Unsupervised Learning
  5. Model Evaluation & Tuning
  6. Predictive Modelling
  7. ML Pipelines
  8. End-to-End ML Project
Outcomes
  • Build machine learning models for prediction, classification, and clustering
  • Work confidently with Python, NumPy, Pandas, and Scikit-learn
  • Perform Exploratory Data Analysis (EDA) and extract insights
  • Evaluate models using accuracy, precision, recall, F1-score, etc.
  • Tune and optimize models using grid search and cross-validation
  • Develop end-to-end predictive modelling systems

3. GENERATIVE AI

Course Overview

The Generative AI Course trains    to work with modern AI tools that create text, images, videos, and automated workflows. This course covers the fundamentals of GenAI, prompt engineering, AI content creation, chatbot building, text-to-image models, automation tools, and enterprise-level AI use cases.

Course Syllabus
  1. Introduction to GenAI
  2. Prompt Engineering
  3. AI Content Generation
  4. Building Chatbots
  5. Text-to-Image Models
  6. AI Automation Tools
  7. Enterprise AI Use Cases
Outcomes
  • Understand the fundamentals of Generative AI and modern AI models
  • Write effective prompts using advanced Prompt Engineering techniques
  • Generate high-quality AI-based text, images, audio, and videos
  • Build AI-powered chatbots using no-code and code-based approaches
  • Work with text-to-image models such as Stable Diffusion and Midjourney
  • Automate tasks and workflows using AI automation tools
  • Apply Generative AI solutions in business, marketing, education, and enterprise environments

4. DATA SCIENCE

Course Overview

The Data Science Course is designed to help   master the complete data workflow—starting from Python programming, statistics, data cleaning, data visualization, exploratory data analysis (EDA), and progressing into basic machine learning and business intelligence dashboards.

Course Syllabus
  1. Python Programming
  2. Statistics & Probability
  3. Data Analysis with Pandas
  4. Data Visualization
  5. Machine Learning Basics
  6. Power BI / Tableau
  7. Real-Time Data Projects
Outcomes
  • Work confidently with Python programming, data structures, and libraries
  • Understand statistics & probability used for data analysis and ML
  • Clean, transform, and analyze datasets using Pandas
  • Create meaningful charts and dashboards using Matplotlib, Seaborn, Power BI, or Tableau
  • Perform Exploratory Data Analysis (EDA) to extract insights and patterns
  • Build simple machine learning models for prediction and classification
  • Use BI tools (Power BI/Tableau) to develop interactive, business-ready dashboards

5. CYBERSECURITY & ETHICAL HACKING

Course Overview

The Cybersecurity & Ethical Hacking Course equips   with the skills needed to protect computer systems, networks, and applications from cyberattacks. The course covers the foundations of networking, ethical hacking methodologies, vulnerability assessment, penetration testing, SOC (Security Operations Center) monitoring, and incident response practices used in real organizations.

Course Syllabus
  1. Networking Basics
  2. Ethical Hacking Fundamentals
  3. Vulnerability Assessment
  4. Penetration Testing
  5. Security Tools
  6. SOC Analysis
  7. Incident Response
Outcomes
  • Understand core networking concepts required for cybersecurity
  • Perform vulnerability assessments using industry tools
  • Conduct penetration testing on web applications, networks, and systems
  • Analyze threats, logs, and attacks through SOC (Security Operations Center) techniques
  • Identify, track, and respond to cyber incidents effectively
  • Understand malware attacks, phishing, brute force, SQL injection, and system exploitation
  • Implement defensive security measures to protect networks and applications

6. CLOUD COMPUTING

Course Overview

The Cloud Computing Course is designed to train   in deploying, managing, and securing applications on leading cloud platforms like AWS, Microsoft Azure, and Google Cloud (GCP). This course covers the fundamentals of cloud architecture, cloud storage, virtual machines, serverless services, IAM security, networking, and real-time cloud deployment.

Course Syllabus
  1. Cloud Fundamentals
  2. AWS/Azure/GCP Core Services
  3. Cloud Storage
  4. Virtual Machines
  5. Serverless Architecture
  6. Cloud Security
  7. Deployment & Monitoring
Outcomes
  • Create and manage cloud storage, buckets, and databases
  • Launch, configure, and manage virtual machines and cloud servers
  • Build applications using serverless services (AWS Lambda, Azure Functions, Cloud Run)
  • Implement cloud security principles including IAM, roles, policies & access control
  • Deploy real applications on cloud platforms using best practices