DevsGene - Learn, Explore, and Grow in Development

Overview:

This course introduces artificial intelligence concepts, basic machine learning ideas, automation workflows, and practical AI tools to help students understand how AI is used in real-world applications.

What you'll learn:

AI and machine learning fundamentals

Data workflows and automation

AI-powered tools and use cases

Ethical considerations in AI

Roadmap

01 AI Basics & Distinctions

Lectures

What is AI? History and evolution

AI vs Machine Learning vs Data Science

Types of AI (narrow, general, super)

Current AI applications

Labortary

Explore AI demos (chatbots, image recognition)

Classify AI examples

Discuss AI in daily life

Set up AI learning environment

02 Data Basics & ML Types

Lectures

Introduction to data in AI

Supervised, unsupervised, reinforcement learning

Data collection and preprocessing

Basic statistics for AI

Labortary

Handle sample datasets

Classify ML types in examples

Preprocess data using tools

Simple data visualization

03 Learning Models Overview

Lectures

Common ML algorithms (regression, classification)

Neural networks basics

Training and evaluation

Overfitting and underfitting

Labortary

Run simple ML models (e.g., via Google Colab)

Train a basic classifier

Evaluate model performance

Adjust for overfitting

04 Ethics & Limitations of AI

Lectures

Ethical issues in AI (bias, privacy)

AI limitations and risks

Regulations and governance

Responsible AI practices

Labortary

Analyze biased AI cases

Discuss ethical dilemmas

Review AI policies

Propose ethical guidelines

05 AI Tools & Automation

Lectures

Popular AI tools (ChatGPT, TensorFlow)

Automation with AI (RPA, workflows)

No-code AI platforms

Integrating AI in business

Labortary

Use AI tools for tasks

Build simple automations

Explore no-code platforms

Automate a workflow

06 Prompt Engineering & AI Interaction

Lectures

What is prompt engineering?

Best practices for AI prompts

Advanced prompting techniques

AI for content generation

Labortary

Craft effective prompts

Test prompt variations

Generate content with AI

Optimize prompts for results

07 AI Business Use Cases

Lectures

AI in industries (healthcare, finance, marketing)

Case studies of AI implementation

ROI of AI projects

Future of AI in business

Labortary

Analyze business case studies

Propose AI solutions

Calculate potential ROI

Discuss implementation challenges

08 AI Mini Project & Review

Lectures

Project planning with AI

Integrating concepts learned

Presentation skills

AI career paths

Labortary

Build an AI-powered mini project

Test and refine

Present project

Portfolio addition

Ready to get enroll?

One Time Payment: 25,000 (PKR)

Whats Included:

Comprehensive course materials and learning resources

Access to hands-on labs and practical exercises

Ongoing instructor guidance and support

Official certificate of completion upon successful completion

Get Enrolled

Introduction to AI

Duration: 2 Months

Cost: 25,000(PKR)

Classes per week: 03

Overview:

What you'll learn:

Roadmap

01

AI Basics & Distinctions

Lectures

What is AI? History and evolution

AI vs Machine Learning vs Data Science

Types of AI (narrow, general, super)

Current AI applications

Labortary

Explore AI demos (chatbots, image recognition)

Classify AI examples

Discuss AI in daily life

Set up AI learning environment

02

Data Basics & ML Types

Lectures

Introduction to data in AI

Supervised, unsupervised, reinforcement learning

Data collection and preprocessing

Basic statistics for AI

Labortary

Handle sample datasets

Classify ML types in examples

Preprocess data using tools

Simple data visualization

03

Learning Models Overview

Lectures

Common ML algorithms (regression, classification)

Neural networks basics

Training and evaluation

Overfitting and underfitting

Labortary

Run simple ML models (e.g., via Google Colab)

Train a basic classifier

Evaluate model performance

Adjust for overfitting

04

Ethics & Limitations of AI

Lectures

Ethical issues in AI (bias, privacy)

AI limitations and risks

Regulations and governance

Responsible AI practices

Labortary

Analyze biased AI cases

Discuss ethical dilemmas

Review AI policies

Propose ethical guidelines

05

AI Tools & Automation

Lectures

Popular AI tools (ChatGPT, TensorFlow)

Automation with AI (RPA, workflows)

No-code AI platforms

Integrating AI in business

Labortary

Use AI tools for tasks

Build simple automations

Explore no-code platforms

Automate a workflow

06

Prompt Engineering & AI Interaction

Lectures

What is prompt engineering?

Best practices for AI prompts

Advanced prompting techniques

AI for content generation

Labortary

Craft effective prompts

Test prompt variations

Generate content with AI

Optimize prompts for results

07