AI and Data Science

AI and Data Science

Course Overview

This comprehensive AI and Data Science course covers the foundations and advanced topics of AI and machine learning. You’ll learn data preprocessing, model development, deep learning, and AI-powered applications. The course includes hands-on experience with Python, TensorFlow, and Scikit-Learn. By the end, you’ll have the skills to analyze data, create intelligent models, and deploy AI solutions in real-world projects.

Learning Outcomes

  • Understand the core concepts of AI and Data Science.
  • Learn data preprocessing techniques for machine learning models.
  • Master supervised and unsupervised learning algorithms.
  • Gain hands-on experience with Python, TensorFlow, and Scikit-Learn.
  • Develop deep learning models using neural networks.
  • Work with real-world datasets to build predictive models.
  • Understand Natural Language Processing (NLP) and its applications.
  • Implement AI models for image and text recognition.
  • Explore reinforcement learning and AI decision-making systems.
  • Learn how to evaluate and optimize AI models.

Course Curriculum

1. Introduction to AI and Data Science

  • Overview of AI and Data Science
  • History and Evolution of AI
  • Key AI and Data Science Applications

2. Data Collection and Preprocessing

  • Understanding Different Data Types
  • Handling Missing and Outlier Data
  • Feature Engineering and Selection

3. Machine Learning Basics

  • Supervised vs. Unsupervised Learning
  • Key Machine Learning Algorithms
  • Evaluating Model Performance

4. Deep Learning Fundamentals

  • Introduction to Neural Networks
  • Activation Functions and Optimizers
  • Training Deep Learning Models

5. Natural Language Processing (NLP)

  • Text Preprocessing Techniques
  • Sentiment Analysis and Text Classification
  • Building AI-Powered Chatbots

6. Computer Vision and Image Processing

  • Understanding Convolutional Neural Networks (CNNs)
  • Image Classification and Object Detection
  • AI Applications in Medical Imaging

7. AI for Big Data and Cloud Computing

  • Working with Large Datasets
  • Big Data Processing with Spark and Hadoop
  • Deploying AI Models on Cloud Platforms

8. AI Ethics and Responsible AI

  • Bias and Fairness in AI Models
  • AI Regulations and Ethical Considerations
  • Future of AI and Social Impact

9. AI in Business and Industry Applications

  • AI in Healthcare and Finance
  • AI in Automation and Robotics
  • AI for Marketing and Customer Insights

10. Capstone Project: Building a Real-World AI Model

  • Defining the AI Project Scope
  • Building and Training the Model
  • Deploying and Presenting AI Solutions

Course Insights

Free

Duration
3 Months
Enrolled
420
Certificate
Included
Language
Urdu & English

What We Offer

  • Lifetime access to all course materials and future updates
  • Hands-on projects with real-world datasets
  • Exclusive access to an AI and Data Science community
  • Certificate of completion included at no extra cost
  • One-on-one mentorship for career guidance