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AI ML Learning Path

3.5
(2)
3 Enrolled
40 hours

About Course

Complete Guide to Artificial Intelligence & Machine Learning with Real-World Projects

Dive into the world of Artificial Intelligence (AI) and Machine Learning (ML) with this comprehensive guide designed for beginners and professionals alike. Learn the core concepts of supervised, unsupervised, and reinforcement learning, along with essential tools like Python, TensorFlow, and Scikit-learn. Understand real-world applications in healthcare, finance, marketing, and automation. This guide also includes hands-on projects such as predictive analytics, chatbots, recommendation systems, and image classification to help you build practical skills. Whether you’re pursuing a career in data science or AI engineering, this guide offers the knowledge and experience you need to succeed.

What Will You Learn?

  • Supervised & unsupervised learning
  • Deep learning
  • Model deployment

Material Includes

  • Video Lectures covering all core concepts and practical demonstrations
  • Downloadable PDF Notes and Slides for each module
  • Hands-on Lab Exercises with step-by-step instructions
  • Virtual Lab Access or simulation tools for practicing real-world scenarios
  • Practice Quizzes and Assignments to reinforce learning
  • Final Capstone Project for practical skill application
  • Resource Library with links to tools, articles, and cybersecurity frameworks
  • Certificate of Completion upon successfully finishing the course

Requirements

  • Prerequisites:
  • Basic computer literacy (familiarity with using a PC or laptop)
  • Understanding of networking fundamentals (IP addresses, protocols, etc.) is helpful but not mandatory
  • No prior cybersecurity experience required – this course is suitable for beginners
  • Technical Requirements:
  • A computer with internet access (Windows, macOS, or Linux)
  • Ability to install free tools and software used in the course
  • Recommended: A web browser, virtual machine software (e.g., VirtualBox), and basic text editor
  • Instructions:
  • Complete lessons in sequence to build your knowledge progressively
  • Participate in labs and exercises for hands-on practice
  • Submit all assignments and the final project to receive certification
  • Reach out via the provided support channel if you encounter technical issues

Audience

  • This course is designed for:
  • Beginners and aspiring cybersecurity professionals who want to build a strong foundation in digital security.
  • IT professionals and system administrators looking to enhance their security knowledge and practices.
  • Students and recent graduates in computer science, IT, or related fields seeking to enter the cybersecurity industry.
  • Business owners and decision-makers who want to understand cyber risks and protect their organization’s digital assets.
  • Professionals preparing for entry-level certifications such as CompTIA Security+, CEH (Certified Ethical Hacker), or CISSP Associate.
  • Anyone interested in learning how to protect personal or organizational data from cyber threats and vulnerabilities.

Course Content

Python for ML

Regression

Classification

Clustering

Neural Networks

TensorFlow

NLP

Capstone Projects

Instructors

admin

admin

4.0
9 Students
10 Courses

Feedback

3.5
Total 2 Ratings
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Reviews (2)

  1. 2 months ago
    Good
  2. 2 months ago
    good

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