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Artificial Intelligence Basics

AI is changing everything. Time to change with it…our AI Basics Course makes sure you do.

(AI-BASIC.AU1) / ISBN : 978-1-64459-723-1
Lessons
Lab
AI Tutor (Add-on)

About This Course

Enroll in our AI Basics Course to demystify artificial intelligence and harness its power.

In this course, dive into machine learning, deep learning, NLP, and robotics through real-world case studies from companies like Uber and Facebook. Learn how to implement AI, avoid costly mistakes, and navigate ethical concerns while exploring AI’s impact on business and society.

From foundational concepts to hands-on labs, you’ll gain practical skills to evaluate AI solutions, deploy chatbots, and automate processes.

Skills You’ll Get

  • Foundational AI Concepts: Understand core principles of AI, machine learning, deep learning, and NLP.
  • AI Implementation Strategy: Learn best practices for deploying AI solutions using real-world case studies.
  • Robotic Process Automation (RPA): Automate workflows and improve efficiency with RPA tools.
  • Ethical AI & Risk Assessment: Identify ethical concerns, biases, and risks in AI systems.
  • Natural Language Processing (NLP) Application: Build chatbots and voice recognition systems using NLP techniques.
  • Future-Ready AI Forecasting: Analyze AI trends, societal impacts, and emerging technologies like autonomous systems.
Download Course Outline

1

Introduction

2

AI Foundations

  • Alan Turing and the Turing Test
  • Cybernetics
  • The Origin Story
  • Golden Age of AI
  • AI Winter
  • The Rise and Fall of Expert Systems
  • Neural Networks and Deep Learning
  • Technological Drivers of Modern AI
  • Structure of AI
  • Conclusion
  • Key Takeaways
3

Data

  • Data Basics
  • Types of Data
  • Big Data
  • Databases and Other Tools
  • Data Process
  • Ethics and Governance
  • More Data Terms and Concepts
  • Conclusion
  • Key Takeaways
4

Machine Learning

  • What Is Machine Learning?
  • Standard Deviation
  • The Normal Distribution
  • Bayes’ Theorem
  • Correlation
  • Feature Extraction
  • What Can You Do with Machine Learning?
  • The Machine Learning Process
  • Applying Algorithms
  • Common Types of Machine Learning Algorithms
  • Naïve Bayes Classifier (Supervised Learning/Classification)
  • K-Nearest Neighbor (Supervised Learning/Classification)
  • Linear Regression (Supervised Learning/Regression)
  • Decision Tree (Supervised Learning/Regression)
  • Ensemble Modelling (Supervised Learning/Regression)
  • K-Means Clustering (Unsupervised/Clustering)
  • Conclusion
  • Key Takeaways
5

Deep Learning

  • Difference Between Deep Learning and Machine Learning
  • So What Is Deep Learning Then?
  • The Brain and Deep Learning
  • Artificial Neural Networks (ANNs)
  • Backpropagation
  • The Various Neural Networks
  • Deep Learning Applications
  • Deep Learning Hardware
  • When to Use Deep Learning?
  • Drawbacks with Deep Learning
  • Conclusion
  • Key Takeaways
6

Robotic Process Automation (RPA)

  • What Is RPA?
  • Pros and Cons of RPA
  • What Can You Expect from RPA?
  • How to Implement RPA
  • RPA and AI
  • RPA in the Real World
  • Conclusion
  • Key Takeaways
7

Natural Language Processing (NLP)

  • The Challenges of NLP
  • Understanding How AI Translates Language
  • Voice Recognition
  • NLP in the Real World
  • Voice Commerce
  • Virtual Assistants
  • Chatbots
  • Future of NLP
  • Conclusion
  • Key Takeaways
8

Physical Robots

  • What Is a Robot?
  • Industrial and Commercial Robots
  • Robots in the Real World
  • Humanoid and Consumer Robots
  • The Three Laws of Robotics
  • Cybersecurity and Robots
  • Programming Robots for AI
  • The Future of Robots
  • Conclusion
  • Key Takeaways
9

Implementation of AI

  • Approaches to Implementing AI
  • The Steps for AI Implementation
  • Identify a Problem to Solve
  • Forming the Team
  • The Right Tools and Platforms
  • Deploy and Monitor the AI System
  • Conclusion
  • Key Takeaways
10

The Future of AI

  • Autonomous Cars
  • US vs. China
  • Technological Unemployment
  • The Weaponization of AI
  • Drug Discovery
  • Government
  • AGI (Artificial General Intelligence)
  • Social Good
  • Conclusion
  • Key Takeaways

1

AI Foundations

  • Exploring AI History and Key Concepts
2

Data

  • Understanding and Managing Data Types Effectively
3

Machine Learning

  • Reviewing Machine Learning Concepts
4

Deep Learning

  • Exploring Deep Learning Concepts and Challenges
5

Robotic Process Automation (RPA)

  • Enhancing Operational Efficiency through Robotic Process Automation
6

Natural Language Processing (NLP)

  • Revising NLP Concepts
7

Physical Robots

  • Transforming Work with Robotics and AI
8

Implementation of AI

  • Deploying AI Systems
9

The Future of AI

  • Charting AI’s Transformation

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Start with an Introduction to Artificial Intelligence Course to build a strong foundation in key concepts like machine learning, deep learning, and NLP. Follow a structured learning path:

  • Step 1: Learn Python (the most common AI programming language).
  • Step 2: Study math fundamentals (statistics, linear algebra, calculus).
  • Step 3: Take beginner-friendly AI/ML courses (like this one) with hands-on projects.
  • Step 4: Practice with real datasets on platforms like Kaggle.
  • Step 5: Explore advanced topics (neural networks, computer vision, etc.) through specialized courses.

This course is designed for absolute beginners, making AI easy to grasp without a technical background.

Yes! Many AI professionals are self-taught. The key is:

  • Structured Learning: Follow a well-organized course (like this AI course for beginners) to avoid confusion.
  • Hands-on Practice: Work on small projects (e.g., chatbots, prediction models) to reinforce learning.
  • Community & Resources: Use free tools (TensorFlow, PyTorch), forums (Stack Overflow), and AI communities for support.

This course provides real-world case studies, interactive labs, and step-by-step guidance, making self-learning effective and engaging.

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