Artificial Intelligence Course

Categories: Cybersecurity

About Course

Get on the fast track to a career in cybersecurity. In this certificate program, you’ll learn in-demand skills, and get AI training from Google experts. Learn at your own pace, no degree or experience required.

Equip learners with foundational to advanced knowledge of cybersecurity, covering threat identification, system protection, and proactive defense strategies using real-world tools and techniques.

Understand key AI and ML concepts and terminologies
Differentiate between supervised, unsupervised, and reinforcement learning
Build and train predictive models using real-world datasets
Explore deep learning, computer vision, and NLP techniques
Apply AI solutions to solve practical problems in various industries
Address ethical issues in AI development and deployment

Course Content

Module 1: Introduction to AI and Machine Learning

1.1 History and Evolution of AI
1.2 Types of Machine Learning
1.3 Applications of AI in Real Life
1.4 Quiz: AI Fundamentals

Module 2: Supervised and Unsupervised Learning

Module 3: Deep Learning and Neural Networks

Module 4: Natural Language Processing (NLP)

Module 5: Model Evaluation and Optimization

Module 6: Responsible AI and Capstone Project

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Basic Computer Skills: Ability to navigate operating systems (Windows/Linux/macOS), manage files, and install software.
Introductory Networking Knowledge: Understanding of IP addresses, DNS, HTTP/HTTPS, ports, and protocols.
Basic Command Line Knowledge: Familiarity with terminal or command prompt is helpful.
Problem-Solving Mindset: Willingness to think critically and investigate technical problems.
Educational Level: Suitable for senior secondary school students, undergraduates, graduates, or early-career professionals in IT or related fields.

Artificial Intelligence Course

About Course

What Will You Learn?

Course Content

Module 1: Introduction to AI and Machine Learning

1.1 History and Evolution of AI

1.2 Types of Machine Learning

1.3 Applications of AI in Real Life

1.4 Quiz: AI Fundamentals

Module 2: Supervised and Unsupervised Learning

2.1 Linear & Logistic Regression

2.2 Decision Trees and K-Means Clustering

2.3 Lab: Building a Classification Model

2.4 Peer Review: Model Comparison

Module 3: Deep Learning and Neural Networks

3.1 Understanding Neural Networks and Activation Functions

3.2 Introduction to Convolutional and Recurrent Neural Networks

3.3 Lab: Image Classification with CNN

3.4 Quiz: Deep Learning Basics

Module 4: Natural Language Processing (NLP)

4.1 Text Processing and Tokenization

4.2 Sentiment Analysis and Chatbots

4.3 Case Study: AI in Language Translation

4.4 Lab: Sentiment Analysis with Python

Module 5: Model Evaluation and Optimization

5.1 Overfitting vs Underfitting

5.2 Cross-Validation and Hyperparameter Tuning

5.3 Lab: Model Performance Metrics

5.4 Quiz: Evaluation Techniques

Module 6: Responsible AI and Capstone Project

6.1 Ethics in AI

6.2 Bias, Fairness, and Transparency

6.3 Final Project: Develop a Mini AI App for a Business Case

6.4 Certificate Submission and Peer Review