Introduction to Artificial Intelligence- AI for beginners

Introduction to Artificial Intelligence- AI for beginners

Artificial Intelligence (AI) is revolutionizing the modern world, driving innovation and efficiency across various industries. Key technologies like neural networks, natural language processing, reinforcement learning, and chatbots drive these transformations. As AI technology advances, it’s reshaping sectors and the world.

In this course we will talk about all that you need to know to get started in the field of AI. You will get familiar with the main approaches and research fields of artificial intelligence. You will know the advantages and disadvantages of AI as well as its possible applications in the future.

The course is split into 5 main sections starting from the history of AI. In this section we cover the basics and the history, next we will go into the present day applications of AI followed by the topics on the main categories and methods of AI. Lastly we will speak about cons and pros as well as the future of AI technology.

What you’ll learn

Learn about the History of AI
Learn about 4 types of Artificial Intelligence systems
Know what are the characteristics of Artificial Intelligence
Discover the future possiblities of AI technology
Discover 3 Paradigms of AI: Fuzzy Logic, Neural Networks and Genetic Algorithms
Know about conventional and computational types of AI

Course content

Module 1: Introduction to Artificial Intelligence

Overview of AI: Definition and History

Key Concepts and Terminologies

Types of AI: Narrow AI vs. General AI

Applications of AI in Various Industries

Ethical Considerations in AI

Introduction to AI Programming Languages (Python)

Module 2: Machine Learning Basics

Introduction to Machine Learning (ML)

Types of ML: Supervised, Unsupervised, Reinforcement Learning

Key Algorithms: Linear Regression, Decision Trees, K-Means Clustering

Data Preprocessing and Cleaning

Feature Engineering

Model Training and Evaluation

Module 3: Deep Learning Fundamentals

Introduction to Neural Networks

Basics of Deep Learning

Key Concepts: Activation Functions, Loss Functions, Backpropagation

Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs)

Hands-on: Building a Simple Neural Network using TensorFlow/Keras

Module 4: Natural Language Processing (NLP)

Introduction to NLP

Text Preprocessing Techniques

Basic NLP Algorithms: Sentiment Analysis, Text Classification

Advanced NLP Techniques: Word Embeddings, Sequence Models

Hands-on: Building an NLP Model for Text Classification

Module 5: AI Tools and Platforms :

Overview of Popular AI Tools (TensorFlow, PyTorch, Scikit-learn)

AI as a Service (AIaaS) Platforms (Google AI, AWS AI, Microsoft Azure AI)

Hands-on: Using AIaaS for Quick Prototyping

International student Fee : USD150$

Requirements

Basic Math Knowledge
Being Familiar with Computer Science

Who this course is for:

Students studying Computer Science
Students studying Computer Engineering
Students studying Mathematics
People interested in Technology
People interested in Science
People interested in Programming
Professionals in STEM fields
Programmers

Course Benefits:

Gain a solid foundation in AI concepts and technologies
Learn practical applications of AI in various industries
Develop skills in AI tools and techniques
Flexible learning options with both in-campus and online classes
Interactive learning through hands-on exercises and projects
Enhanced career prospects in the growing field of AI

Career Path:

Entry-Level Positions: AI Research Assistant, Data Analyst, Junior Machine Learning Engineer
Mid-Level Positions: AI Developer, Data Scientist, Machine Learning Engineer
Advanced Positions: AI Project Manager, AI Consultant, Senior Data Scientist
Leadership Roles: Chief AI Officer, Director of AI, AI Strategy Manager

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Job Interview Questions

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