Mastering Ai: Basics To Aws Certified Ai Practitioner

Unlock the future with AI — from foundational concepts to practical AWS deployment in one comprehensive course.

What you'll learn

History, ethics, and societal implications of AI

Core AI concepts: logic, reasoning, search, probability

Machine learning techniques: supervised, unsupervised, reinforcement learning

Deep learning architectures including CNNs, RNNs, and generative models

Practical implementation of AI with AWS services like SageMaker, Lex, Polly, Rekognition

Preparation for AWS Certified AI Practitioner exam

Real-world case studies and ethical AI deployment strategies

Requirements

Basic understanding of mathematics (algebra, probability, statistics)

Familiarity with programming (preferably Python)

Interest in AI/ML concepts and technologies

No prior AI experience required — this course starts from scratch

Description

Artificial Intelligence (AI) is transforming the world at an unprecedented pace — revolutionizing industries, reshaping how we work, and unlocking powerful tools that once existed only in science fiction. This course is your gateway to becoming a confident AI practitioner. Whether you're a student, developer, or business professional, you’ll gain a solid foundation in AI, machine learning, deep learning, and AWS-based AI services, preparing you for real-world implementation and certification.Section 1: Introduction to Artificial IntelligenceThis section lays the groundwork for understanding AI by exploring its definition and historical evolution. You'll learn how AI evolved from rule-based systems to modern-day intelligent agents. We then highlight AI’s growing importance and diverse applications — from healthcare to finance to autonomous vehicles. The section concludes with a thoughtful discussion on AI ethics, societal impact, and the moral responsibilities of building intelligent systems.Section 2: Foundations of Artificial IntelligenceHere, we dive into the core building blocks of AI. Beginning with an overview, you’ll study logic and reasoning systems that enable machines to make decisions. You'll then explore probability and statistics as a backbone for uncertainty handling in AI. Important AI problem-solving strategies like search algorithms are introduced, followed by knowledge representation and reasoning — enabling machines to ‘think’ and ‘understand’ their environment.Section 3: Machine Learning in Artificial IntelligenceMachine Learning (ML) is a core component of modern AI. This section starts with an introduction to ML and delves into supervised and unsupervised learning paradigms. Concepts such as clustering, distance metrics, and dimensionality reduction are explained with real-world analogies. We also explore association rule learning, reinforcement learning, and its types. By the end, you'll understand how machines learn from data and improve over time.Section 4: Deep LearningDeep learning powers today’s most advanced AI applications. This section begins with the basics of neural networks, followed by an introduction to deep learning architectures. You'll gain insights into CNNs used for image recognition, RNNs used for sequential data, and generative models for AI creativity. Topics like transfer learning and fine-tuning are also covered to show how pre-trained models can be leveraged for better performance.Section 5: AWS Certified AI PractitionerThis final section prepares students for AWS AI certification and practical industry applications. It starts with a comprehensive introduction to AWS AI and ML tools, such as SageMaker, DeepLens, Lex, Polly, and Rekognition. Students will learn to build, train, and deploy models using AWS infrastructure. We also explore AI services in NLP and computer vision, model evaluation, ethical AI development, prompt engineering, and best practices. The section includes case studies, exam prep, and continuous improvement strategies to reinforce learning.Conclusion:By the end of this course, you’ll not only understand the theoretical foundations of AI but also gain hands-on experience with powerful tools used by industry professionals. Whether you're looking to apply AI in business, pursue a technical career, or pass the AWS Certified AI Practitioner exam, this course equips you with the knowledge and confidence to move forward.

Overview

Section 1: Introduction to Artificial Intelligence

Lecture 1 Definition and Brief History of AI

Lecture 2 Importance and Applications of AI

Lecture 3 AI Ethics and Societal Impacts

Section 2: Foundations of Artificial Intelligence

Lecture 4 Introduction

Lecture 5 Logic and Reasoning

Lecture 6 Probability and Statistics

Lecture 7 Search Algorithms

Lecture 8 Knowledge Representation and Reasoning

Section 3: Machine Learning of Artificial Intelligence

Lecture 9 Introduction to Machine Learning AI

Lecture 10 Supervised Learning

Lecture 11 Unsupervised Learning

Lecture 12 Clustering

Lecture 13 Distance Measures

Lecture 14 Dimensionality Reduction

Lecture 15 Association Rule Learning

Lecture 16 Reinforcement Learning

Lecture 17 Types of Reinforcement Learning Part 1

Lecture 18 Types of Reinforcement Learning Part 2

Section 4: Deep Learning

Lecture 19 Neural Networks Basics

Lecture 20 Deep Learning Introduction

Lecture 21 Convolutional Neural Networks (CNNs)

Lecture 22 Recurrent Neural Networks (RNN)

Lecture 23 Generative Models

Lecture 24 Transfer Learning and Fine Tuning

Section 5: AWS Certified AI Practitioner

Lecture 25 Introduction to AWS Certified AI Practitioner

Lecture 26 Understanding AI and ML

Lecture 27 Natural Language Processing (NLP)

Lecture 28 Computer Vison (CV)

Lecture 29 Applications of AI in Various Industries

Lecture 30 Supervised vs Unsupervised Machine Learning

Lecture 31 Algorithms of Supervised and Unsupervised Machine Learning

Lecture 32 Reinforcement Learning (RL)

Lecture 33 Principal Component Analysis (PCA)

Lecture 34 Basic Questions

Lecture 35 Introduction to AWS AI Services

Lecture 36 Amazon SageMaker

Lecture 37 Aws DeepLens

Lecture 38 Amazon Comprehend

Lecture 39 Case Studies

Lecture 40 Intermediate Questions

Lecture 41 Implementing AI Solutions with AWS

Lecture 42 Woring with Amazon SageMaker

Lecture 43 Using AWS Lex

Lecture 44 Using AWS Polly

Lecture 45 AWS Rekognition

Lecture 46 Combining AWS Services

Lecture 47 Understanding Foundation Models

Lecture 48 Model Selection and Architecture

Lecture 49 Data Preperation and Preprocessing

Lecture 50 Model Training and Optimization

Lecture 51 Model Evaluation and Deployment

Lecture 52 Summary

Lecture 53 Ethical Considerations and Best Practices in AI-ML

Lecture 54 Introduction to Prompt Engineering

Lecture 55 Continuous Improvement

Lecture 56 Exam Overview

Lecture 57 Advanced Questions

Students and professionals seeking a career in AI and machine learning,Data scientists and developers wanting AWS certification,Business leaders and product managers exploring AI implementation,Educators and researchers aiming to understand or teach AI fundamentals,Anyone curious about how AI works and how to use it responsibly and effectively