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Artificial Intelligence Tutorial

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Explore different AI approaches, including symbolic AI, rule-based systems, data-driven AI, hybrid AI, reactive AI, goal-driven AI, predictive AI, and generative AI. Learn how these approaches work, compare their advantages and limitations, understand real-world applications, and choose the right AI approach through practical examples and activities.

15 Modules

88 Lessons

English

1.5 Hrs

Show more

Learn via Video Course

Artificial Intelligence and Machine Learning Course Logo

Artificial Intelligence and Machine Learning Course

55 hrs

Reading Plan

MODULE 1

AI Fundamentals and Real-World Problem Solving

6 mins
lessonIntroduction to Artificial Intelligence

1 min

lessonHistory and Evolution of AI

1 min

lessonAI vs. Automation vs. Machine Learning vs. Deep Learning

1 min

lessonUnderstanding Generative AI

1 min

lessonAI Applications, Capabilities, and Limitations

1 min

lessonIdentifying AI Problems and Defining Success Criteria

1 min

MODULE 2

Core AI Approaches and Intelligent Systems

6 mins
lessonSymbolic and Rule-Based AI

1 min

lessonStatistical and Data-Driven AI

1 min

lessonHybrid AI Systems

1 min

lessonReactive and Goal-Driven AI Systems

1 min

lessonPredictive AI vs. Generative AI

1 min

lessonChoosing the Right AI Approach

1 min

MODULE 3

Intelligent Agents and Environments

6 mins
lessonUnderstanding Intelligent Agents

1 min

lessonSensors, Actuators, Percepts, and Actions

1 min

lessonThe PEAS Framework

1 min

lessonTypes of Intelligent Agents

1 min

lessonUnderstanding AI Environments

1 min

lessonBuilding a Simple Intelligent Agent

1 min

MODULE 4

AI Problem-Solving and Search Algorithms

5 mins
lessonProblem Formulation in AI

1 min

lessonUninformed Search Algorithms

1 min

lessonInformed Search and Heuristics

1 min

lessonImplementing A Search in Python

1 min

lessonComparing Search Algorithms and Their Applications

1 min

MODULE 5

Knowledge Representation and Logical Reasoning

5 mins
lessonIntroduction to Knowledge Representation

1 min

lessonFacts, Rules, and Semantic Networks

1 min

lessonPropositional Logic and Logical Operators

1 min

lessonFirst-Order Logic and Inference

1 min

lessonRule-Based Reasoning and Expert Systems

1 min

MODULE 6

Mathematics and Probability for AI

6 mins
lessonMathematical Foundations for AI

1 min

lessonLinear Algebra - Vectors, Matrices, and Dot Products

1 min

lessonStatistics and Descriptive Statistics

1 min

lessonProbability and Conditional Probability

1 min

lessonBayes' Theorem and Probabilistic Reasoning

1 min

lessonOptimization and Gradient Descent

1 min

MODULE 7

Machine Learning Fundamentals

6 mins
lessonIntroduction to Machine Learning

1 min

lessonTypes of Machine Learning

1 min

lessonUnderstanding Datasets, Features, and Labels

1 min

lessonCommon Machine Learning Algorithms

1 min

lessonBuilding Your First Machine Learning Model with Python

1 min

lessonEvaluating Machine Learning Models

1 min

MODULE 8

Data Preparation and Feature Engineering

6 mins
lessonUnderstanding Data Preparation

1 min

lessonHandling Missing Values and Duplicate Data

1 min

lessonEncoding Categorical Data

1 min

lessonFeature Scaling and Outlier Handling

1 min

lessonFeature Engineering and Feature Selection

1 min

lessonBuilding a Data Preprocessing Pipeline

1 min

MODULE 9

Supervised Learning Algorithms

6 mins
lessonUnderstanding Supervised Learning

1 min

lessonLinear Regression and Logistic Regression

1 min

lessonDecision Trees and Random Forests

1 min

lessonSupport Vector Machines and K-Nearest Neighbors

1 min

lessonEvaluating Supervised Learning Models

1 min

lessonHyperparameter Tuning and Model Selection

1 min

MODULE 10

Unsupervised Learning Algorithms

6 mins
lessonIntroduction to Unsupervised Learning

1 min

lessonClustering and the K-Means Algorithm

1 min

lessonHierarchical Clustering and DBSCAN

1 min

lessonDimensionality Reduction and Principal Component Analysis

1 min

lessonAnomaly Detection

1 min

lessonEvaluating and Applying Unsupervised Learning

1 min

MODULE 11

Neural Networks and Deep Learning

6 mins
lessonIntroduction to Neural Networks

1 min

lessonActivation Functions and Network Architecture

1 min

lessonHow Neural Networks Learn

1 min

lessonUnderstanding Deep Learning

1 min

lessonBuilding a Neural Network with Python

1 min

lessonOverfitting, Regularization, and Model Improvement

1 min

MODULE 12

Natural Language Processing (NLP) and Computer Vision

6 mins
lessonIntroduction to Natural Language Processing

1 min

lessonText Preprocessing and Representation

1 min

lessonCommon NLP Tasks and Sentiment Analysis

1 min

lessonIntroduction to Computer Vision

1 min

lessonImage Classification with Python

1 min

lessonReal-World Applications and Challenges

1 min

MODULE 13

Generative AI Large Language Models (LLMs) and Prompt Engineering

6 mins
lessonUnderstanding Generative AI

1 min

lessonLarge Language Models (LLMs)

1 min

lessonPrompt Engineering

1 min

lessonAdvanced Prompting and Reliable AI Outputs

1 min

lessonBuilding a Simple LLM Application with Python

1 min

lessonRetrieval-Augmented Generation and AI Agents

1 min

MODULE 14

AI Model Evaluation Deployment and MLOps

6 mins
lessonEvaluating AI Model Performance

1 min

lessonModel Deployment and AI APIs

1 min

lessonIntroduction to MLOps

1 min

lessonMonitoring, Data Drift, and Model Maintenance

1 min

lessonModel Optimization and Deployment Strategies

1 min

lessonThe End-to-End AI Lifecycle

1 min

MODULE 15

Responsible AI Ethics Real-World Projects and Future Trends

6 mins
lessonIntroduction to Responsible AI

1 min

lessonBias, Fairness, and Explainability

1 min

lessonPrivacy, Security, and AI Safety

1 min

lessonResponsible AI Governance and Regulations

1 min

lessonReal-World AI Projects and Responsible Implementation

1 min

lessonFuture Trends and Career Opportunities in AI

1 min

Artificial Intelligence Tutorial

This beginner-friendly AI tutorial introduces the fundamentals of Artificial Intelligence, different AI approaches, and their real-world applications. Learn about symbolic AI, rule-based systems, machine learning, hybrid AI, predictive AI, and generative AI through simple explanations, practical examples, and Python code. Understand how to choose the right AI approach for different problems and build a strong foundation in AI concepts.

AI Tutorial for Beginners: Types, Examples & Applications

Learn AI fundamentals, types of AI, rule-based and data-driven systems, predictive and generative AI, real-world applications, and how to choose the right AI approach.

AI Tutorial for Beginners: Types, Examples & Applications

Learn AI fundamentals, types of AI, rule-based and data-driven systems, predictive and generative AI, real-world applications, and how to choose the right AI approach.

Prerequisites

This course is suitable for:

  • Basic computer knowledge.
  • Basic understanding of programming concepts is helpful but not mandatory.
  • Familiarity with Python is beneficial for understanding the code examples.
  • No prior knowledge of Artificial Intelligence or Machine Learning is required.

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Established in 2014 and acquired by the HCL Group in 2022, HCL GUVI is dedicated to providing effective and high-quality learning and skilling programs that transcend language barriers in technology education. HCL GUVI today is trusted by over 4.8 million users, 20 languages and 2500+ corporate partners.

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Refer & Earn

HCL GUVI (Grab Your Vernacular Imprint) Geek Network Private Limited is a leading online learning and skills development company, incubated by IIT Madras and IIM Ahmedabad.

Established in 2014 and acquired by the HCL Group in 2022, HCL GUVI is dedicated to providing effective and high-quality learning and skilling programs that transcend language barriers in technology education. HCL GUVI today is trusted by over 4.8 million users, 20 languages and 2500+ corporate partners.

app storeplay storeRefer & Earn
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