Introduction to Artificial Intelligence (AI) 3-WEEK

Course Overview:

Artificial Intelligence (AI) is transforming how we live, work, and interact with technology. This course provides a comprehensive introduction to the fundamental concepts, techniques, and applications of AI. Students will explore the core principles behind intelligent systems, learn how machines mimic human reasoning and perception, and understand the real-world impact of AI across industries.

By the end of this course, learners will gain both theoretical knowledge and hands-on experience with AI algorithms, tools, and frameworks.

Course Objectives:

After completing this course, students will be able to:

Understand the history, evolution, and scope of Artificial Intelligence.
Explain key AI concepts such as machine learning, deep learning, and neural networks.
Analyze real-world problems that can be solved using AI techniques.
Implement basic AI models using Python and libraries like TensorFlow or Scikit-learn.
Evaluate the ethical and social implications of AI technologies.
Develop small AI-based applications and understand how AI integrates into modern systems.

Prerequisites:

Basic knowledge of programming (preferably Python)
Familiarity with mathematics (algebra, probability, and statistics)
Logical reasoning and analytical skills

Course Duration:

8–12 Weeks (Flexible depending on institution or training module)

Course Modules:

Module 1: Introduction to Artificial Intelligence

What is AI?
History and Evolution of AI
AI vs. Machine Learning vs. Deep Learning
Applications of AI in real life (healthcare, finance, robotics, etc.)
The AI Ecosystem and Future Trends

Module 2: Problem-Solving and Search in AI

Problem formulation and representation
Uninformed (blind) search techniques: BFS, DFS, Uniform Cost Search
Informed (heuristic) search: A*, Greedy Best-First Search
Constraint Satisfaction Problems (CSPs)
Optimization techniques and game playing (Minimax, Alpha-Beta pruning)

Module 3: Knowledge Representation and Reasoning

Representing knowledge using logic
Propositional and Predicate Logic
Inference mechanisms and reasoning
Semantic networks, frames, and ontologies
Expert systems and rule-based reasoning

Module 4: Machine Learning Basics

What is Machine Learning (ML)?
Supervised vs. Unsupervised Learning
Classification and Regression algorithms (Linear Regression, Decision Trees, Naive Bayes)
Clustering techniques (K-Means, Hierarchical Clustering)
Evaluation metrics and model performance

Module 5: Neural Networks and Deep Learning

Introduction to Artificial Neural Networks
Perceptron and Multi-Layer Perceptron
Backpropagation and activation functions
Introduction to Deep Learning and Convolutional Neural Networks (CNNs)
Applications in image and speech recognition

Module 6: Natural Language Processing (NLP)

Introduction to NLP and text processing
Tokenization, Stemming, and Lemmatization
Bag of Words and Word Embeddings (Word2Vec, GloVe)
Text classification and sentiment analysis
Chatbots and language models (Introduction to GPT & LLMs)

Module 7: Robotics and Computer Vision

Overview of AI in robotics
Path planning and navigation
Basics of Computer Vision
Object detection and recognition
Human-robot interaction

Module 8: AI Tools and Frameworks

Python for AI
TensorFlow, PyTorch, and Scikit-learn basics
Data preprocessing with Pandas and NumPy
Visualization using Matplotlib and Seaborn
Building a mini AI project (hands-on)

Module 9: Ethics, Challenges, and Future of AI

Ethical issues in AI (bias, privacy, job displacement)
Responsible and explainable AI
AI and cybersecurity
Government policies and AI governance
The future of AI: trends and emerging technologies

Assessment Methods:

Quizzes and Assignments after each module
Mini Project (e.g., Chatbot, Image Classifier, or Sentiment Analyzer)
Midterm and Final Exams (Theory + Practical)
Group Discussion and Presentation on AI applications and ethics

Learning Outcomes:

By the end of this course, students will:

Understand how AI systems function and their underlying logic.
Be able to apply AI algorithms to solve real-world problems.
Gain foundational knowledge to pursue advanced AI, ML, or Data Science courses.
Develop critical thinking about the social, legal, and ethical aspects of AI.

Recommended Textbooks & References:

Artificial Intelligence: A Modern Approach – Stuart Russell & Peter Norvig
Machine Learning – Tom M. Mitchell
Deep Learning – Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Python Machine Learning – Sebastian Raschka
Online resources: Coursera, edX, NPTEL, and Kaggle tutorials

Software & Tools Used:

Python 3.x
Jupyter Notebook / Google Colab
TensorFlow / PyTorch
Scikit-learn, NumPy, Pandas
OpenCV for computer vision
NLTK / spaCy for NLP

Certification:

Upon successful completion of all modules, quizzes, and projects, participants will receive a “Certificate of Completion in Introduction to Artificial Intelligence” recognized by the institution or training provider.

🔹 1. Fundamentals of Artificial Intelligence
What AI is and how it works
Real-world applications across industries
Types of AI and intelligent systems
🔹 2. Python Programming for AI
Writing clean and efficient Python code
Using core libraries like NumPy, Pandas, and Matplotlib
Handling and analyzing data for AI tasks
🔹 3. Machine Learning Essentials
Understanding supervised, unsupervised, and reinforcement learning
Training models and evaluating performance
Preventing overfitting and improving accuracy
🔹 4. Major ML Algorithms
Linear Regression, Logistic Regression
Decision Trees, Random Forest
K-Means Clustering, PCA
Naive Bayes, KNN, and more
🔹 5. Deep Learning & Neural Networks
Building neural network architectures
Understanding activation functions and layers
Training models using TensorFlow or PyTorch
🔹 6. Computer Vision Techniques
Image classification with CNNs
Object detection and image recognition
Working with real-time video streams
🔹 7. Natural Language Processing (NLP)
Text processing and sentiment analysis
Using transformer-based models
Creating chatbots and text-based AI projects
🔹 8. AI in Robotics & Automation
Sensor data processing
Intelligent decision-making in robots
Integration of AI with hardware
🔹 9. Ethical & Responsible AI
Avoiding bias in models
Ensuring fairness, transparency, and safety
Understanding responsible AI guidelines
🔹 10. Real-World Projects & Portfolio Building
Build complete AI projects from scratch
Work with real datasets
Create your own machine learning and deep learning models
Prepare a portfolio to showcase your skills
If you want, I can also create:
✅ A short version for product cards
✅ A long SEO version
✅ Separate modules, week-wise breakdown
✅ A full course curriculum PDF

1. Complete Course Notes & PDFs
Well-structured lecture notes
Downloadable PDFs for every module
Diagrams and flowcharts for easy understanding
🎥 2. Video Lectures & Tutorials
High-quality recorded sessions
Step-by-step coding explanations
Real-world case study walkthroughs
💻 3. Source Code Files
Python scripts for all AI algorithms
Jupyter notebooks for hands-on practice
Dataset-ready project templates
📊 4. Practice Datasets
Real-world datasets for ML & AI projects
Clean and raw data for training and testing
Computer Vision and NLP datasets
🛠️ 5. Project Kits & Resources (If Applicable)
Robotics and sensor data sheets
IoT project samples
Hardware integration guides
📁 6. Assignments & Quizzes
Weekly assignments
Concept-based quizzes
Model-building challenges
🏆 7. Certification Upon Completion
Official course completion certificate
Can be shared on LinkedIn, resume, or portfolio
🤖 8. Bonus Learning Materials
AI career roadmap
Interview preparation notes
List of AI tools, libraries, and frameworks
If you want, I can also prepare:
✅ A shorter version
✅ A more detailed “What’s Included” list
✅ A comparison chart (Basic vs Pro version)

🔹 1. Basic Computer Knowledge
You should know how to use a computer, browse files, and install software. No advanced skills needed.
🔹 2. Laptop or Desktop Required
A system with the following minimum specifications:
4GB RAM (8GB recommended)
Windows, macOS, or Linux
Stable internet connection
🔹 3. No Prior Programming Required (Beginner-Friendly)
This course starts from the basics.
However, having little familiarity with Python is helpful (not compulsory).
🔹 4. Install Required Software
You will need to install:
Python (latest version)
Jupyter Notebook / VS Code
Required Python libraries (NumPy, Pandas, OpenCV, TensorFlow etc.)
Installation instructions are provided in the course.
🔹 5. Willingness to Learn & Practice
AI requires practice and experimentation.
Be ready to:
Try coding exercises
Run models
Complete small projects
🔹 6. Follow the Step-by-Step Instructions Provided
All lectures, assignments, project files, and datasets are organized for smooth learning.
Just follow the modules in the given order.
🔹 7. Join Support Group (If Provided)
Access to discussion groups or instructor support helps you:
Clear doubts
Improve learning
Complete projects faster
If you want, I can also create:
✅ A short version for product cards
✅ A “Who Should Take This Course?” section
✅ FAQ section for your AI course

🔹 1. Students & Engineering Learners
B.Tech, BCA, MCA, Diploma students
Anyone preparing for tech careers
Learners who want hands-on AI and ML experience
🔹 2. Beginners in Programming & Technology
Individuals with little or no coding background
Anyone who wants to start with Python and AI basics
Learners shifting from non-technical fields into tech
🔹 3. Working Professionals
Software developers
IT engineers and technical staff
Professionals wanting to upgrade to AI/ML roles
🔹 4. Robotics & Automation Enthusiasts
Students interested in robotics projects
Learners who want to build AI-powered robots
Anyone working with sensors, microcontrollers, and automation
🔹 5. Data Science Aspirants
Learners who want to build a strong AI foundation
Beginners preparing for data science jobs
Students focusing on model building and real-world datasets
🔹 6. Entrepreneurs & Innovators
Startup founders exploring AI solutions
Business owners wanting to integrate AI into their products
Innovators interested in automation and smart technologies
🔹 7. Anyone Interested in the Future of Technology
Tech enthusiasts
Curious learners
Individuals who want to understand how AI shapes the world
If you want, I can also write:
✅ Course Outcomes
✅ Who Should Not Take This Course
✅ FAQ section

Course Content

Introduction to AI & its applications
What is AI? Types of AI: Weak AI, Strong AI, Super AI Applications in daily life and industries History & future of AI

What is AI?

01:05

Mathematics for AI
Linear Algebra basics Probability & statistics Optimization concepts Understanding matrices and vectors in ML

Python for AI
Python basics Libraries: NumPy, Pandas, Matplotlib Working with data sets Data preprocessing and cleaning

Machine Learning Fundamentals
What is Machine Learning (ML)? Types of ML: Supervised, Unsupervised, Reinforcement Model training & evaluation Overfitting vs underfitting

Supervised Learning Algorithms
Linear & Logistic Regression Decision Trees & Random Forest Naive Bayes K-Nearest Neighbors Practical implementation

Unsupervised Learning Algorithms
Clustering: K-Means, Hierarchical Dimensionality Reduction: PCA Anomaly detection Feature extraction

Neural Networks & Deep Learning
Basics of neural networks Activation functions Training deep models Introduction to TensorFlow/PyTorch

Computer Vision
Image classification Convolutional Neural Networks (CNN) Object detection Real-time video processing

Natural Language Processing (NLP)
Text preprocessing Sentiment analysis Transformers & GPT models Chatbots and language understanding

AI in Robotics
Sensors and perception Motion control & decision-making Using AI models in robotic systems Practical automation examples

Ethical & Responsible AI
Bias in AI Privacy concerns Fairness & transparency Future challenges

Real-World AI Projects
Predictive analytics Face recognition Smart home automation Industry-based case studies Capstone project

A course by

Shankar kumar

AI Engineer

Student Ratings & Reviews

5.0

Total 2 Ratings

2 Ratings

0 Rating

ANKIT KUMAR

3 weeks ago

EXCELLENT

Shankar kumar

4 months ago

thanks for sharing it

Price:
Original price was: ₹1,700.00.Current price is: ₹249.00.
Instructor:Shankar kumar
Duration: 20 hours 045 minutes
Lessons:1
Students:5
Level:Intermediate

Introduction to Artificial Intelligence (AI) 3-WEEK

Course Overview:

Course Objectives:

Prerequisites:

Course Duration:

Course Modules:

Module 1: Introduction to Artificial Intelligence

Module 2: Problem-Solving and Search in AI

Module 3: Knowledge Representation and Reasoning

Module 4: Machine Learning Basics

Module 5: Neural Networks and Deep Learning

Module 6: Natural Language Processing (NLP)

Module 7: Robotics and Computer Vision

Module 8: AI Tools and Frameworks

Module 9: Ethics, Challenges, and Future of AI

Assessment Methods:

Learning Outcomes:

Recommended Textbooks & References:

Software & Tools Used:

Certification:

What Will You Learn?

Material Includes

Requirements

Audience

Course Content

Introduction to AI & its applications What is AI? Types of AI: Weak AI, Strong AI, Super AI Applications in daily life and industries History & future of AI

What is AI?

Mathematics for AI Linear Algebra basics Probability & statistics Optimization concepts Understanding matrices and vectors in ML

Python for AI Python basics Libraries: NumPy, Pandas, Matplotlib Working with data sets Data preprocessing and cleaning

Machine Learning Fundamentals What is Machine Learning (ML)? Types of ML: Supervised, Unsupervised, Reinforcement Model training & evaluation Overfitting vs underfitting

Supervised Learning Algorithms Linear & Logistic Regression Decision Trees & Random Forest Naive Bayes K-Nearest Neighbors Practical implementation

Unsupervised Learning Algorithms Clustering: K-Means, Hierarchical Dimensionality Reduction: PCA Anomaly detection Feature extraction

Neural Networks & Deep Learning Basics of neural networks Activation functions Training deep models Introduction to TensorFlow/PyTorch

Computer Vision Image classification Convolutional Neural Networks (CNN) Object detection Real-time video processing

Natural Language Processing (NLP) Text preprocessing Sentiment analysis Transformers & GPT models Chatbots and language understanding

AI in Robotics Sensors and perception Motion control & decision-making Using AI models in robotic systems Practical automation examples

Ethical & Responsible AI Bias in AI Privacy concerns Fairness & transparency Future challenges

Real-World AI Projects Predictive analytics Face recognition Smart home automation Industry-based case studies Capstone project