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Artificial Intelligence

Course Objective

An Artificial Intelligence course is designed to introduce learners to the theory, methods, tools, and applications of AI. The goal is to equip students with the knowledge and practical skills needed to build intelligent systems that can simulate human reasoning, learn from data, and make decisions.

This course provides a comprehensive introduction to Artificial Intelligence, focusing on the principles, techniques, and applications of AI systems. Learners will explore both the theoretical foundations and the practical tools used to design, build, and deploy AI models. The course covers key areas including machine learning, deep learning, natural language processing (NLP), computer vision, and reinforcement learning, while also addressing ethical implications of AI technologies.

By the end of the course, learners will be able to apply AI concepts to real-world problems and develop intelligent applications using popular AI frameworks like TensorFlow, scikit-learn, and PyTorch.

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🧠 What You Will Learn

Introduction to AI

History, definition, types of AI (narrow, general, super AI), applications

Python for AI

Basics of Python, NumPy, Pandas, Matplotlib (if needed)

Machine Learning (ML)

Supervised & unsupervised learning, regression, classification, clustering

Deep Learning (DL)

Neural networks, backpropagation, CNNs, RNNs, LSTMs, transfer learning

Natural Language Processing (NLP)

Text preprocessing, sentiment analysis, language models, transformers

Computer Vision

Image processing, object detection, facial recognition

Reinforcement Learning (RL)

Agents, environments, rewards, Q-learning

Tools & Frameworks

TensorFlow, Keras, PyTorch, OpenCV, scikit-learn

Ethics & Responsible AI

Bias, privacy, fairness, AI safety

Capstone Project

Real-world AI application like chatbots, recommendation system, etc.

📘 Course Objectives

By completing this course, students will:

Understand the fundamental concepts of AI and its real-world applications

Gain hands-on experience in designing and implementing AI models

Learn core machine learning techniques such as regression, classification, clustering

Explore advanced AI domains like deep learning, NLP, and computer vision

Use industry-standard tools and libraries for model development and deployment

Build a capstone project to demonstrate practical understanding

Understand AI ethics, bias, fairness, and responsible AI practices

🛠️ Skills You Gain

  • Problem-solving with machine learning algorithms
  • Building & training neural networks
  • Natural Language Processing (NLP)
  • Computer vision model development
  • Working with real datasets
  • Using AI libraries and tools (TensorFlow, scikit-learn, etc.)
  • Model evaluation & optimization

⏱️ Course Duration

Introductory:

4–8 weeks (part-time)

Intermediate:

3–6 months (part-time/full-time)

Advanced / PG:

6–12 months (full programs)

🎓 Who Should Enroll

This course is suitable for:

  • Students and professionals from engineering, computer science, IT, or data science
  • Anyone interested in transitioning to an AI-related role
  • Developers looking to upskill in AI, ML, and deep learning
  • Entrepreneurs and product managers exploring AI integration into products

📚 Prerequisites

🔰 Many beginner AI courses also teach Python and the math foundations if you're starting from scratch.

Basic math (linear algebra, probability, statistics)

Python programming

Logical thinking

Familiarity with data science concepts

Willingness to work with code

Basic calculus (for deep learning)

✅ Prerequisites

  • Basic programming knowledge (preferably Python)
  • Understanding of high school-level mathematics
  • (Optional) Prior exposure to data science or statistics is a plus

Note: Beginner-friendly versions of the course are available that include a Python and math refresher.

📦 Tools & Frameworks

Python

NumPy

Pandas

Scikit-learn

TensorFlow

PyTorch

Hugging Face Transformers

OpenAi API

Stable Diffusion

DALL.E

Jupyter

Google Colab

Streamlit

🧑‍💻 Learning Outcomes

After completing this course, students will be able to:

Apply AI and ML techniques to solve complex data-driven problems

Work with real datasets and AI tools used in the industry

Build and deploy AI-powered applications

Demonstrate a portfolio-ready project that showcases AI proficiency

Understand the societal and ethical implications of deploying AI

Artificial Intelligence Course Tools

1. Programming Languages
  • Python — The most popular language for AI development due to its rich libraries and simplicity.
  • R — Often used for statistics and data analysis.
  • Java and C++ — Used in some AI applications, especially where performance matters.
  • TensorFlow — Open-source library for machine learning and deep learning by Google.
  • PyTorch — Another popular open-source deep learning framework by Facebook.
  • Keras — High-level neural networks API, running on top of TensorFlow or Theano.
  • scikit-learn — For traditional machine learning algorithms and data preprocessing.
  • NumPy — For numerical computing.
  • Pandas — Data manipulation and analysis.
  • Matplotlib / Seaborn — For data visualization.
  • Jupyter Notebook — Interactive coding environment ideal for data exploration.
  • NLTK (Natural Language Toolkit) — Python library for working with human language data.
  • spaCy — Industrial-strength NLP library.
  • Transformers (by Hugging Face) — For state-of-the-art NLP models.
  • OpenCV — Open-source computer vision and image processing library.
  • YOLO (You Only Look Once) — Real-time object detection framework.
  • Google Colab — Free cloud-based Jupyter notebook environment with GPU support.
  • AWS SageMaker — Managed machine learning service for building, training, and deploying models.
  • Microsoft Azure ML Studio — Cloud platform for machine learning workflows.
  • Git — Version control system.
  • GitHub / GitLab / Bitbucket — Platforms for hosting code repositories and collaboration.
  • Docker — Containerization tool useful for reproducible AI environments.
  • Anaconda — Python distribution with many pre-installed data science and AI packages.

🧠 Artificial Intelligence Course Content

Most AI courses are broken down into the following core modules:
📦 Module 1: Introduction to Artificial Intelligence
  • What is AI? Definitions & history
  • Types of AI: Narrow, General, Super AI
  • Applications of AI in industry (finance, healthcare, robotics, etc.)
  • Limitations and challenges of AI
  • Overview of AI subfields (ML, DL, NLP, CV, RL)
  • Problem-solving and search algorithms o Uninformed sea (BFS, DFS) o Informed search (A*, greedy search)
  • Game playing: Minimax, Alpha-Beta pruning
  • Knowledge representation and reasoning
  • Logic and inference (Propositional & First-order logic)
  • Linear Algebra: vectors, matrices, dot products
  • Probability & Statistics: Bayes theorem, distributions, hypothesis testing
  • Calculus: gradients, derivatives (for deep learning)
  • Optimization: cost functions, gradient descent
  • Basics of Python programming
  • Libraries: NumPy, Pandas, Matplotlib, Seaborn
  • Data loading, preprocessing, and visualization
  • Introduction to Jupyter Notebooks and Colab
  • Supervised learning o Linear & logistic regression o Decision trees, Random Forests o K-Nearest Neighbors (KNN), Naive Bayes o Support Vector Machines (SVM)
  • Unsupervised learning o K-Means clustering o Hierarchical clustering o Principal Component Analysis (PCA)
  • Model evaluation: accuracy, precision, recall, F1-score, ROC-AUC
  • Introduction to neural networks
  • Perceptrons, forward & backpropagation
  • Activation functions, loss functions
  • Deep neural networks (DNNs)
  • Convolutional Neural Networks (CNNs) for image processing
  • Recurrent Neural Networks (RNNs), LSTM, GRU for sequence modeling
  • Transfer learning & pretrained models
  • Text preprocessing: tokenization, stemming, lemmatization
  • Bag of Words, TF-IDF
  • Word embeddings: Word2Vec, GloVe
  • Sentiment analysis, topic modeling
  • Language models: BERT, GPT
  • Chatbots and conversational AI basics
  • Image processing basics
  • Filters, edge detection, color spaces
  • Object detection and localization
  • Face recognition and image classification
  • OpenCV & CNN-based projects
  • Agent-environment interaction
  • Rewards, states, actions
  • Q-learning, Deep Q Networks (DQNs)
  • Policy vs Value-based methods
  • Applications in games, robotics, automation
  • scikit-learn: ML models and pipelines
  • TensorFlow and Keras: Deep learning
  • PyTorch: Custom model building
  • OpenCV: Image and video processing
  • NLTK, spaCy, Hugging Face: NLP
  • AI bias and fairness
  • Explainable AI (XAI)
  • AI and privacy issues
  • AI governance and regulations
  • Ethical dilemmas in autonomous systems
  • Choose a real-world problem
  • Data collection and cleaning
  • Model selection, training, and evaluation
  • Deployment (optional): Flask, FastAPI, Streamlit
  • Final presentation and report

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