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Artificial Intelligence (AI)

Diploma Course

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Diploma in Artificial Intelligence

This diploma course is designed for individuals looking to develop a strong foundation in Artificial Intelligence. It covers essential AI concepts, machine learning algorithms, neural networks, and deep learning techniques. Through hands-on projects and real-world case studies, students will gain practical experience in training AI models, understanding data science principles, and implementing AI-driven solutions. Whether you're an aspiring AI engineer, data scientist,r tech enthusiast, this program provides the knowledge and skills needed to excel in the fast-evolving worldf artificial intelligence.

Month 1: Introduction to AI & Machine Learning

What is AI?: History and Scope
Typesf AI: Narrow AI, General AI, and Super AI
Applicationsf AI in Various Industries
Introduction to Python for AI
  • Python basics: Variables, Data Types, Loops, Functions
  • Python Libraries for AI: NumPy, Pandas, Matplotlib
  • Data Structures: Lists, Dictionaries, Tuples, and Setss
Supervised Learning Algorithms
  • Linear Regression
  • Logistic Regression
  • Decision Trees
Unsupervised Learning Algorithms
  • K-Means Clustering
  • Principal Component Analysis (PCA)
Supervised Learning Algorithms
  • Linear Regression
  • Logistic Regression
  • Decision Trees
Unsupervised Learning Algorithms
  • K-Means Clustering
  • Principal Component Analysis (PCA)


Month 2: Advanced Machine Learning & Neural Networks

Support Vector Machines (SVM)
k-Nearest Neighbors (k-NN)
Ensemble Methods: Bagging and Boosting
Model Evaluation Metrics: Confusion Matrix, ROC, Precision, Recall, F1-Score
Introduction to Neural Networks
Activation Functions (ReLU, Sigmoid, Tanh)
Feedforward Neural Networks and Backpropagation
Deep Learning Basics
  • Introduction to Deep Learning
  • Convolutional Neural Networks (CNNs) for Image Processing
  • Recurrent Neural Networks (RNNs) for Sequential Data
Text Preprocessing: Tokenization, Lemmatization, and Stemming
Sentiment Analysis
Word Embeddings: Word2Vec, GloVe
Introduction to Transformers and Language Models (BERT, GPT)


Month 3: AI Applications & Internship

AI in Healthcare, Finance, Retail, and Marketing
AI for Recommendation Engines
AI for Robotics and IoT
AI in Cybersecurity
AI and Ethics: Bias, Fairness, and Responsibility
Students select and workn a comprehensive AI project
  • Image Classification using CNN
  • Predictive Analytics using Machine Learning
  • Text Classificationr Sentiment Analysis using NLP
Project Presentation and Peer Review


Internship Program (1 Month)

  • Internship Setup: Partner with AI-driven organizations or industries
  • Hands-on Experience: Work on live projects under industry mentorship
    • Data Preprocessing and Feature Engineering
    • Model Development and Optimization
    • Model Development and Optimization
  • Evaluation and Feedback: Weekly evaluations based on performance
  • Internship Report Submission: Write a detailed report on the AI project, including the problem solved, methodology, and results
  • Final Presentation: Present the internship project to a panel for review

Tools and Platforms Used

  • Programming Language: Python
  • Libraries and Frameworks: TensorFlow, Keras, PyTorch, OpenCV, NLTK
  • Cloud Platforms: Google Colab, AWS, Microsoft Azure
  • Version Control: Git and GitHub for collaboration
  • Tools: Jupyter Notebooks, Anaconda
LMS