What you'll learn
The Diploma in Artificial Intelligence is a comprehensive program designed to build strong foundations in AI, Machine Learning, Data Science, and modern automation technologies. This course covers core concepts like Python programming, data handling, algorithms, deep learning, neural networks, natural language processing, and real-world AI applications. Students gain hands-on experience through practical labs, projects, and industry-oriented tools, preparing them for high-demand careers in AI development, data analysis, automation, and intelligent system design.
Show More
Course Syllabus
Module 1: Fundamentals of AI & Computer Science
- Introduction to Artificial Intelligence
- History & Applications of AI
- Basics of Computer Systems
- Number Systems & Logic
- Introduction to Linux / Windows for AI
- Basics of Algorithms & Flowcharts
Module 2: Python Programming for AI
- Introduction to Python
- Variables, Data Types, Conditional Statements
- Loops & Functions
- Lists, Tuples, Dictionaries
- File Handling
- Object-Oriented Programming (OOP)
- Python Libraries for AI:
- NumPy
- Pandas
- Matplotlib
- Seaborn
Module 3: Mathematics & Statistics for AI
- Linear Algebra
- Vectors, Matrices, Determinants
- Calculus Basics
- Probability & Statistics
- Mean, Median, Mode
- Variance & Standard Deviation
- Probability Distributions
- Optimization Basics (Gradient Descent)
Module 4: Data Science & Data Preprocessing
- Introduction to Data Science
- Data Collection Techniques
- Data Cleaning & Preprocessing
- Handling Missing Values
- Data Transformation
- Feature Engineering
- Data Visualization (Matplotlib / Seaborn)
Module 5: Machine Learning (ML)
Supervised Learning
- Linear Regression
- Logistic Regression
- Decision Tree
- Random Forest
- Support Vector Machine (SVM)
- K-Nearest Neighbors (KNN)
Unsupervised Learning
- K-Means Clustering
- Hierarchical Clustering
- PCA (Dimensionality Reduction)
Model Evaluation & Metrics
- Accuracy, Precision, Recall
- Confusion Matrix
- F1-Score
- Cross-Validation
Module 6: Deep Learning (DL)
- Introduction to Neural Networks
- Activation Functions
- Feed-Forward Neural Networks
- Backpropagation
- Introduction to TensorFlow / Keras
Neural Network Architectures
- CNN (Convolutional Neural Networks)
- RNN (Recurrent Neural Networks)
- LSTM Networks
- Transfer Learning
Module 7: Natural Language Processing (NLP)
- NLP Basics
- Text Preprocessing
- Tokenization
- Stemming / Lemmatization
- Bag of Words & TF-IDF
- Sentiment Analysis
- Chatbots & Conversational AI Basics
Module 8: AI Tools & Technologies
- Google Colab
- Jupyter Notebook
- Kaggle
- Git / GitHub
- APIs for AI
- Auto-ML Tools
Module 9: AI Project Development
- Project Planning
- Dataset Selection
- Model Training
- Model Deployment
- Flask / FastAPI
- Cloud Deployment (Optional)
- AWS / Azure / Google Cloud
- Documentation & Presentation
Module 10: Mini Projects & Final Project
Examples:
- Face Detection System
- Spam Email Classifier
- Movie Recommendation System
- Chatbot Development
- Object Detection using ML/DL
Bestseller
