This Artificial Intelligence Diploma provides a complete pathway from Python programming to advanced AI techniques. Participants learn data analysis, statistics, and algorithms, followed by machine learning, deep learning, computer vision, NLP, generative AI, and model deployment. The course includes practical exercises and capstone projects to ensure learners gain portfolio-ready AI solutions and are prepared for professional roles in AI and data science.
Course Overview
Course Syllabus
01 Module 1: Python Essentials
- Python Basics: Syntax
- Variables
- Data Types
- Strings
- Lists
- Sets
- Tuples
- Dicts
- Containers
- Conditions
- If
- Loops
- Functions
- Lambda
- File Handling; Python OOP: Classes & Objects
- Inheritance
- Encapsulation
- Polymorphism; Algorithms: Quick Sort
- Selection Sort
- Merge Sort
- Linear & Binary Search; Data Structures: Linked List
- Stack
- Queue
- Graphs
02 Module 2: Mathematics & Statistics for AI
- Probability: Basic theory
- Conditional & Unconditional
- Distributions (Normal
- Binomial
- Poisson); Calculus: Functions
- Limits
- Derivatives
- Chain Rule
- Partial Derivatives
- Gradient Descent
- Integration Basics; Linear Algebra: Scalars
- Vectors
- Matrices
- Matrix Operations
- Determinants & Inverse
- Eigenvalues & Eigenvectors; Data Pipeline & DataOps: Data types
- Descriptive Statistics
- Central Tendency
- Dispersion
- Sampling
- Hypothesis & A/B Testing
03 Module 3: Exploratory Data Analysis & Visualization
- NumPy & Pandas
- Data Cleaning & Preparation
- Handling Missing Data
- Categorical Data
- Outliers
- Feature Scaling
- Train/Test Split; Mini Project: EDA on Real-world Dataset; Data Visualization: Matplotlib
- Seaborn
- Indexing & Filtering
- Aggregations & Groupby
04 Module 4: Machine Learning
- Foundations: Supervised Learning (Linear & Logistic Regression
- KNN
- SVM
- Naive Bayes
- Decision Trees
- Random Forest)
- Evaluation Metrics (Accuracy
- Precision
- Recall
- F1)
- Cross-validation & Grid Search; ML Automation: Feature Engineering
- Scaling & Selection
- Handling Imbalanced & Categorical Data; Unsupervised Learning: K-Means
- Mean-Shift
- PCA; Model Deployment: Save/Load Models
- Simple API
- Model Selection
- Tools Comparison
05 Module 5: Deep Learning
- Foundations: Neural Networks (ANN)
- Activation Functions & Optimizers
- Forward & Backpropagation; Computer Vision: CNN
- Image Classification
- Pretrained Models
- OpenCV; NLP: Text Classification
- Transformers & Hugging Face Models; Time Series & Forecasting: RNN
- LSTM; Generative AI: GANs
- Image Generation
- DeepFakes; Deployment: Model Saving
- API for DL Models
06 Module 6: Capstone Projects
- Image Classifier with CNN
- Text Sentiment Analysis (NLP)
- Time Series Price Forecasting
- AI Model with Live API