Programme Content
Introduction to Python
Control Flow (Conditionals, Loops)
Functions and Modules
Data Structures (Lists, Dictionaries, Sets, Tuples)
Object-Oriented Programming
Error Handling
Libraries Overview (NumPy, Pandas, Matplotlib)
Scientific Computing and Graphing
Real-world scripting use-cases (e.g., file parsing, web scraping)
Projects like "Python Web Scraper" or "Data Cleaner Script"
Learning Objectives:
Develop proficiency in writing Python programs to solve computational problems.
Understand core programming concepts such as data types, control flow, functions, and OOP principles.
Manipulate data structures such as lists, dictionaries, and sets efficiently.
Utilise key Python libraries (NumPy, Pandas, Matplotlib) for data manipulation and visualisation.
Debug and handle errors in Python programs effectively.
file parsing and web scraping for real-world data collection
Complete hands-on mini-projects like a Python Web Scraper and Data Cleaner Script
Topics Covered:
Linear Algebra:
Learning Objectives:
Fundamentals of Optimization
Least-square Approximation
Eigenvalues and Eigenvectors
Special Matrices
Singular Value Decomposition
Gradient Calculus
Statistics
Learning Objectives:
Introduction to Probability
Descriptive statistics
Foundations of Probability
Probability Distribution
Descriptive statistics
Foundations of Probability
Probability Distribution
Inferential statistics
Topics Covered:
Data Cleaning Techniques
Data Normalisation & Standardisation
Feature Selection
Dimensionality Reduction
Handling Categorical Variables
Feature Engineering
Balancing Datasets
Include SQL for dataset querying
Learning Objectives:
Clean and preprocess raw datasets by handling missing values and outliers
Normalize and standardize data for consistent model input
Apply feature selection and dimensionality reduction techniques
Encode categorical variables and engineer new features
Balance imbalanced datasets to improve model fairness
Use basic SQL queries to extract, filter, and join data from structured databases
Topics Covered:
Supervised Learning (Linear, Logistic Regression)
Classification Algorithms (Decision Trees, KNN, Naive Bayes, SVM)
Ensemble Methods (Random Forest, Gradient Boosting)
Clustering Algorithms (K-means, DBSCAN)
Model Evaluation Metrics
Cross-Validation
Hyperparameter Tuning
XGBoost, LightGBM, stacking/blending
Focused mini-project: "Credit Risk Classifier using ML"
Learning Objectives:
Build supervised models for regression and classification tasks
Implement popular ML algorithms like Decision Trees, SVM, KNN, Naive Bayes
Use ensemble methods including Random Forest and Gradient Boosting
Apply advanced models like XGBoost and LightGBM for high performance
Combine models using stacking and blending for better accuracy
Evaluate models using metrics like accuracy, F1-score, and ROC-AUC
Tune models using cross-validation and hyperparameter search
Apply concepts in a real-world mini-project: Credit Risk Classifier
Topics Covered:
Neural Networks Basics
Training Neural Networks
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Transfer Learning (e.g., ResNet, BERT, Transformers)
Hands-on with TensorFlow/Keras in Google Colab
Learning Objectives:
Understand the architecture and training of neural networks
Build and train CNNs for image tasks and RNNs for sequence data
Apply Transfer Learning using pre-trained models like ResNet (vision) and BERT (text)
Develop and train deep learning models using TensorFlow/Keras in Google Colab
Gain hands-on experience in building scalable, real-world DL solutions
Topics Covered:
Opencv:
Image processing,
face detection,
contour analysis,
object tracking,
real-time video apps.
NLP
Generative AI and LLMs
Prompt engineering
Using OpenAI APIs
Building Q&A bots with LLMs
Langchain Framework
Dspy
AI project
AI in Healthcare
Forecasting using Time Series
E-commerce Recommender System
Learning Objectives:
Apply OpenCV for image processing, face detection, object tracking, and real-time video analysis.
Use NLP and LLMs for prompt engineering, Q&A bots, and text generation with OpenAI APIs.
Build smart applications using LangChain, Dspy, and Generative AI techniques.
Develop AI solutions for healthcare, time series forecasting, and recommender systems.
Gain hands-on experience in building real-world AI/ML projects across multiple domains.
Topics Covered:
Introduction to Model Deployment
Containerisation with Docker
Deployment Frameworks (Flask, FastAPI)
Cloud Deployment (AWS, GCP, Azure)
Model Monitoring and Management
CI/CD for ML Models
Detecting and diagnosing faults
MLOps Principles
MLflow or W&B for model tracking
Real deployment demo (e.g., Streamlit app + backend API)
Learning Objectives:
Understand the end-to-end process of deploying machine learning models in production.
Containerise machine learning models using Docker for scalable deployment.
Deploy models as APIs using frameworks such as Flask and FastAPI.
Implement cloud-based deployment solutions using AWS, GCP, or Azure.
Monitor model performance in production and manage updates to deployed models.
Integrate CI/CD pipelines for continuous model deployment and scaling using MLflow or W&B.
Apply MLOps principles to manage the entire machine learning lifecycle from development to deployment.
Build real-world apps with Streamlit and backend APIs
