Certificate in Artificial Intelligence and Machine Learning 

Week 1: Introduction to AI and ML

History and Evolution of AI: Overview of AI development from its inception to present day

Key Concepts and Terminology: Definitions of AI, ML, Deep Learning, and related terms

Applications of AI: Real-world applications in various fields like healthcare, finance, and autonomous systems

Week 2: Python for AI and ML

Python Basics and Control Structures

NumPy for Numerical Computations

Pandas for Data Manipulation

Data Visualisation with Matplotlib and Seaborn

Week 3: Data Preprocessing and Exploration

Data Cleaning: Handling missing values, outliers, and duplicates

Data Transformation: Scaling, normalisation, and encoding categorical variables

Exploratory Data Analysis (EDA): Visualisation techniques and summary statistics

Week 4: Supervised Learning - Regression

Linear Regression: Concepts, assumptions, and implementation

Evaluation Metrics: Mean Absolute Error, Mean Squared Error, and R-squared

Week 5: Supervised Learning - Classification

Logistic Regression: Binary classification, sigmoid function, and cost function

Evaluation Metrics: Accuracy, precision, recall, F1-score, and ROC-AUC

Week 6: Other Supervised Learning Algorithms

Decision Trees: Basics, advantages, and limitations

k-Nearest Neighbors (k-NN): Concept and implementation

Support Vector Machines (SVM): Basic understanding and applications