AI/ML Fundamentals with Python

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AI/ML Fundamentals with Python

This course provides a comprehensive, hands-on introduction to the field of data science with a strong focus on artificial intelligence (AI) and machine learning (ML). Students will learn to work with real-world datasets, understand key ML principles, and develop predictive models using Python and libraries such as NumPy, Pandas, Scikit-learn, and Matplotlib. The curriculum covers the full machine learning pipeline from data preprocessing and exploratory analysis to model training, evaluation, and optimisation. Emphasis is placed on applied learning, understanding algorithms, and fostering critical thinking, enabling students to tackle classification, regression, and clustering tasks. The course also discusses ethical issues in AI, including how to evaluate model fairness and bias.

Overview

Structured over 12 weeks, the course begins with Python programming fundamentals, then moves on to core topics in data handling, visualisation, and statistical understanding. Learners then progress to machine learning theory and implementation, including supervised algorithms (e.g., linear regression, decision trees, and support vector machines) and unsupervised techniques (e.g., k-means clustering and dimensionality reduction). Students will explore model validation strategies, performance metrics, issues related to overfitting and underfitting, and hyperparameter tuning. Each week features lab exercises, quizzes, and real dataset applications to reinforce learning. The course finishes with a capstone project, where students tackle a real-world problem, applying the full AI/ML workflow, from data preparation to model deployment, to produce a portfolio-worthy project.

Career Opportunities

Graduates of this course are well-prepared for a wide range of entry-level roles in the AI and data science field, including Machine Learning Engineer, Data Scientist (Junior), AI Research Assistant, and Python Developer with an AI/ML focus. These roles are in high demand across various industries, including healthcare, fintech, edtech, logistics, and retail, where data-driven decision-making and intelligent automation are becoming increasingly critical. The course also serves as a strong stepping stone for more advanced specialisations in deep learning, natural language processing, and MLOps.

Graduate Benefits

Graduates of the AI & Machine Learning with Python Course complete the program ready to apply practical machine learning skills to real-world projects, preparing them for entry-level roles in AI and data-driven industries or for advanced study in AI/ML specialisations.

Portfolio and Practical Experience:

A capstone project demonstrating the complete machine learning lifecycle, from data collection to model deployment.
Hands-on experience using Python tools and libraries, including Pandas, Scikit-learn, Matplotlib, and Jupyter Notebooks.
Applied projects showcasing model evaluation, performance optimisation, and data preprocessing.

Professional Skills Developed:

Practical proficiency in building, testing, and iterating ML models.
Understanding of ethical AI practices, including model bias and fairness.
Confidence in using data storytelling and visualisation for stakeholder communication.

Career Opportunities:

Graduates are prepared for junior roles in AI, machine learning, and data science across industries, including fintech, health-tech, and education. The course also builds a strong foundation for advancing into deep learning and applied AI specialisations or contributing to AI initiatives within existing professional roles.